Doctoral Theses

Data Driven Discovery of Modular Biological Systems

Fri, 01 Sep 2023 00:00:00 GMT

Data Driven Discovery of Modular Biological Systems Altae-Tran, Han This dissertation presents a big data-driven approach for biological and biomedical discovery. The topics covered include the evolution and diversity of CRISPR systems, the identification and analysis of hypervariable protein systems, the identification of ancestral systems, and the development of RNA-guided systems for genome editing and therapeutic applications. Additionally, one of the chapters focuses on population-scale longitudinal mapping of COVID-19 symptoms, behavior, and testing, providing valuable insights for public health officials during the early stages of the pandemic. Through the development of novel methodologies and the utilization of big data-driven methods, this dissertation contributes to the expanding landscape of biomedical research. Chapter II delves into the origins of Cas9 and Cas12, examining the evolutionarily conserved non-coding RNA associated with IscB and the diverse RNA-guided nucleases encoded by IS200/IS605 elements. Through phylogenetic analysis and experimental characterization, we gain insight into the evolutionary history and diversity of IscB systems, and their potential biological functions. In Chapter III, we explore the diversity and function of Obligate Mobile Element Guided Activity (OMEGA) systems, focusing on TnpB and its relationship with Cas12 systems. We examine the taxonomy, genomic features, and evolution of these systems, as well as their mobility and potential exaptation. Chapter IV is dedicated to optimizing OMEGA RNA-guided systems for therapeutic applications. We screen natural IscB variants for efficient genome editing and engineer OrufIscB for enhanced activity, demonstrating its potential as a versatile genome interrogation tool. In Chapter V, we employ deep terascale clustering to discover functionally diverse CRISPR systems. Using a fast locality-sensitive hashing algorithm, we identify rare CRISPR systems, such as DinG-HNH, Type I Cascade components with HNH domains, and the Type VII CRISPR system, which is a precise RNA-guided RNA endonuclease complex containing a β-CASP nuclease. In Chapter VII, we investigate compact RNA editors, focusing on the discovery and characterization of Cas13bt. We repurpose Cas13bt for base editing and deliver these base editors to human cells using adeno-associated viruses (AAV), demonstrating their potential for therapeutic applications. Chapter VIII focuses on the identification and analysis of hypervariable protein systems with repeat signatures, seeking to find generalizations of concepts from other repeat systems such as CRISPR and TALENs. A computational pipeline is established to identify hypervariable repeat signatures in proteins, resulting in candidate systems that were characterized in additional detail. Multiple new mechanisms of modularity (two functions that are decoupled via an interchangeable domain or structure, such as repeats) were identified, pointing to a greater landscape of hypervariable protein systems than previously thought. These findings have implications for the understanding of protein architectures and may also provide valuable insights for the design of novel protein-based tools and therapeutics. Finally, Chapter IX presents one of the early large-scale studies conducted during the COVID pandemic, focusing on population-scale longitudinal mapping of COVID-19 symptoms, behavior, and testing. The study was conducted relatively early during the pandemic and collected data from a large user base of the How We Feel application. The data-driven approach employed various data analysis techniques, such as logistic regression, UMAP, and prediction models, to identify factors associated with testing propensity, symptoms associated with COVID, and behavior of patients after contracting COVID. The findings from this study could have provided valuable insights in the early stages of the pandemic, informing policymakers and public health officials such as the state of Connecticut to make data-driven decisions. Overall, this dissertation presents methods for and results from applying big data-driven methods to discovery from large biomedical databases. It specifically focuses on the exploration of diverse CRISPR systems, ancestors of CRISPR systems, hypervariable protein systems, and protein engineering for therapeutics and genome editing applications. From examining the evolutionary origins of Cas9 and Cas12 to investigating the diversity of OMEGA systems and optimizing them for therapeutic use, this work deepens our understanding of these complex biological systems. The discovery of rare CRISPR systems and compact RNA editors further broadens the landscape of genetic tools with potential therapeutic applications. Additionally, the identification and analysis of hypervariable protein systems reveal new mechanisms of modularity, with implications for protein architectures and the development of novel protein-based tools. Finally, the large-scale study of COVID-19 symptoms, behavior, and testing during the early stages of the pandemic demonstrates the power of data-driven approaches in informing public health decisions. Collectively, this research contributes significantly to our understanding of complex biological systems and highlights the potential for their application in advancing human health and biotechnology.

Carbon Nanotube Based Biosensors Using Corona Phase Molecular Recognition (CoPhMoRe): Development and Applications

Wed, 01 May 2024 00:00:00 GMT

Carbon Nanotube Based Biosensors Using Corona Phase Molecular Recognition (CoPhMoRe): Development and Applications Jin, Xiaojia Molecular recognition sites that specifically bind a target molecule are essential for clinical research, disease diagnosis, and therapeutic development. To this end, a promising technique developed by the Strano laboratory at MIT is Corona Phase Molecular Recognition (CoPhMoRe), which uses amphiphilic polymers or macromolecules adsorbed onto a nanoparticle surface to generate a synthetic recognition site. The underlying nanoparticle, which can also function as the sensor transducer, pins the polymer to a specific 3D confirmation using non-covalent interactions, resulting in a binding pocket analogous to the antigen binding domain of a natural antibody. While CoPhMoRe has proven considerable versatile in recognizing small organic molecules such as vitamins, neurotransmitters, pharmaceutical drugs and steroid hormones, the recognition of large molecules such has viral proteins has been less explored. Macromolecular analytes introduce a much wider set of potential interactions, requiring refined analysis and new insights into mechanisms. This thesis focuses on (i) constructing CoPhMoRe sites for protein analytes, (ii) exploring new methods and mechanistic understanding to inform CoPhMoRe recognitions and also (iii) translating CoPhMoRe phases to interfaces that can be incorporated into biosensors for specific applications. Towards Aim (i), this thesis has developed CoPhMoRe sites for protein based disease biomarkers, including interleukin-6, nucleocapsid and spike proteins of SARS-CoV-2, enabling rapid and label-free near-IR fluorescence detection of target analytes with dose dependent responses in complex environments. Towards Aim (ii), this thesis investigates new methods and analyses for CoPhMoRe characterization, such as the expansion of the Molecular Probe Absorption (MPA) technique. This technique measures the accessible surface area of a CoPhMoRe based sensor by using a fluorescent molecule as a probe that quenches upon interacting with the corona phase. Further advances involve instrumentation and mathematical models to analyze the chiroptical properteis of corona phases, facilitating the CoPhMoRe handedness determination at the single molecule level with circularly polarized excitation sources. Towards Aim (iii), this thesis explores form factor advancements to broaden the utility of CoPhMoRe sensors. It includes profiling cellular immune heterogeneity by integrating the optical nanosensor arrays into microfluidics to interrogate chemical species efflux from individual cells in real-time using Nanosensor Chemical Cytometry (NCC). Furthermore, nanosensors are encapsulated into stable hydrogels for integration into acoustic tags to track hormone levels in marine animals. Together, the successful development of CoPhMoRe nanosensors opens new pathways for synthetic molecular recognition that enables the detection of biological macromolecules, and holds great promise for life science applications.

Design, Synthesis and Applications of Versatile Porous Poly(arylene ether)s

Thu, 01 Feb 2024 00:00:00 GMT

Design, Synthesis and Applications of Versatile Porous Poly(arylene ether)s Wu, Yifan This thesis describes the synthesis of various porous poly(arylene ether)s for the applications in heterogeneous catalysis and gas separation. In Chapter I, we offer an overview of the structure and properties of porous poly(arylene ether)s, and introduce the key challenges in heterogeneous catalysis and gas separation. In Chapter II, we present the development of a solution-processable microporous organic polymer catalyst that displays high catalytic performance and size-selectivity in the heterogeneous SuzukiMiyaura coupling reaction. The catalyst can be used to create catalytic impellers that simplifies its use and recovery, thereby conforming to green chemistry principles. In Chapter III, we demonstrate a tunable synthetic platform for the advent of eight representative microporous poly(arylene ether)s with tertiary-amine functional groups. We compare the competition enhancements in sorption affinity for H₂S and CO₂ to those of primary-amine functional membranes and provide explanation for the reason why the slight enhancements do not fully translate to enhanced separation performance. In Chapter IV, we explore the potential of free volume manipulation in enhancing acid gas separation for microporous polymer membranes. By incorporating labile functional groups onto a microporous poly(arylene ether) and employing thermal treatment with oxygen, we improve the combined acid gas selectivity and increase membrane resistance to plasticization. In Chapter V, we synthesize a series of ionic poly(arylene ethers), and evaluate their potential applications in propylene/propane separation, proton exchange and heterogenous catalysis. We study the separation performance of carboxylate-functionalized polymer, and the ion exchange capacity and efficiency as solid acid catalyst for sulfonated polymers to demonstrate their promise.

Explaining Allied Military Postures: Extended Deterrence, the U.S. Nuclear Umbrella, and the Search for Security

Thu, 01 May 2025 00:00:00 GMT

Explaining Allied Military Postures: Extended Deterrence, the U.S. Nuclear Umbrella, and the Search for Security Kwon, Jung Jae How do non-nuclear allies of the United States bolster deterrence even as they rely on the United States and its nuclear umbrella for security? How do they exercise agency in operationalizing the deterrence extended to them by the United States against their nuclear-armed adversaries? Although the existing literature offers valuable insights into the measures the United States employs to signal commitment and enhance the credibility of its security guarantees, it has paid far less attention to the role of allies themselves. This dissertation addresses that gap by introducing allied integration theory, a new framework for understanding allies’ incentives and choices. The theory explains and predicts variation in allies’ peacetime military postures. I first develop a typology of four ideal types, each reflecting allies’ different choices regarding capabilities, doctrine, tolerance for escalation risk, and integration with U.S. forces, including U.S. nuclear forces. I then argue that allies’ decisions are shaped primarily by their threat environment. Specifically, I highlight two key factors: the threat of strikes (conventional, WMD, or nuclear) and the threat of invasion from their adversaries. I then test the theory through two pairs of comparative case studies. The first pair examines Japan and South Korea in the post-Cold War period; the second compares West Germany and Norway, two frontline states of NATO, during the later Cold War (1970s-1980s). This research design allows for examining both within-case and cross-case variation while holding constant many potential confounders within each pair. Drawing on extensive fieldwork, elite interviews, foreign-language sources, U.S. archival materials, and secondary sources, I analyze how each ally made key decisions on their postures and evaluate the performance of allied integration theory. The findings contribute to the broader debates on alliance politics in the nuclear era and the interplay between conventional and nuclear domains. The research also offers practical insights for addressing growing security challenges faced by U.S.-led alliances amid intensifying geopolitical competition with multiple nuclear-armed adversaries.

Discovery and characterization of diverse microbial RNA-guided systems

Thu, 01 Jun 2023 00:00:00 GMT

Discovery and characterization of diverse microbial RNA-guided systems Kannan, Soumya Precise modification of nucleic acids is a powerful technique to enable understanding of the relationship between variation in genetic information and biological phenotypes and to treat genetic diseases. Over the past decade, the microbial adaptive immune system CRISPR-Cas has revolutionized genome editing, largely due to ease of target reprogramming. Cas nucleases can be retargeted by changing a short sequence in the associated non-coding RNA, which acts to guide the enzyme or complex to a complementary nucleic acid target. Exploration of the natural diversity of CRISPR-Cas systems has uncovered numerous variants with widely differing protein and domain architectures that exhibit distinct substrate specificities and activities, tying RNA-guided nucleic acid recognition to diverse enzymatic functions. This diversity has fueled the development of CRISPR-Cas systems for additional applications beyond genome editing, such as transcriptome editing, rapid diagnostics and molecular recording tools, and has aided in optimization of existing technologies via identification of systems that naturally exhibit desirable properties. In this thesis, we explore the evolution and diversity of RNA-guided microbial systems and characterize and engineer them for use in human cells. First, we investigate the origins of Cas9 and Cas12, the most widely used Cas proteins for genome editing. We find that they evolved from compact transposon-encoded nucleases, which we termed OMEGA, that already employed an RNA-guiding mechanism. Next, we develop OMEGA systems as genome editing tools that, due to their small size, are more compatible with therapeutic delivery vectors. We further explore microbial genomes and metagenomes to discover Cas13b-t, a similarly compact RNA-targeting system that we develop as a deliverable RNA editing platform. Finally, we extended the theme of mining sequence databases to comprehensively catalog proteins and domains associated with CRISPR-Cas systems. Through this analysis, we identify and characterize several novel CRISPR-Cas types and subtypes with potential for development as biotechnologies.

Understanding Ion Conduction in Polymer-Ceramic Composite Electrolytes for Lithium Ion Batteries

Wed, 01 May 2024 00:00:00 GMT

Understanding Ion Conduction in Polymer-Ceramic Composite Electrolytes for Lithium Ion Batteries Sand, Sara Catherine In the transition to safer, more energy-dense solid state batteries, polymer-ceramic composite electrolytes may offer a potential route to achieve simultaneously high lithium ion conductivity and enhanced mechanical stability. Despite numerous studies on the polymer-ceramic composite electrolytes, disagreements persist on whether the polymer or the ceramic are the primary conduction pathway and how each phase is impacted by the proximity of the other. This lack of understanding limits the design of effective composite solid electrolytes. Therefore, the goal of this thesis is to establish the role that the polymer plays in the ionic conduction and what changes in this phase to allow for enhanced conduction. I present a collection of well-controlled experiments using model systems and minimizing the parameter space, as well as utilizing advanced characterization techniques. In doing so, we probe the underlying mechanisms of how lithium ion conductivity is affected in polymer-ceramic composites. In particular, the use of positron annihilation spectroscopy has been instrumental in revealing the primary mechanisms that alter lithium ion conductivity in the polymer matrix. First, we present a comprehensive and in-depth review of the literature in Chapter 2. This chapter statistically analyzes the trends in ionic conductivities achieved in the field and present the various arguments for ion conduction pathways and interfacial effects made throughout the last three decades. As a result of this field-wide analysis, we hypothesize that the major component whose ionic conductivity is affected in the composite is the polymer. Thus the following experiments and results focus on how the polymer is altered structurally and/or chemically. Second, in Chapter 3 we present a study utilizing thin polymer films deposited on substrates of varying acidity, as a model system. Comparison of the polymer film conductivities, chemistry and structures allow us to deduce that the modification of polymer structure near a ceramic interface is a major factor, while the ceramic-polymer interface chemistry has a negligible effect on the conductivity. In particular, the crystallinity and free volume of the polymer change appreciably to result in a higher ionic conductivity in thin films as compared to bulk materials. In Chapter 4, we assess well-controlled composite electrolytes with inert, non-lithium conducting ceramic fillers, and active, lithium conducting ceramic fillers in the polymer, in particular by consistently controlling the particle size and filler volume fraction. We find that the increase in the free volume of the polymer phase plays a crucial role in the conductivity of both types of composites. The addition of active fillers only minorly improves conductivity over inert fillers, solely due to the conduction path introduced by the active ceramic particles. In Chapter 5, we assess composites containing inert nanometer-site particles to those with inert micrometer-size particles. We find that the ionic conductivity improves with nanoparticles but not with the micrometer-size particles, and again we are able to correlate this improvement with the free volume present in the polymer matrix. Lastly, in Chapter 6, we will summarize several experiments that, though less pertinent to the main aims of this thesis, are valuable to the field in understanding the techniques for characterizing these materials. Through this thesis, we provide a strong argument for the importance of the polymer’s structure in the composite electrolytes, and particularly an increase in the free volume present in the polymer phase. Such analysis of free volume has been limited in the field thus far. Thus, this thesis fills an important knowledge gap that has been limiting the understanding and control of how ceramic fillers increase lithium ion conductivity in polymer-ceramic composite electrolytes.

Magnetization Dynamics in Multi-sublattice Magnetic Materials

Wed, 01 May 2024 00:00:00 GMT

Magnetization Dynamics in Multi-sublattice Magnetic Materials Lee, Byung Hun Magnon spintronics explores the solid-state devices that utilize electric control of spin currents carried by magnons, the quanta of spin waves. The magnon, the dynamic eigen-excitation of magnetically ordered materials, offers promising opportunities for next-generation information processing and communication systems. It can be engineered through various parameters, such as the orientation and magnitude of applied magnetic fields, choice of magnetic material, and sample geometry, enabling versatile engineering of wave-based computing technologies. Beyond the conventional ferromagnetic magnons, the final goal of this thesis is to obtain a better understanding of antiferromagnetic dynamics, which gains huge attention due to its unique characteristics including ultrafast magnetization dynamics, low damping, and negligible dipole fields. To achieve this, the study focuses on the magnetization dynamics within magnetic materials ranging from ferromagnet, ferrimagnet, and antiferromagnet, using Brillouin Light Scattering (BLS) spectroscopy. Starting from the understanding of ferromagnetic magnetization dynamics, the interface-driven effects on magnon physics are studied in ferromagnetic insulator/metal bilayers. The study reveals previously unidentified interface-driven changes in magnetization dynamics that can be exploited to locally control and modulate magnonic properties in thin-film heterostructures. As the ferromagnetic film transitions towards antiferromagnetic behavior, notable changes in BLS spectra are observed, including significant linewidth broadening. These experimental results highlight the crucial role of antiferromagnetic exchange interactions in multi-sublattice magnetic materials' magnon dynamics. Motivated by the observed reduction in magneto-optical (MO) signal for ferrimagnetic thin films, we conducted a comprehensive study on canted antiferromagnet, focusing on thin hematite (𝛼 — Fe₂O₃) film. Through both experimental and theoretical analyses of the MO properties of hematite films, we demonstrate that the canted net magnetic moment induced by the Dzyaloshinskii-Moriya interaction (DMI) generates a significant linear MO effect, while the quadratic MO signal arises from the Néel vector. Attributing to the observation, we further demonstrate that the secondary MO effect from dynamical net magnetic moment induces the significant BLS signal, in both theoretical and experimental manners. Furthermore, we demonstrate the strong correlation between magnetic domain structure and local magnon spectra, which is identified for the first time in thin 𝛼 — Fe₂O₃ film. Our study extends to the direct observation of a non-thermalized magnon state with spin current injection, indicative of a characteristic excitation mechanism with linearly polarized modes in an easy-plane antiferromagnet. The magnon thermalization and relaxation processes are further elucidated between the two linearly polarized modes. The result shows the significant contribution of high k magnons to long-distance spin transport in hematite using non-local electric measurements. Finally, we characterize the magnetic anisotropy within hematite films, distinguishing each contribution to its origin. These findings offer insights into magnetization dynamics and the manipulation of AFM spin textures via optical methods.

Controlling Protein and Cell Adhesion Through Interfacial Engineering for More Efficient Biomanufacturing

Thu, 01 Jun 2023 00:00:00 GMT

Controlling Protein and Cell Adhesion Through Interfacial Engineering for More Efficient Biomanufacturing McCue, Caroline Interfaces between biological and synthetic materials present both challenges and opportunities within biomanufacturing. Protein and cell adhesion to surfaces can be controlled to improve steps in a typical biologic manufacturing process from cell culture to protein separation and purification. Within downstream processes, in protein purification, chromatography columns are used to separate target proteins from the output of a bioreactor. This work explores how crystallization could be used as an alternative purification process, by utilizing functionalized nanoparticles to nucleate protein crystals faster and at lower concentrations. We demonstrate significant improvements in nucleation induction time and nucleation rates using bioconjugate-functionalized nanoparticles. Within upstream processes, in adherent cell culture, cells are typically detached from surfaces using trypsin, an enzyme that can damage sensitive cells, and result in genetic mutations. This work studies how passive surface textures and active coatings can impact cell growth, morphology and adhesion. We show that micropost surfaces can significantly reduce cell-surface adhesion, and how electrically active surfaces can be used to detach cells on demand without the use of trypsin. These interfacial platforms present opportunities to reduce the costs of traditional biomanufacturing processes, reduce damage to cells, and enable new high throughput platforms.

Structural Investigation of Allosteric Regulation in Class III Ribonucleotide Reductases

Wed, 01 May 2024 00:00:00 GMT

Structural Investigation of Allosteric Regulation in Class III Ribonucleotide Reductases Andree, Gisele A. Ribonucleotide reductases (RNRs) are essential enzymes that use radical-based chemistry to catalyze the reduction of ribonucleotides to deoxyribonucleotides. Each RNR class uses a different cofactor to generate a catalytically essential thiyl radical in the active site. Anaerobic (class III) RNRs employ an oxygen-sensitive glycyl radical cofactor installed within the adjacent glycyl radical domain. To maintain intracellular nucleotide pool balance, RNRs are allosterically regulated. For the prototypical class Ia RNR from Escherichia coli, this regulation involves the Nterminus of the catalytic subunit in a region known as the ‘cone domain’. ATP or dATP binding to the cone domain results in an association between it and the radical-generating subunit that either allows or prevents, respectively, radical transfer. Most class III RNRs have a cone domain and are allosterically regulated, but the mechanism of how this regulation proceeds is not well understood. Allosteric activity regulation for such a class III RNR, the class III RNR from Streptococcus thermophilus (StNrdD) is the focus of this thesis. We have developed a universal liquid chromatography tandem mass spectrometry based activity assay, which was adapted for use in class III RNR, and used the assay to show that ATP is an allosteric activity enhancer and dATP is an allosteric activity inhibitor of StNrdD. We used a combination of cryogenic electron microscopy (cryo-EM) and X-ray crystallography to show that ATP and dATP bind to the StNrdD cone domain and that the cone domains adopt exceptionally different conformations in the presence of either allosteric effector. Mutagenesis assays and hydrogendeuterium exchange mass spectrometry data show that the flexible region between the cone domain and the core is important for catalysis. Changes between ATP- and dATP-binding in the cone domains underlie the observed conformational and allosteric changes. This work answers some long-standing questions surrounding class III allosteric regulation and lays the groundwork to continue improving our molecular-level understanding of this complex enzyme system.

Real-time Anticipation and Entrainment in Human-Robot Interaction

Wed, 01 May 2024 00:00:00 GMT

Real-time Anticipation and Entrainment in Human-Robot Interaction Fourie, Christopher Kurt In this work, reactive control methodologies, alongside real-time methodologies for dense human motion prediction, are utilized to facilitate real-time anticipation and entrainment in human robot interaction. The technical contributions of this thesis include: extensions to dynamical systems-based modulation approaches that enable real-time circumnavigation of non-convex obstacles (NOMAD), a trajectory clustering approach based on a relaxation of dynamic time warping (TRACER), a real-time human modelling and prediction approach (HABITS), and the integration of these technologies into an anticipation and entrainment controller that enables real-time adaptive synchronization between a human and a robot. NOMAD introduces several on-manifold strategies that enable real-time navigation in the presence of non-convex obstacles, alongside a methodology for the eff icient representation of dense environments that can represent up to 240k points while maintaining a 1ms loop. TRACER is a probabilistic trajectory clustering algorithm that uses the expectation-maximization algorithm and a relaxation of dynamic time warping (Soft-DTW), with demonstrable improvement over non-probabilistic techniques such as kMedoids or DBSCAN. HABITS is an event-driven probabilistic filtering and incremental profiling framework that provides robust segmentation, prediction, and alignment estimation in real-time (25Hz) for emergent interactions in structured settings. The combination of these technologies is then demonstrated to enable both effective real-time anticipation (de-conflicting a workspace), as well as to support entrainment (long-term human-robot synchronization) in human-robot interaction.

Approaches to quantitatively analyze protein complex assembly and regulation

Wed, 01 May 2024 00:00:00 GMT

Approaches to quantitatively analyze protein complex assembly and regulation Kinman, Laurel F. Biological macromolecular complexes occupy high-dimensional conformational landscapes corresponding to their assembly and regulation. For many protein complexes, these conformational landscapes encode diverse functional states of the complex, and indeed, there is a growing appreciation of the critical significance of protein dynamics in driving protein function. As such, there is a need for approaches to experimentally and quantitatively resolve these conformational landscapes to better understand: 1) the diverse structural states these complexes sample; 2) how these states and their dynamics are linked to biological function; and 3) how these landscapes are modulated by binding partners or environmental signals, or over the course of complex assembly. State-of-the-art structural approaches including heterogeneous cryogenic electron microscopy (cryo-EM) offer one potentially powerful mechanism for resolving these landscapes, and we present here approaches to resolve large numbers (100s-1,000s) of volumes from a single dataset. Moreover, I explore methods to analyze the resulting large volume ensembles using supervised and unsupervised approaches, and demonstrate the power of these approaches by applying them to identify a proofreading role for the universally-conserved methyltransferase KsgA in biogenesis of the bacterial small ribosomal subunit. However, many of the most highly dynamic protein complexes – involved in critical cellular processes like environmental sensing and signal transduction – remain inaccessible to structural techniques like heterogeneous cryo-EM. I suggest that combining structural approaches with high-throughput techniques, including proteomic and library-based approaches, has substantial power to shed light on the function and regulation of such complexes. As an example, I couple a high-throughput reporter assay with deep mutational scanning, finding highly distributed phosphorylation regulated assembly of the Atg1 complex in Saccharomyces cerevisiae in response to diverse environmental cues. Taken together, my work generates a toolbox of complementary approaches for quantitatively characterizing the assembly and regulation of protein complexes, and I anticipate substantial utility in applying these approaches to broadly characterize the functional and regulatory landscapes of protein complexes.

Full Body, Continuous, Wearable Ultrasound

Wed, 01 May 2024 00:00:00 GMT

Full Body, Continuous, Wearable Ultrasound Wang, Chonghe In the rapidly evolving field of healthcare technology, the development of wearable ultrasound systems emerges as a groundbreaking innovation with the potential to transform patient monitoring and disease detection methodologies. This thesis, titled "Full Body, Continuous, Wearable Ultrasound," by Chonghe Wang, explores the design, implementation, and impact of two pioneering wearable ultrasound technologies: the Bioadhesive Ultrasound (BAUS) and the Adjustable Wearable Ultrasound (AWUS) systems. Aimed at enabling continuous, non-invasive monitoring of internal organ health, these systems represent a significant leap forward in medical imaging and healthcare management. At the core of the BAUS system is an innovative bioadhesive hydrogel-elastomer hybrid couplant, which facilitates the attachment of ultrasound probes directly to the skin, ensuring stable, long-term imaging. Conversely, the AWUS system employs phase-transition hydrogel couplants for dynamic adjustment of probe orientation and position, allowing for optimized imaging across multiple organs. Together, these systems offer unparalleled capabilities for full-body monitoring, with potential applications ranging from early disease detection to enhanced patient care and a deeper understanding of human physiology. Through rigorous experimental validation, this research assesses the imaging quality, physiological monitoring accuracy, durability, and comfort of the BAUS and AWUS systems. Employing advanced image processing and machine learning techniques, the study analyzes data collected from continuous imaging sessions, highlighting the systems' ability to capture dynamic physiological events and detect pathological changes. This thesis underscores the transformative potential of wearable ultrasound technology in healthcare and medical research. By shifting the paradigm from episodic to continuous monitoring, it opens new avenues for proactive health management, promising to improve patient outcomes, reduce healthcare costs, and advance our understanding of the human body.

Geometry, packing, and synchronization in three-dimensional (3D) multicellular development and diseases

Wed, 01 May 2024 00:00:00 GMT

Geometry, packing, and synchronization in three-dimensional (3D) multicellular development and diseases Tang, Wenhui Three-dimensional (3D) multicellular systems, along with their developmental and pathological processes, present unique features compare to 2D flat systems. Both the spatial organization and temporal cell dynamics can be influenced by fundamental difference in cell-microenvironment interactions, cell mechanics, as well as cell physical properties. However, the effort to understanding the fundamental physics and mechanics is limited due to the constraint in 3D techniques. Moreover, the evolving multicellular properties during biological processes are unclear. In this thesis, we systematically study the geometry, packing, and synchronization in three-dimensional multicellular development and diseases. First, we give a perspective on the emerging evidence of 3D tissue geometry in guiding morphogenesis and abnormal growth during diseases. We review the effort in fabricating various tissue-mimicking structures to study collective cell behaviors, and emphasize how tissue geometry influences physical, mechanical, and biological properties. At the end, we propose future directions, challenges, and potential applications in geometry-induced stem cell therapeutics. Second, we report a surprising result that cells can feel the substrate curvature that they live on. We find that epithelial cells tend to form hexagonal packs to minimize the free energy; cells in these hexagonal packs are more solid-like compared to the cells out of packs. As a result, when the substrate curvature increases, the size of these hexagonal cell packs decreases to release bending energy. Therefore, on more curved surface, we observe a more fluid-like cell monolayer with more active cell dynamics and less collectiveness. Such behavior is observed not only on fabricated geometries, but also on a spontaneously growing human lung alveolosphere system in 3D derived from human induced pluripotent stem cells. Third, as building blocks of life, cells actively coordinate their positions to form various structures and perform functions. A central question is how do cells in three-dimensional organisms accurately arrange their positions and morphology on curved structures during development? We observe an emergence of topological order, specifically a topological gas-to-liquid transition, during the growth of human lung alveolospheres, regulated by the increasing nucleus-to-cell size ratio. Our finding reveals the critical role of cell nuclear size in regulating cell packing during tissue development, and suggests the importance of topological phase changes in establishing tissue stability. Last, from a temporal perspective, we report an experimental observation of large-scale synchronized oscillations generated by sustained contraction and expansion, revealing un- expected phase dynamics in epithelia. We then apply this temporal analysis to studying proliferating epithelia undergoing jamming transition and cancer invasion across various degrees of malignancy. Remarkably, this method provides an original perspective to assess the stage of tissue development, estimate cancer malignancy level, as well as distinguish healthy tissues from the diseased ones.

Multidimensional MOF Mixed Matrix Membranes for Efficient Gas Separation

Fri, 01 Sep 2023 00:00:00 GMT

Multidimensional MOF Mixed Matrix Membranes for Efficient Gas Separation Lee, Hyunhee Membrane separations are crucial in the chemical industry, with polymeric materials traditionally used due to their cost and mechanical benefits. However, they face challenges in permeability–selectivity trade-off, and in stability. Metal–organic frameworks (MOFs) offer potential solutions with their customizable properties but are difficult to manufacture. Mixed-matrix membranes (MMMs), which incorporate MOFs into polymers, mitigate some issues, yet high MOF loading can lead to aggregation and voids. This thesis investigates the promising potential of MMMs for efficient and improved gas separation, leveraging unique morphologies and understanding the dynamics of MOF–polymer interactions. First, the novel branch-shaped ZIF-8 (BZ) was developed and incorporated into polymer matrix, which successfully established a percolated network at loadings as low as 20 wt%, showing permeability boost. Also, it showed suppressed polymer chain dynamics and a smaller diffusion cut-off than traditional ZIF-8, which resulted in an enhanced membrane stability and superior performance in H₂-based separations. BZ was studied further by investigating temperature-dependent properties of MMMs. BZ and control ZIF-8 (CZ) MMMs exhibited unique gas transport behaviour in relation to temperature shifts, with BZ MMM demonstrating more significant temperature dependence for H₂-based separations. As temperature decreases, the H₂/CH₄ permselectivity of BZ MMMs drastically increases, with minor changes in H₂ permeability. Conversely, at higher temperatures, separation performance aligns with that of CZ MMM, showing continuous yet broad control over the gas performance. To understand the origin of this selectivity difference, facet-specific gas transport in polymer nanocomposites was studied with the hypothesis of BZ consist of facet 100, which characterize less thermally stable polymorph, cubes. A key finding is the interaction between 100 facet and polyimides, which enhances hydrogen-based and ethylene/ethane separation, particularly at subambient temperatures, which is consistent with the trend observed for BZ MMMs. In conclusion, this thesis addresses the enhancement of MMMs through innovative morphological approaches, where percolated network enhances permeability and 100 facet termination may restrict the MOF–polymer interphase confinement, leading to high selectivity for small gas pairs, which is very difficult to achieve at the same time. The temperature effects and facet-termination effects on gas transport in MMMs can also offer substantial contributions to the development and optimization of mixed matrix membranes for efficient gas separations.

Chemically Circumventing the Oxidative Instability of Boronic Acids for Biological Applications

Mon, 01 Sep 2025 00:00:00 GMT

Chemically Circumventing the Oxidative Instability of Boronic Acids for Biological Applications FitzGerald, Forrest Grant Boronic acids are more than just chemical reagents used in synthetic organic chemistry. They are life-saving chemo- and radiotherapeutics, optical imaging agents, chemical sensors of biological stress, hydrogels, nanomaterials, and so much more. While the literature is rich with applications of boronic acids, there is much room for improvement. From the environment we live in, to the biochemical transformations that drive cellular respiration, oxidation is a common thread. Boronic acids are highly susceptible to oxidation by reactive oxygen species that are continuously generated in these environments, and though boronic acids have found wide use, their utility is limited by their short lifetimes due to this form a degradation. We sought to find chemical solutions for this instability. Benzoxaborolone, a boralactone, was previously developed in the Raines lab as an oxidatively stable arylboronic acid. In this current work, we demonstrate that this oxidative stability is enhanced by electron-withdrawing functional groups and reduced by electron-donating functional groups appended to the aryl ring. Applying principles of physical organic chemistry, we reasoned that this electronic impact is predictable depending on the identity of the functional group, with valuable insights for use in medicinal chemistry and chemical biology. Next, we demonstrated that a benzoxaborolone–fluorophore conjugate is an equally effective glycan imaging reagent compared to the commonly used phenylboronic acid-bearing reagent, yet is more stable and thus versatile. We designed this reagent to be a general-use minimalistic glycan-binding reagent with high diffusivity and modularity. We then used this reagent to explore the diverse organ-specific glycome in mice, leveraging recently reported techniques in expansion microscopy to acquire fluorescence images in nanoscale resolution. Finally, we took a new approach to circumventing the chemical instability of boronic acids in biological settings. Bortezomib is a highly potent alkylboronic acid chemotherapeutic that has been a mainstay in the clinic for the treatment of multiple myeloma for nearly twenty-five years. Unfortunately, it is highly susceptible to oxidation and thus prone to inactivation during synthesis, storage, and administration. Substituting in an aromatic, oxidatively stable benzoxaborolone is not an effective strategy for bortezomib, as it would completely change the chemical structure and abrogate activity. Instead, we leveraged a popular boronic acid protecting group, Nmethyliminodiacetic acid (MIDA), used primarily in organic chemistry, to create a bench-stable, oxidation-resistant bortezomib that retains its cytotoxic potency toward cancer cells. Furthermore, we uncovered for the first time that the MIDA protecting group may be susceptible to esterase-mediated hydrolysis, paving a path towards potential prodrug applications in the future.

The true, the good and the justified: Essays on epistemic normativity and value

Mon, 01 Sep 2025 00:00:00 GMT

The true, the good and the justified: Essays on epistemic normativity and value Gélineau, Félix-Antoine This dissertation about the role that value—in particular, the value of truth—plays in the explanation of epistemic norms, the norms that should govern our belief-formation and belief-revision processes. It is a pervasive practice for us to assess one another’s beliefs, decrying some and commending others: we find something amiss in a belief formed by wishful thinking, and deem proper a belief arrived at via meticulous consideration of one’s evidence. It is natural to think that there is a close connection between what epistemic norms sanction, truth, and the fact that truth matters. In what sense is truth valuable? and what does that entail for how we should conceive of epistemic norms? These questions drive my dissertation. In chapter 1, “The true, the good and the justified”, I argue that the teleological conception of epistemic justification, the view that for a belief to be justified is for it to be formed in a way that is conducive to truth, is safe from the main objections it faces. These import assumptions about value that belong to ethical consequentialism; the epistemic teleologist need not and should not accept them. “Epistemic value” is best understood as a ‘placeholder’, not as a term denoting value in any substantive sense. The upshot is that endorsing a teleological explanation of epistemic norms does not commit one to the idea that true beliefs ought to be promoted in the way the good ought to be promoted according to ethical consequentialists. In chapter 2, “Why be antisocial (about epistemic normativity)”, I examine whether epistemic normativity is grounded in the usefulness of truth for communities and argue that it is not. If what we ought to believe were to be explained in terms of the usefulness of truth for communities, we should expect our epistemic norms to be wildly different from what they are: they could condone trade-offs between true and false beliefs across a community when doing so would be useful for it; they would often fail to prescribe believing over other doxastic attitudes when the epistemic aims of the community would be equally well-served by either; finally, there is no way to answer in a satisfying manner the question of what isolated individuals should believe. In chapter 3, “What’s truth got to do with it?”, I develop an account of epistemic normativity that does justice to the idea that truth matters but avoids the shortcomings of other value-based accounts. I argue that, given any plausible account of the value of truth, the value of truth can explain some, but not all epistemic norms. The account of epistemic normativity that I present is pluralistic: I distinguish between substantive norms, which are explained by the value of truth, and basal norms, which stem from the good functioning of mechanisms of belief-formation and belief-revision, independently of the value of truth. This account is shown to be superior to other accounts discussed in the course of the dissertation.

Ions in Electrically Conductive and Insulating Metal-Organic-Frameworks: Transport and Energy Storage

Mon, 01 Sep 2025 00:00:00 GMT

Ions in Electrically Conductive and Insulating Metal-Organic-Frameworks: Transport and Energy Storage Su, Alice Yue Ion transport in metal-organic frameworks (MOFs) is attracting increasing attention since ions can be easily incorporated into porous MOF structures as guest species, promising a variety of possible applications. While electronically insulating but ionically conductive MOFs show great potential as solid electrolytes, the precise structure and tunability of MOFs also enables rational combination of electronic and ionic conductivity to create intrinsic mixed conductors. The combination of both conduction pathways is highly relevant for energy storage applications where ions interact with and insert into electronically conductive electrode active materials. This thesis first explores ion transport in an anionic MOF electrolyte, conducting mono- and divalent cations. Transport studies give insight into how MOF structure and ion-related variables impact ionic conductivity and activation energy. Studies on this material paint a picture that furthers our understanding of fundamental ion transport mechanism in MOF electrolytes. Incorporating electronic transport as an additional layer of complexity, new mixed proton-electron conductive two-dimensional MOFs based on aromatic azaborine ligands are synthesized. Their dual conductive nature is confirmed by separating the ionic and electronic contributions to the overall transport. Lastly, a family of triazatruxene-based two-dimensional electronically conductive MOFs are explored as pseudocapacitors. Here, the diffusion of ions inside the pore network as well as their interaction with MOF active sites depending on the interlayer spacing are investigated for their impact on capacitance.

Plurals of politeness and the morphosyntax of number

Mon, 01 Sep 2025 00:00:00 GMT

Plurals of politeness and the morphosyntax of number Sinha, Yash Many languages use plural pronouns to address (and refer to) single individuals with politeness or honorification. In some languages, these plurals of politeness (PoPs) show mixed agreement, triggering plural agreement on some agreement targets and singular on others. In this dissertation, I use PoPs to investigate the internal structure of DPs, focusing on how number features are represented within them. Starting first with pronominal DPs, I adopt the view that these contain two phrases: (i) a noun phrase (NP) headed by a silent noun (Postal 1966 and subsequent work), and (ii) an index phrase (idxP), realized by the pronoun, which occupies the specifier of the DP and introduces a referential index (Jenks 2022; see also Choi 2014, Giusti 2015). My novel claim is that both the NP and the idxP bear their own number features. In most cases, this is not detectable because the number features of the NP and idxP match. I argue, however, that this is not the case for PoPs, which allows us to see that these two number features are in fact distinct. Specifically, I show that the agreement patterns of PoPs are best accounted for by treating them as consisting of a plural idxP with a singular NP (i.e., a plural pronoun with a silent singular noun). This analysis not only derives the mixed agreement of PoPs seen in some languages, but also explains certain cross-linguistic restrictions on the distribution of singular agreement with them. I also extend this analysis to account for nominal PoPs, a type of nominal DP found in a subset of languages with pronominal PoPs. These nominal PoPs have the same semantics and the same agreement patterns as their pronominal counterparts, but crucially, the morphology on the noun in nominal PoPs is singular and not plural. I argue that these similarities and differences can be explained by positing that idxPs are present in nominal DPs as well, but are not realized overtly. This analysis of nominal PoPs is shown to also be able to account for the DP-internal concord patterns seen with them.

Oddness under Discussion

Mon, 01 Sep 2025 00:00:00 GMT

Oddness under Discussion Hénot-Mortier, Adèle At a broad level, this dissertation's main claim is that many cases of pragmatic oddness do not stem from assertions alone, but rather from their interaction with the questions they implicitly evoke. Felicitous assertions, must evoke felicitous questions. To operationalize this claim, a model of compositionally derived implicit question is devised, along with conditions of their well-formedness, drawing from familiar concepts in pragmatics, such as Redundancy and Relevance. This model assigns a central role to the degree of specificity, or granularity, conveyed by assertions. At a more narrow level, this dissertation argues that disjunctions and conditionals fundamentally differ in terms of the questions they evoke, and that this difference has direct consequences on the oddness/felicity profiles of sentences involving these operators. Disjunctions are shown to be prone to Redundancy issues, while conditionals are shown to be prone to Relevance issues. In other words, disjunctions and conditionals typically display distinct flavors of oddness. This is supported by three main classes of sentences. First, sentences that can be seen as equivalent, but which combine conditionals and disjunctions in distinct ways, display varying felicity profiles. Second, "pure" disjunctions and conditionals that can be seen as isomorphic, if not equivalent, display varying felicity profiles. Third, some differences between these disjunctions and conditionals remain when additional pragmatic phenomena, in particular scalar implicatures, are at play, and such differences shift in a way predicted by our approach. This dissertation therefore justifies the appeal to a more elaborate model of (implicit) questions, which, when fed to the pragmatic module, is characterized by a better empirical accuracy on challenging data, than previous model solely based on assertive content.

Illumination-Robust Terrain Relative Navigation for Planetary Descent

Mon, 01 Sep 2025 00:00:00 GMT

Illumination-Robust Terrain Relative Navigation for Planetary Descent Mitchell, Adriana Macieira Missions to explore planetary bodies (e.g., Moon, Mars, Titan, Europa, Enceladus) through surface exploration have been planned for the next decades. These missions depend on autonomous optical navigation capabilities for safe entry, descent, and landing near key scientific areas of interest, potentially near hazardous terrain. Terrain Relative Navigation (TRN) enables autonomous precision landing by matching descent images to an a priori orbital map. However, performance degrades significantly when large differences in solar illumination exist between the map and descent imagery, particularly under high azimuth angle changes, due to terrain-induced shading inversions that break assumptions of photometric consistency in both frequency and intensity-based correlation methods. This thesis presents a set of methods to robustify TRN performance under large directional illumination changes, addressing three core challenges: understanding the failure of existing TRN methods, improving feature matching robustness to varying Sun angles, and generating reliable navigation maps from incomplete orbital imagery. First, the physical cause of correlation failure is characterized through a frequency-domain analysis of shading effects. Building on the azimuth impact matrix, which models the directional dependence of shading-induced phase reversals, this work applies it as a frequencydomain correction to frequency correlation to improve correlation peak accuracy across large solar azimuth differences. Second, a novel frequency-domain photometric correction method, Solar Orientation Layering via Frequency Image eXtraction (SOLFIX), is introduced to produce corrected map products aligned with expected descent conditions by layering spatial frequency content aligned with known solar angles from multi-resolution, multi-illumination orbital imagery. Third, a predictive illumination-aware map is developed to identify terrain regions likely to yield reliable correlations. This map integrates solar azimuth geometry, terrain aspect from low-resolution digital elevation models, and spatial frequency information to pre-filter unreliable areas of the map prior to localization. The proposed methods are validated on Mars orbital datasets, simulated terrain renderings, and NASA JPL’s field test imagery, each with Sun angle variations. Despite wide Sun angle differences, the proposed methods recover accurate localization where baseline TRN methods fail due to the bias from shading inversions caused by large solar azimuth differences. These contributions enable reliable optical navigation in scenarios where acquiring new orbital maps is infeasible and support future planetary missions operating under severe illumination constraints.

Verification Planning for Efficient Uncertainty Reduction of Space Science Systems

Mon, 01 Sep 2025 00:00:00 GMT

Verification Planning for Efficient Uncertainty Reduction of Space Science Systems Stenzel, June Verification for a complex engineering system is essential for mitigating risks associated with quantitative uncertainty and ensuring confidence in the system’s performance. Verification planning is the problem of deciding how to allocate resources in this process, and verification for some systems may allocate more time and money to certain verification activities than is necessary to achieve a desired level of certainty. The need for efficient and effective verification is especially great for space systems and space science instruments, which are often subject to active cost and schedule constraints that make verification planning a challenge. This research proposes the Uncertainty Quantification Verification Planning Methodology (UQVM) for designing optimal-under-uncertainty verification plans in a systematic, quantitative, model-based way. Uncertainty quantification methods are used to model instrument performance, determine sources and magnitudes of parametric uncertainty, and perform sensitivity analysis. Stochastic models of potential verification activities are also developed with subject matter expertise, and are subjected to uncertainty quantification. A novel approach to optimal Bayesian experimental design (OBED) is developed to determine sets of verification activities that minimize effort and maximize certainty of system performance. A comprehensive systems engineering approach brings together these techniques of uncertainty quantification and experimental design methods, so that systems engineers can optimize design of AI&T and V&V campaigns with respect to programmatic cost and confidence of system performance, and can refine those plans as new verification data is obtained. UQVM is demonstrated for space science system case studies. A study of verification planning for CCD performance shows that optimally uncertainty-reducing plans within a cost cap can be identified that reduce the variance of predicted performance by 67%, and that iterative data-informed verification planning can reduce the variance of predicted performance by 96%. A retrospective analysis of the sensitivity verification for the Large Lenslet Array Magellan Spectrograph (LLAMAS) shows that optimized plans can reduce testbed time by 94% without a loss in uncertainty reduction, and that plans can be identified that perform better than historical plans with a confidence of greater than 90%. An early-phase analysis of precision control verification for the James Webb Space Telescope (JWST) shows that tests can be ranked in order of benefit-at-cost.

Molecular Mechanisms Defining and Driving Receptivity in Conversion of Fibroblasts to Motor Neurons

Mon, 01 Sep 2025 00:00:00 GMT

Molecular Mechanisms Defining and Driving Receptivity in Conversion of Fibroblasts to Motor Neurons Beitz, Adam M. Layers of regulation stabilize cellular identity and prevent aberrant cell-fate transitions. Cell fate conversion processes, such as the conversion of fibroblasts into motor neurons, attempt to induce a cell of one type to become a cell of another type by activating genes and gene networks associated with the desired final cell type. Cell fate conversion processes have the potential to revolutionize drug discovery and drive the development of novel cell-based therapies, but their translational potential is limited by poor conversion rates. Forced overexpression of lineage-specifying transcription factors is rarely sufficient to induce a complete change in cellular identity. We find that overexpression of known oncogenes can enhance a cell’s receptivity to conversion by increasing proliferation rates and increase conversion yields 100x, even when converting to a non-proliferative state. In this thesis, we use the model system of mouse embryonic fibroblast to motor neuron conversion to determine how oncogenic mutants of HRAS and the tumor suppressor protein p53 induce a receptive cell state and enhance conversion. We find that cells that proliferate at high rates early in conversion attain the motor neuron identity at higher rates than cells that do not proliferate as much. We isolate cells that attain high rates of proliferation and define the subcellular properties of these conversion-receptive cells. Receptive cells display more compact chromatin as proliferation destabilizes chromatin structures generally, including those that reinforce the starting cell type identity. An increase in trimethylation of H3K27, a histone mark known to induce chromatin compaction and reduce transcriptional output, supports this this compaction and is associated with a global decrease in transcription rates. Finally, we make a counter-intuitive finding that the interaction between mutant and native p53 enhances conversion beyond its role in promoting proliferation that is dependent on the presence of native p53. The p53 mutant induces accumulation of native p53 in a subpopulation of cells. We developed a tool to track p53 levels in mouse embryonic fibroblasts in order to track p53 accumulation during conversion. By developing tools to isolate cells with different conversion-receptivity during oncogene-enhanced conversion, we can characterize the subcellular features that promote conversion. We expect future cell fate conversions may be engineered to systematically guide cells through a receptive state without oncogenes.

Structural and biochemical investigations of collagen-I trimerization

Mon, 01 Sep 2025 00:00:00 GMT

Structural and biochemical investigations of collagen-I trimerization Srinivasa, Sorin Asha The fibrillar collagens display a unique assembly process, with assembly initiating at the C-terminal propeptide (C-Pro) domain, a small globular domain that encodes vast amounts of information for chain selection, stoichiometry, and molecular recognition. The C-Pro domain is responsible for ensuring that strands that assemble together are of the same type of collagen, and, in the case of certain collagens, that the stoichiometry between strands is correct. In addition to being involved in assembly, the C-Pro domain has also been shown to play a significant role in collagen proteostasis. Quality control factors in the cell are able to specifically recognize misfolded C-Pro domains in the context of disease-causing mutations, demonstrating a vital role for C-Pro folding in this process. In chapter 2, we report progress towards the answer to a long-elusive question: why do certain collagens favor heterotrimeric assemblies even when homotrimeric assemblies are possible? We show progress towards a high-resolution structure of the collagen-I C-Pro 2:1 heterotrimer, and investigate the role of Ca2+ coordination in dictating assembly behavior and chain association. In chapter 3, we characterize the assembly and proteostasis defects associated with a set of C-Pro mutations that have been observed in patients with osteogenesis imperfecta. Our data reveal that, while some variants are effectively recognized by the cell’s quality control mechanisms and retained intracellularly, others are not and secrete at similar levels to the wild-type. We also demonstrate that even severely assembly-deficient C-Pro domains can escape quality control, likely resulting in severe or lethal disease. Collectively, the work presented here significantly advances our understanding of how collagen-I assembly occurs in healthy biological systems, and how it can be disrupted in disease, setting the stage for a variety of future investigations into this challenging system.

Structured Bayesian Inference for Spatio-Temporal Systems with Applications in Remote Sensing

Mon, 01 Sep 2025 00:00:00 GMT

Structured Bayesian Inference for Spatio-Temporal Systems with Applications in Remote Sensing Leung, Kelvin Man Yiu Satellite-based remote sensing observing systems are a key source of information for understanding Earth system dynamics. Bayesian inference provides a principled framework for retrieving physical parameters from satellite observations while quantifying uncertainty. However, the high dimensionality and spatio-temporal complexity of remote sensing problems pose major computational challenges for traditional inference methods. This thesis develops scalable algorithms for Bayesian inference for remote sensing systems by leveraging low-rank structure and sparse conditional dependence structure. The resulting methods enable accurate and efficient posterior characterization at scales relevant for modern satellite missions. The first theme of this thesis is identifying low-rank structure in problems where the scientific goal is to estimate a small number of quantities of interest (QoIs) that are a function of the unknown parameters. Using a gradient-based dimension reduction framework, we construct informative subspaces of the observation space that are tailored to specific QoIs. This framework is integrated with transport maps to enable simulation-based inference directly for the QoIs, without the need to recover the full posterior of the high-dimensional parameters. We demonstrate this approach on imaging spectroscopy data from NASA’s upcoming Surface Biology and Geology (SBG) mission and show that it achieves inference accuracy comparable to Markov chain Monte Carlo (MCMC) while requiring orders of magnitude less computational time. In addition, we examine the role of preconditioning in dimension reduction and demonstrate that the optimal choice of preconditioner depends on the nonlinearity of the forward model. Next, we explore how conditional independence structure can be used to improve the scalability of inference algorithms relevant to remote sensing systems. We first consider a single-pixel setting and exploit within-state conditional independence to build sparse transport maps for hyperspectral retrievals. These sparse maps reduce computation over standard nonGaussian inference methods while preserving accuracy. Extending beyond individual pixels, we develop an information filter that leverages spatio-temporal conditional independencies in satellite observing systems. By incorporating sparse inverse covariance structure into the filtering equations, we achieve significant improvements in both scalability and inference accuracy on data relevant to NASA’s OCO-2, EMIT, and SBG missions. Building on this structure, this thesis also explores extensions of large-scale spatio-temporal inference to the continuous non-Gaussian setting using measure transport. Drawing inspiration from belief propagation algorithms for Gaussian graphical models, we construct decomposed transport maps tailored to spatio-temporal graphical structures. These methods enable scalable inference while capturing non-Gaussian features of the posterior. We demonstrate their application to spatio-temporal systems, providing a viable framework for high-fidelity uncertainty quantification.

Towards Intelligent Psycho-Social Support to Augment Behavioral Health Management in Isolated Environments

Mon, 01 Sep 2025 00:00:00 GMT

Towards Intelligent Psycho-Social Support to Augment Behavioral Health Management in Isolated Environments Nguyen, Golda Long-duration spaceflight requires astronauts to live in isolated, confined, and extreme environments while physically and socially separated from support systems for up to years at a time. This thesis explores how intelligent, autonomous tools may augment behavioral health management when real-time, Earth-based support is inaccessible in deep space. Such intelligent psycho-social support must be able to: 1) characterize individual risks, 2) assess health state accurately, and 3) deliver appropriate interventions or countermeasures. To augment these system functions, techniques from statistical modeling, natural language processing, and conversational AI are investigated across three case studies of isolation: wide-scale isolation during the COVID-19 pandemic, isolation in a space analog habitat, and social isolation in the general public. To explore risk characterization, statistical models were constructed on trait-based, behavioral, and social environment factors in relation to mood and anxiety state in isolation. Models of civilian risk under isolation were developed to inform automated risk characterization for future private astronauts. To explore augmenting psychological assessment, a feasibility analysis of natural language processing (NLP) for automated affect classification was conducted. Transformer-based NLP techniques were tested against lexicon-based and other machine learning (ML)-based techniques on affect classification of personal journal text from analog astronauts. Transformer-based models demonstrated improved detection of negative affect classes, but overall, lexicon-based models were still comparable to ML-based models. Finally, to explore augmenting countermeasures, a study of engagement and disclosure in AI-augmented reflection was conducted. In this study, participants shared more content volume and spent more time in a hybrid reflection condition (reflecting alone first before chatting with a prompted chatbot) than in separated conditions (journaling alone or reflecting only with the bot). Higher loneliness was associated with lower comfort and engagement, but behavioral benefits were similar across lonely and non-lonely users, suggesting further tailoring is needed to support isolated individuals. Through these case studies, this work presents a point of departure towards a vision of intelligent psychosocial support systems that are not meant to replace human connection, but to augment behavioral healthcare for individuals in isolated settings on Earth and in space.

Discovery and characterization of plateau potentials in cortical neurons of awake mice

Mon, 01 Sep 2025 00:00:00 GMT

Discovery and characterization of plateau potentials in cortical neurons of awake mice Mojica Soto-Albors, Raúl E. Plateau potentials are large calcium-dependent regenerative depolarizations that support burst firing and facilitate behavioral time scale synaptic plasticity (BTSP) in the hippocampus. Despite substantial progress in our understanding of these events in CA1, it remains unclear whether they occur in the neocortex and, if so, how do they manifest. To address this, we performed in vivo whole cell patch clamp recordings from layer (L) 2/3, L4, and L5 pyramidal neurons (PNs) in mouse primary visual cortex (V1) to produce the first systematic characterization of cortical plateau potentials. We established functional correlates of plateau potentials and evaluated their role in plasticity induction. First, we described the high prevalence of prolonged somatic depolarizations accompanied by high-frequency spikes (~105 Hz) in 43% of L5 PNs. Cortical plateau potentials closely resembled those previously described in the hippocampus, averaging ~27 mV in amplitude and ~60 ms in duration, with pronounced intraburst spike amplitude attenuation. Recordings obtained from L2/3 and L4 neurons revealed that cells in these layers do not generate plateaus, indicating a unique generation site in L5. Within L5, neurons exhibiting plateaus had lower input resistance than those that did not, suggesting plateaus may be specific to thick-tufted extratelencephalic (ET) PNs. We further described how the incidence of plateaus in L5 PNs was surprisingly not increased by visual stimulation. Intriguingly, their prevalence more than tripled during periods of behavioral arousal. Furthermore, plateau initiation was more likely during the rising phase of the extracellular theta rhythm (5-10 Hz) in V1, suggesting that cortical plateaus are modulated by internal state and network rhythms rather than visual stimuli alone. Finally, we investigated the role of cortical plateaus in plasticity by pairing a non-preferred stimulus with artificially evoked events. In contrast to the BTSP observed in CA1, spiking output remained consistent before and after pairing in V1. However, subthreshold responses indicated some synaptic depotentiation for the preferred stimulus, indicating that plateaus might be sufficient to alter synaptic weights, though in a different way than that demonstrated in hippocampus. Collectively, this work sheds light on an underexplored cortical output mechanism unique to L5 pyramidal neurons, positioning the plateau potential as a cell-type-specific phenomenon that may reshape sensory representations in the neocortex, with implications for cortical computation and biologically inspired learning rules in neural networks.

Comprehensive laboratory studies of organic oxidation across a range of photochemical ages and peroxy radical conditions

Mon, 01 Sep 2025 00:00:00 GMT

Comprehensive laboratory studies of organic oxidation across a range of photochemical ages and peroxy radical conditions Franco Deloya, Lesly Joanne Reactive organic carbon (ROC), defined as all volatile organic compounds (VOCs) and particulate organic carbon except methane, is the largest source of reactive emissions in the atmosphere and therefore plays a central role in atmospheric chemistry. After emission, ROC undergoes rapid chemical transformations driven by sunlight and atmospheric oxidants, forming peroxy radicals (RO₂) whose fate shapes the resulting oxidation products. This sequence of reactions, known as the ROC lifecycle, continues until ROC is removed via wet or dry deposition or fully oxidized to carbon dioxide (CO₂). Throughout its evolution, ROC contributes to the formation of ozone, particulate matter and CO₂, making its study necessary for understanding air quality and climate. While many aspects of this cycle have been explored through modeling, laboratory experiments, and field campaigns, our understanding of the ROC lifecycle remains incomplete due to the large number of compounds and its chemical complexity as it evolves in the atmosphere. This thesis investigates key aspects of the ROC lifecycle, focusing on its multigenerational chemical evolution over extended atmospheric aging and on how RO₂ chemistry shapes gas-phase species distributions. The first part investigates the multigenerational oxidation of ROC through a series of laboratory experiments designed to simulate atmospheric aging over extended timescales. These experiments make comprehensive measurements of both gas- and particle-phase carbon to gain a holistic understanding of ROC evolution in the atmosphere. While models have been used to simulate the full evolution of ROC, few experiments have constrained this process. These experiments provide the first direct constraints on the lifetime of ROC against heterogeneous oxidation, the formation of small and long-lived oxygenated VOCs, and the evolution of carbon properties (e.g. carbon number, carbon oxidation state) over multiweek atmospheric equivalent aging. We then compare these experimental results to long-term aging simulations using several chemical mechanisms that are implemented in models. Most chemical mechanisms achieve near-total carbon closure, however, they differ in species composition, ROC mineralization rates, and OH reactivity, especially at later stages of oxidation. These discrepancies highlight the gaps that remain in our understanding of the long-term chemical evolution of ROC. Finally, we conduct experiments that simulate a variety of RO₂ environments representative of atmospheric conditions. Past chamber experiments have primarily focused on RO₂ chemistry under extreme conditions that involve short bimolecular lifetimes and “low” or “high” NOₓ conditions. Our findings suggest that traditional metrics used to assess species distributions are insufficient to explain the full range of changes with shifts in the RO₂ environment. A more comprehensive accounting of all relevant RO₂ fates is necessary to explain gas-phase species composition, which in turn influences HOₓ and NOₓ cycling, ozone production and ultimately our understanding of secondary organic aerosol formation. Together, these results provide new constraints on the atmospheric evolution of ROC across a wide range of oxidative and photochemical conditions, improving our understanding of ROC and its role in air quality and climate.

Structural characterization and antibiotic development for the Neisseria gonorrhoeae class Ia ribonucleotide reductase

Mon, 01 Sep 2025 00:00:00 GMT

Structural characterization and antibiotic development for the Neisseria gonorrhoeae class Ia ribonucleotide reductase Dorfeuille, Andrew Leonard Jacques Ribonucleotide reductases (RNRs) are essential enzymes that catalyze the reduction of ribonucleotides to deoxyribonucleotides, a critical step in DNA biosynthesis and repair. Class Ia RNRs, found in eukaryotes and many aerobic bacteria including Escherichia coli (E. coli) and Neisseria gonorrhoeae (N. gonorrhoeae), are regulated by complex allosteric mechanisms that control enzymatic activity and substrate specificity. These enzymes function as α₂β₂ complexes, with the α₂ subunit housing regulatory sites and the β₂ subunit providing a catalytic radical. Proper regulation of RNR is vital for maintaining balanced dNTP pools and genomic integrity, making RNRs attractive targets for therapeutic intervention in both infectious disease and cancer. This thesis examines the structural basis of specificity regulation in N. gonorrhoeae class Ia RNR using cryogenic electron microscopy (cryo-EM). Near atomic-resolution structures of the enzyme bound to four canonical substrate/specificity-effector pairs (CDP/dATP, UDP/dATP, GDP/TTP, and ADP/dGTP) reveal that effectors induce conformational changes in loop 2 of the α₂ subunit, which alter hydrogen bonding contacts in the active site, leading to preferential substrate binding. This mechanism is also conserved in the E. coli class Ia RNR, a close homolog. Cryo-EM maps also show weak, non-specific binding of a second nucleotide in the cone domain, likely reflecting artifacts of high nucleotide concentrations used in the cryo-EM experiments rather than physiological relevance. Building on these findings, Chapter III investigates the potential interaction of the cyclic dinucleotide, c-diAMP, with the cone domains of E. coli and N. gonorrhoeae RNRs. We find that c-diAMP binds both enzymes with low micromolar affinity but does not alter the activity of either RNR to a large extent over the no-effector control. Although similar, the behavior of E. coli and N. gonorrhoeae RNRs are not identical, highlighting the potential of targeting the cone domain for species-specific RNR inhibition. This approach could enable the development of novel antibiotics, particularly needed for combatting antibiotic-resistant N. gonorrhoeae.

Generalizable Reinforcement Learning for Network Control

Mon, 01 Sep 2025 00:00:00 GMT

Generalizable Reinforcement Learning for Network Control Wigmore, Jerrod This thesis confronts the critical generalization gap of Deep Reinforcement Learning (DRL) that hinders its effective application to queueing network control, where policies often fail to perform robustly in unseen topologies and traffic conditions upon deployment. We develop and analyze a suite of novel techniques that systematically embed structural domain knowledge and safety considerations to create more robust, efficient, and generalist learning agents. To improve generalization for a large class of queueing network control problems, we first introduce the Switch-Type Network (STN), a policy architecture that embeds the "switch-type" property common in classical control. This architectural prior improves sample efficiency and enables superior zero-shot generalization across varying network parameters. To address generalization across multi-hop networks, we then propose the Multi-Axis Graph Neural Network (MA-GNN), which augments the traditional inter-node message passing operations of a GNN with a novel intranode aggregation mechanism to capture complex, permutation-invariant dependencies between different traffic classes. This allows the MAGNN to learn and output high-level control coefficients that are effective for unseen network topologies. Recognizing the limitations of offline training, we shift to online adaptation and introduce an intervention-assisted DRL framework that guarantees stability in environments with unbounded state-spaces. By partitioning the state space and ceding control to a provably stable policy in high-congestion regions, this framework prevents catastrophic learning failures; its stability is proven via Lyapunov analysis, and foundational policy gradient theorems are extended to support the interventional setting. As a complementary case study in structured exploration, we also develop a Bayesian Hierarchical Bandit model and a Hierarchical Thompson Sampling (HTS) algorithm for the multi-band radio channel selection problem, which leverages environmental correlations to guide exploration and significantly reduce regret. Collectively, these contributions provide a comprehensive framework for creating DRL agents that are more robust and practical, demonstrating that embedding knowledge of policy structure, network topology, safety, and environmental correlations is a crucial step towards deploying autonomous agents in complex, real-world systems.

Local Competition, Number and Definitness

Mon, 01 Sep 2025 00:00:00 GMT

Local Competition, Number and Definitness Doron, Omri In this dissertation, I explore the consequences of local competition inside the DP. I argue that various phenomena, including multiplicity inferences, homogeneity and definiteness, are best explained as locally-triggered scalar implicatures (SIs), when coupled with a view of SIs as presupposed (Bassi et al., 2021). I begin with the puzzle of the multiplicity inferences that arise from the use of plural indefinites, and show that deriving them as presupposed SIs naturally explains their felicity conditions and projection from embedded environments. I then argue that this competition-based system can account for the typology of number marking, and in fact providing us with a parsimonious theory of the crosslinguistic variation. A key result of this argument is that any language which allows for number marking on nouns has both the singular and the plural feature in its inventory. Finally, I suggest that local competition can also derive the inferences stemming from definite descriptions, including uniqueness, maximality and homogeneity.

Engineering DNA-based electrochemical diagnostics for translational applications

Mon, 01 Sep 2025 00:00:00 GMT

Engineering DNA-based electrochemical diagnostics for translational applications Zhou, Xingcheng Low-resource settings are disproportionally burdened by infectious diseases due to limited access to early and accurate disease detection. Current gold standard methods, while effective, have high turnaround times and require costly infrastructure that is often impractical in these environments. Electrochemical biosensors are a promising alternative to due to their sensitivity, selectivity, low-cost, portability, and rapid response time. Among various biorecognition elements, DNA is particularly advantageous due to its versatility, comparable stability, and low production cost. However, there is a significant gap between laboratory proof-of-concept biosensors and commercially viable biosensors. The main challenges associated with commercializing DNA-based sensors include ineffective surface chemistries, limited target range, poor long-term stability, and high-cost, non-scalable manufacturing processes. In my thesis, I resolve these specific challenges by engineering DNA-based electrochemical biosensors systems to support their translation into commercially-viable products. First, I address ineffective surface chemistries for modifying screen-printed carbon electrodes, which are widely used for bioelectrochemical systems due to their low production cost and scalable manufacturing. However, effective modification with biomolecules remains a challenge as the main methods are either non-specific, require harsh reagents, or form weak monolayers. In this project, we develop a new facile, bio-orthogonal, and biocompatible surface chemistry for modifying screen-printed carbon electrodes. This approach enables the modification of electrode surfaces with DNA, whole cells, and proteins while maintaining bioactivity, supporting applications in both biosensing and clean energy. Next, I expand the range of targets for nucleic acid electrochemical detection. Electrochemical hybridization assays are sensitive and specific but are limited to very short nucleic acids. To resolves this, we develop a restriction enzyme-assisted electrochemical hybridization assay for improved nucleic acid detection. By incorporating target-specific restriction enzymes, I detect long nucleic acids, with performance dependent on the location of the cut site relative to the electrode surface. Thus, I establish guidelines for assay design to serve as a generalizable platform for robust electrochemical detection of long nucleic acids. Subsequently, I solve the challenge of long-term storage of sensors. Commercialization of DNA-based electrochemical biosensors is challenged by the stability and shelf-life of the DNA monolayer. There is no technology that allows storage of these sensors long term at room temperature at dry conditions. Here, we report a novel method to preserve DNA-based biosensor through a protective coating of polyvinyl alcohol. We show that the coating significantly improves the shelf life at both room temperature and elevated temperatures. We further demonstrate that the DNA is viable for downstream sensing. Our finding allows facilitates the commercialization of DNA-based biosensors as viable products. Finally, I design and fabricate a multiplexed electrochemical diagnostic device for respiratory viruses. While electrochemical biosensors are a major research area of diagnostics that can be utilized in low resource settings, effectively integrating assays into a seamless, inexpensive fluidic device is difficult. In this project, we first develop an assay to detect three types of respiratory viruses with sensitivity comparable to PCR. We then integrate the workflow into a shelf-stable diagnostic utilizing low-cost materials and a scalable manufacturing process. Our device offers a practical solution for device integration and future disease control for vulnerable populations. Overall, the methods developed in this work have the potential to support the transition of DNA-based electrochemical biosensor from academic research to commercially-viable products, paving the way for more FDA-approved diagnostics for early disease detection and advancing health equity in vulnerable populations.

Essays in Labor and Public Economics

Mon, 01 Sep 2025 00:00:00 GMT

Essays in Labor and Public Economics Martin Richmond, Jane Alexandra This dissertation examines how institutional policies and labor market frictions affect human capital formation, employment outcomes, and economic mobility across generations. The three essays explore distinct but interconnected aspects of how families and workers navigate constraints in education, childcare, and credentialing systems. The first essay is joint with Jonathan Rothbaum and investigates intergenerational spillovers from parental disability insurance receipt, using linked Census/IRS/SSA administrative data covering over 400,000 families. Using US administrative data, we link dependent children of SSDI recipients to their tax filings at age 25. We document a key descriptive fact, that the income of the child at age 25 is increasing in the age at which the parent receives their first SSDI transfer. We show that the probability that a child themselves receives an SSDI transfer as an adult is decreasing on the same margin; however, the pattern persists in the sample of children who do not receive such transfers. We build a model to show that these facts are consistent with a "lost-years" mechanism by which children whose parents become resource constrained earlier in life lose more years of costly intergenerational human capital investment. The second essay examines firms' decisions to remove bachelor's degree requirements from job postings and the subsequent hiring outcomes. By tracking within-role requirements over time, I identify instances where employers have explicitly removed bachelor's degree prerequisites from job advertisements. Linking these changes to aggregated resume data, I analyze whether individuals subsequently hired have different educational credentials, alternative qualifications, or experiential backgrounds. After the removal of degree requirements, the share of individuals hired with a degree falls by 1-3 p.p. Concurrently, the share of people hired that report a non-degree credential is roughly unchanged, and the average years of labor market experience possessed by a candidate increases by 0.5 years. I detail a simple model explaining why firms may be motivated to remove degree requirements and substitute them with other screening mechanisms. To address potential endogeneity concerns, I employ an instrumental variable approach, exploiting staggered state-level policy shifts in the removal of degree requirements from public sector employment, which act as an information treatment to firms. I also show that these firm-level and state-level policy changes are broadly uncorrelated with local labor market conditions. The third essay is joint with Maya Bidanda and analyzes how childcare access affects parents' choice between wage employment and self-employment. In this paper, we exploit variation in subsidized Pre-Kindergarten (Pre-K) to understand how access to childcare impacts mothers’ propensity for self-employment and formal work. We find that mothers of children who are too young for school are more likely to be self-employed and less likely to be in formal work than mothers of older children. We do not find similar trends for fathers. In addition, access to low-cost childcare increases the likelihood for formal work and decreases the likelihood of self-employment. This is evidence that mothers are pushed into self-employment due to frictions or barriers in formal work when childcare is not available. In the last part of the paper we discuss the impact of these patterns on mothers' lifetime income.

Machine Learning for Chemical Reactivity Prediction: Paradigms, Challenges, and Applications

Mon, 01 Sep 2025 00:00:00 GMT

Machine Learning for Chemical Reactivity Prediction: Paradigms, Challenges, and Applications Raghavan, Priyanka The discovery of new therapeutic agents in the pharmaceutical industry is a complex, iterative process, often encapsulated by the Design-Make-Test-Analyze (DMTA) cycle, in which chemists ideate, synthesize, and assay compound targets of interest. A significant bottleneck in this cycle is the "Make" phase, where the synthesis of novel compounds can be time-consuming, resource-intensive, and fraught with unpredictable outcomes. Accurate prediction of chemical reactivity, particularly reaction yields and selectivities, is therefore paramount to accelerating drug discovery by enabling more efficient synthesis planning, reducing material waste, and guiding the design of more synthetically accessible molecules. As such, this dissertation explores the application of machine learning (ML) to address critical challenges in chemical reactivity prediction, with a particular focus on low-data regimes and the integration of predictive models into practical drug discovery workflows. This thesis begins by addressing the pervasive challenge of predicting reaction yields from sparse, literature-derived data. It details the assembly of a large dataset of substrate scopes and evaluates single-task and multi-task ML approaches, highlighting the limitations imposed by data scarcity and noise in real-world chemical literature. Recognizing these challenges, this thesis then provides recommendations for designing experimental datasets that are more conducive to robust machine learning, specifically offering considerations for curating data with the downstream modeling goal in mind. Building on these insights, this thesis then turns toward specific applications of machine learning in medicinal chemistry, first presenting a direct, impactful implementation of ML to enhance synthetic accessibility in drug design by predicting Suzuki cross-coupling yields from a large, historical pharmaceutical library dataset. ML models are shown to often outperform expert intuition and be successfully integrated into existing workflows for library design and rescue, significantly increasing synthesis efficiency. Finally, this thesis expands from chemical reactions to enzymatic reactions, detailing a computational and ML-based workflow for transaminase enzyme selection, to streamline the enantioselective synthesis of valuable chiral amine building blocks used in medicinal chemistry. Collectively, this thesis contributes to the growing field of machine learning in chemistry by addressing fundamental challenges in reactivity prediction, particularly in low-data and real-world industrial settings. It provides novel modeling paradigms for existing data and insights into the limitations of current approaches, offers a conceptual framework for improved data generation, and demonstrates the tangible benefits of integrating ML models into the DMTA pipeline. Throughout, the critical interplay between data quality, molecular representation, and model architecture and evaluation is emphasized, paving the way for more reliable and impactful predictive tools that can accelerate the pace of chemical discovery.

Property prediction with machine learning and ab initio methods for iridium photoactive complexes and metal-organic frameworks

Mon, 01 Sep 2025 00:00:00 GMT

Property prediction with machine learning and ab initio methods for iridium photoactive complexes and metal-organic frameworks Terrones, Gianmarco Guin Data-driven prediction of a chemical’s properties prior to synthesis or use can accelerate chemical discovery by increasing the probability of candidate suitability for the given application. In this thesis, data-driven models and complementary first-principles calculations have been used to study three types of chemistries: iridium photoactive complexes, metal-organic frameworks, and reactions for azetidine synthesis. Iridium photoactive complexes are commonly used in OLED lighting, photocatalysis, and bioimaging due to their unique phosphorescent properties and triplet excited state population. Metal-organic frameworks are studied for heterogeneous catalysis and gas separations and storage due to their tunable metal environments and porous structures. Azetidine-containing molecules have potential for use as pharmaceuticals due to their high stability and good pharmacokinetics. However, despite their advantageous properties, challenges remain in designing these chemistries and informing this design with computation. The excited state properties of iridium complexes are challenging and costly to predict through first-principles methods. Similarly, stability issues often affect metal-organic frameworks, yet these cannot be efficiently modeled by physics-based routines. Lastly, synthetic approaches toward azetidine synthesis are limited, and computational study of novel synthetic approaches to identify desirable reactant characteristics would benefit the future scope of azetidine products. The models developed in this thesis have proven to be complementary tools to first-principles approaches, and have major benefits in their speed of application and ability to train directly on experimental data for properties that challenge methods like DFT. The models are applied to screen chemical space for promising candidates through consideration of hypothetical, not-yet-synthesized iridium complexes and metal-organic frameworks generated through component combination of existing structures. The machine learning models are also used to derive structure-property relationships through feature importance analysis, for example identifying qualities of iridium photoactive complexes that impart longer or shorter excited state lifetime. In addition to model generation, work in this thesis has covered code development of a software package for molecule structure generation, modification, and fingerprinting, and also development of intuitive web interfaces for easy use of data-driven models. It is expected that the tools developed in this thesis will both allow for a greater understanding of iridium complexes, metal-organic frameworks, and azetidine synthesis, and enable low-cost exploration of chemical space for novel material selection.

Spacecraft Autonomy through Computer Vision and Onboard Planning

Mon, 01 Sep 2025 00:00:00 GMT

Spacecraft Autonomy through Computer Vision and Onboard Planning Kacker, Shreeyam Earth observation (EO) from satellite platforms has experienced widespread growth since the commercialization and widespread availability of data, and has had large impacts on applications such as agriculture, disaster monitoring, and defense and intelligence. Observing unpredictable phenomena is still challenging for EO missions due to long lead times from scheduling, uplinking, and executing the image capture onboard the spacecraft. This delay between planning and execution means that conditions can change in between them, causing a task to become unobservable and missed in the meantime, for example due to cloud cover obscuring a target. Dynamic tasking (DT) is a mission concept that aims to mitigate this unpredictability by moving autonomy onboard the spacecraft and quickly reacting to conditions as observed, using several potential perception sources. In this work, we consider DT as applied to a tasked Earth-observing satellite, whose goal is to image Earth’s landmass at predefined targets. The considered goal in this work for DT and onboard autonomy is avoidance of cloud cover, which can cause up to 66% of imaging tasks to be occluded, but factoring real-world constraints on operationalization and onboard edge computing. Instead of using end-to-end learned methods, we build upon existing work on spacecraft scheduling, incorporating a mixed-integer linear program (MILP) scheduler as the primary scheduling algorithm. Rather than directly incorporating DT into a global problem, we instead develop a set of heuristics which can estimate the utility of lookahead actions. We construct these heuristics from two directions: one from a simplified and constrained version of the scheduling problem with order statistics, and a second using a convolutional neural network with large amounts of synthetically generated data. We also consider DT using information from meteorological satellites in geostationary Earth orbit (GEO), parameterizing information delay rather than performing detailed analysis of data pipelines. In cases where all tasks are equally valued, all DT cases tested, including both meteorological and vision cases, outperform the conventional scheduler across all trials, ranging between 40% and 100% increase in total schedule utility based on cloud-free captures, depending on the DT method used. In cases where tasks have Pareto-distributed utility, the gap between the omniscient and conventional schedule shrinks drastically, to within 4% of total utility, and only a single DT method outperforms the conventional schedule consistently, as the environment becomes significantly more challenging due to asymmetric upside and downside risk. We also present methods by which to fractionate global state such that data can be efficiently stored and updated across a satellite constellation, allowing these heuristics to continue working across the constellation with minimal modification.

Large-Scale Multi-Robot Spatial Perception

Mon, 01 Sep 2025 00:00:00 GMT

Large-Scale Multi-Robot Spatial Perception Chang, Yun This thesis addresses the challenge of scalable and robust multi-robot spatial perception, with the goal of supporting autonomous task execution in large-scale environments. The work focuses on two core issues: scaling to large, complex environments, and incorporating highlevel scene understanding to enable autonomy for complex tasks. Current multi-robot systems typically focus on geometric reconstruction for navigation, but often fall short in providing the scene understanding needed for complex decision-making and task execution in real-world environments. Conversely, many recent demonstrations of autonomous task execution are limited to small, controlled environments, with few methods addressing scalability to larger scenes. This work bridges this gap by integrating multi-robot simultaneous localization and mapping (SLAM) with spatial perception in order to support downstream autonomy for complex tasks. We begin by introducing methods to enhance the robustness and efficiency of loop closure detection in centralized multi-robot SLAM, focusing on prioritizing loop closures and mitigating the impact of incorrect loop closures in large-scale environments. We then present the first fully distributed metric-semantic SLAM system for multi-robot teams, which supports real-time semantic mapping and enables large-scale deployments with up to 8 robots and 8 kilometers of traversal. To improve reasoning across robot teams, we extend this work to 3D scene graphs, proposing a framework for collaboratively building and maintaining a shared multi-robot scene graph online. Additionally, we introduce algorithms for task-oriented compression of 3D scene graphs to support communication across robots under bandwidth constraints. Finally, we explore open-set scene understanding made possible by advances in visual-language models and highlight the need for task-driven mapping. Building on this, we propose a novel framework for grounding high-level language commands into scene graphs, enabling robots to decompose high-level tasks into executable subtasks while focusing on task-relevant components of the environment. The contributions of this thesis are validated through experimental evaluations in extreme environments and real-world deployments, where multi-robot teams operate in large-scale settings. These experiments tackle a broad range of tasks, from navigation and object search to executing high-level language commands (e.g., “clean the room”). Our contributions advance multi-robot large-scale spatial perception and have the potential to impact real-world applications such as exploration, service robotics, and search and rescue, where autonomous multi-robot teams are essential for performing complex tasks in large environments.

Locality: a case study from Äiwoo

Mon, 01 Sep 2025 00:00:00 GMT

Locality: a case study from Äiwoo Roversi, Giovanni This thesis explores issues in the syntax of Äiwoo, an understudied Oceanic language from the Solomon Islands. This language showcases an intricate system of clausal alternations, where several parameters vary independently from each other: verbal morphology, word order, and possibilities of Ā-extraction (i.e., which argument(s) may or may not be extracted). As the title indicates, this thesis is set up as a case study, in a sort of “learn by doing” fashion. I attempt to build a descriptively and explanatory adequate formal model of the clausal alternation system in Äiwoo, within a Minimalist framework. By doing so, I examine what a theory of grammar must look like for the model to work as intended. Building such a model of Äiwoo teaches us something about a number of central issues in syntactic theory such as the locality of movement, the A /Ā-distinction, and the syntax of Austronesian languages specifically. I show that conjoining van Urk’s (2015) theory of the A /Ā-distinction and to the independent idea of featurally relativized probes (Béjar 2003), we predict the existence of “non-local A-movement”, that is, movement with the binding-theoretical properties of A-movement that nonetheless does not obey strict DPlocality, and crucially without the need of invoking the notion of “mixed A /Ā-movement”. I show that two instances of this predicted kind of movement are attested in the Äiwoo clause: movement to both spec,TP and spec,CP can target either the subject or – nonlocally – the object, depending on their features. I also propose that features assigned by a probe to a goal (“goal-flagging”; Deal to appear, Clem & Deal 2024) can be further manipulated by the syntax, being searched for by a higher probe. Further, Äiwoo shows an interesting instantiation of the Austronesian “pivot-only” Āextraction restriction, in that it comes with a series of exceptions. I argue that in Äiwoo, this restriction is caused by an Ā-intervention effect, contra analyses of this phenomenon in other Austronesian languages based on phasehood (Rackowski & Richards 2005, Erlewine & C. Lim 2023, Hsieh 2025, a.o.) or DP-intervention (Aldridge 2004, 2008, a.o.). Notably, as soon as the highest DP in a clause does not carry Ā-features, the restriction vanishes, thus allowing for the “exceptional” extraction of lower arguments.

Rapid In-Space Assembly and Manufacturing of Large Reticulated Truss Structures

Mon, 01 Sep 2025 00:00:00 GMT

Rapid In-Space Assembly and Manufacturing of Large Reticulated Truss Structures Bhundiya, Harsh G. Modern deployable space structures have enabled spectacular missions like the James Webb Space Telescope, but they are constrained by the rocket fairing and a tradeoff between deployed size and structural precision that limits their use for future communications and astronomy applications. In-space assembly and manufacturing (ISAM), i.e., the construction of structures in the space environment, offers an approach to overcome these issues and enable novel missions both on orbit and on planetary surfaces. Structures constructed in space can be optimized for loads in space, achieve higher packaging ratios, and increase mission flexibility. Given these benefits and decreasing launch costs from modern reusable rockets, there is a resurgence of interest in ISAM from academic, commercial, and governmental entities. However, current ISAM concepts are hindered by inefficient construction processes with high size, weight, and power requirements and a lack of systems-level design of spacecraft and construction processes. This thesis aims to address these challenges to enable energy-efficient, rapid ISAM of large space structures. The first contribution is an analysis of the fabrication time of large truss structures, considering the constraints of spacecraft power, attitude control authority, and avoidance of flexural vibrations. The analysis shows that angular momentum storage of the spacecraft and flexibility of the structure are dominant constraints on fabrication time of gridshell geometries with diameters over 60 m, while the available power and control torque limit fabrication time for diameters under 60 m. This trade study provides quantitative estimates of the total fabrication time, e.g., five spacecraft constructing a 200 m diameter gridshell in five days, and highlights design tradeoffs to enable rapid ISAM, including using multiple spacecraft and varying the feedstock material based on the structure size. Motivated by the long fabrication timescales, the second contribution is an understanding of spacecraft attitude dynamics with changes in mass properties and environmental disturbances during construction. In particular, variable-mass rigid body dynamics are used to understand the feasibility of gravity gradient capture, a passive approach that exploits the gravity gradient disturbance during ISAM. The concept is illustrated with two case studies on the construction of truss structures, a 2D triangle unit cell and a 3D curved gridshell, by spacecraft in circular orbits. Based on the time reversibility of the equations of motion, initial conditions are computed that result in gravity gradient capture by solving the equations backward in time, considering the changes in mass properties from the prescribed construction sequence. The analysis highlights both the feasibility of passive gravity gradient capture and the sensitivity of initial conditions to small perturbations. It is found that deploying a gravity gradient boom before the start of the construction sequence can decrease this initial condition sensitivity by an order of magnitude, and more generally, designing an ISAM process to maintain the minimum principal inertia axis in the direction of the orbit radius vector can facilitate the robust passive gravity gradient capture of large structures. Finally, to aid the design of attitude control systems for ISAM spacecraft, ground experiments are presented to understand the attitude dynamics during Bend-Forming, a candidate deformation process for fabricating truss structures. The experimental results reveal the effect of metal springback during construction, which causes flexible vibrations and coupled motion of both the truss and spacecraft. Additionally, experiments with closed-loop, thruster-based position and attitude control highlight the possibility of control-structure interactions during construction, due to the decreasing natural frequency of the truss structure. Together, these contributions provide a framework for the efficient design of future ISAM spacecraft and construction processes to enable the next generation of space structures.

Space Optical Interferometry for High-Resolution Coherent Imaging of Astronomical Objects

Mon, 01 Sep 2025 00:00:00 GMT

Space Optical Interferometry for High-Resolution Coherent Imaging of Astronomical Objects Black, Mason R. The hunt for Earth-like exoplanets is one of the great scientific endeavors of the 21st century. To date, the characterization of known exoplanets with instruments like JWST has been spatially unresolved—we can study atmospheric constituents via spectroscopy, but we cannot see continents, synoptic weather, or the geographic distribution of potential spectral biosignatures. The diffraction limit dictates that an optical aperture with sufficient angular resolution to resolve planetary scale features from the vantage point of our solar system would be prohibitively immense—hundreds to thousands of kilometers in diameter at visible wavelengths. Optical interferometry offers perhaps the only path to the required angular resolution by interfering light from multiple telescopes, or sub-apertures, providing Fourier components at a resolution corresponding to the sub-aperture separation. In recent decades, ground-based interferometry has made major strides in sensitivity by calibrating for atmosphere-induced piston errors to enable long coherent integration times. In addition to high-resolution astrometry, these sensitivity gains have allowed for milliarcsecond-scale imaging of bright astronomical objects. Still, optical interferometry from the Earth’s surface faces many fundamental performance limitations, and a space-based system would allow for the study of much dimmer targets at higher diffraction-limited resolutions, taking us a step closer to one day mapping exo-Earths. With recent advances in satellite miniaturization and lower cost-to-orbit, multiple groups have proposed new designs for a first demonstration mission, but no astronomical interferometer has yet flown in space. This work investigates the expected performance of first- and second-generation space optical interferometer concepts for astronomical imaging, focusing on maturing the technology that will be needed to push sensitivity and resolution beyond what is currently possible from the ground. The first mission envisioned is a formation flying pathfinder comprising three CubeSats, aiming to demonstrate the first measurements of interference fringes from starlight collected by separated space telescopes. This feat will require sub-wavelength matching of the optical path lengths traveled by the starlight to maintain the mutual coherence, which is achieved using rapid measurements of the interference fringe itself as a source of path length feedback. The performance of this fringe tracking is modeled via a time-domain control simulation accounting for the micro-vibration disturbance environment that would be expected on a CubeSat platform using reaction wheels for attitude control, which could induce up to 5 µm in optical path length noise and several arcseconds in optical alignment errors if left uncorrected. Also considered are the optical losses and noise sources associated with photon arrival statistics and detection as well as the beam pointing jitter. Simulation results indicate that such a mission would be able to stabilize interference fringes on at least the fifty brightest stars to better than 45 nm even under pessimistic disturbance assumptions, which would be sufficient to demonstrate the feasibility of space interferometry for a subsequent larger mission. The second mission concept analyzed aims to push the limits of faint-object interferometry from space by implementing a dual-feed beam combiner for fringe stabilization and phase referencing using bright off-axis guide stars. A three-telescope interferometer is assessed in its ability to map the surfaces of recently discovered dwarf planets in the outer solar system from a sun-synchronous Earth orbit. The population of stars usable as an interferometric phase reference is found to be within the off-axis field of view permitted by a 1-meter differential optical delay line, aiding image reconstruction via use of the Fourier phase referenced to a fixed point in the sky. Bayesian statistical imaging algorithms are employed to demonstrate recovery of an image from simulated noisy measurements of an 18th magnitude rotating object 37 millarcseconds in diameter, but results indicate that to do this with the integration times permitted by the chosen orbital formation would require approximately Hubble-sized telescope apertures. Potential alternative operational strategies to enable longer integration times with more modest apertures are discussed. Informed by the simulation results, recommendations are presented for near-term technology development in support of space interferometry.

Synthetic Mucins for Microbial Modulation

Mon, 01 Sep 2025 00:00:00 GMT

Synthetic Mucins for Microbial Modulation Barnes, Carolyn E. Mucus is a ubiquitous hydrogel that coats all epithelial cell surfaces. Once thought to be an inert hydrogel, the mucosal barrier is now recognized as a critical component of the innate immune system. It serves not only as a physical and chemical barrier against foreign objects, pathogens, and environmental stressors, but also as a selective interface that shapes an organism’s microbiome. Many of these functions are mediated by the primary structural component of mucus: the mucin protein. Mucins are large, densely glycosylated proteins, with carbohydrates contributing up to 80% of their molecular weight. In addition to mediating microbial interactions, mucins contribute to the biophysical properties of mucus that enable lubrication, adhesion, and protection of tissues. Beyond their physical responsibilities, mucins modulate virulence of microbes, promote cultivation of commensal bacteria through adhesion points and nutrient presentation, and mediate critical immune modulations of the host. However, molecular-level insights remain lacking in understanding mucin structure as well as function. The large size and heterogeneity of mucins and their glycosylation have made it challenging to parse the individual contributions of mucin structural features to biological function. Synthetic mucin mimics, developed using polymer chemistry, offering a promising strategy to probe these structure–function relationships by enabling precise control over molecular weight, glycan identity and density, polymer architecture, and morphology. To address these challenges, we developed novel synthetic mucins to elucidate mucin structure–function relationships. Emphasizing the importance of mucin’s extended morphology, we designed new synthetic strategies to isolate and investigate the impact of mucin structural motifs, such as anionic glycan identity and bottlebrush architectures, on synthetic mucin morphology (Chapter 2). We next demonstrated that synthetic mucins can provide insight into the glycan-binding preferences of probiotic bacteria, highlighting the roles of mucins in shaping microbial organization within the microbiome and emphasizing the potential of synthetic mucins as prebiotics (Chapter 3). Finally, recognizing that mucins key modulators of microbial virulence and infection, we explored the potential of synthetic mucins as antibacterial scaffolds capable of delivering therapeutic cargoes. This work emphasized the importance of understanding how polymer structure influences biological activity and targeting capabilities (Chapter 4). Ultimately, we anticipate that the synthetic mucin platform developed in this work will help address fundamental questions in mucin biology and advance the development of mucin-inspired therapeutic materials.

Techno-Economic Assessment of Grid-Scale Energy Storage Technologies Under Evolving Market and Decarbonization Scenarios: Liquid Air and Lithium-ion Battery Systems

Mon, 01 Sep 2025 00:00:00 GMT

Techno-Economic Assessment of Grid-Scale Energy Storage Technologies Under Evolving Market and Decarbonization Scenarios: Liquid Air and Lithium-ion Battery Systems Cetegen, Shaylin Ashley As global energy systems transition toward low-carbon futures, long-duration energy storage technologies are expected to play a critical role in ensuring grid reliability and flexibility. Liquid air energy storage has emerged as a promising long-duration energy storage solution due to its scalability, ability to be sited flexibly, and environmental sustainability. This thesis investigates the technical modeling and economic feasibility of liquid air energy storage systems under both current and projected future electricity market conditions using a combination of process modeling, mathematical optimization, and techno-economic analysis. The study begins with an exploratory investigation into the modeling of process components in liquid air energy storage systems, emphasizing multistream heat exchangers and the emergence of bifurcation phenomena in thermodynamic models. This is followed by an economic optimization of standalone LAES systems across various electricity markets in the United States and Europe. A mixed-integer linear programming framework is developed to simultaneously optimize system design and hourly operation with the objective of maximizing net present value, providing new insights to inform LAES deployment strategies. Building on these foundations, a forward-looking economic analysis is conducted for 18 electricity regions in the United States under eight distinct decarbonization scenarios. This analysis is based on future electricity price projections from the Cambium 2023 dataset developed by the National Renewable Energy Laboratory. The results identify Texas and Florida as especially favorable markets for liquid air energy storage under a range of decarbonization pathways. Sensitivity analyses reveal that economic incentives such as capital expenditure subsidies have a greater impact on profitability than technical improvements such as increased round-trip efficiency. Finally, the thesis presents a comparative economic assessment of liquid air energy storage systems and lithium-ion battery systems across key metrics, including levelized cost of storage, system lifetime, siting constraints, and cost-effectiveness over multi-hour to multi-day durations. The findings show that liquid air energy storage systems can offer lower cost and greater flexibility than lithium-ion batteries for long-duration applications. This work establishes a generalizable framework for assessing the economic feasibility of emerging energy storage technologies and offers practical insights for decision-makers evaluating the role of liquid air energy storage in future electricity systems.

Active Epistemology

Mon, 01 Sep 2025 00:00:00 GMT

Active Epistemology Fiat, Yonathan Orthodox epistemology deals with what we might call "passive agents:" agents that merely respond to what's given to them. But we care most about are active agents: agents that can sing, dance, seek evidence, make conjectures, etc. In this dissertation, I explore three ways in which this observation is relevant to the traditional questions of epistemology. I argue that it can shine a new light on the nature of knowledge, help us make sense of standard scientific practices, and help solve some famous puzzles in epistemology. Chapter 1 asks how the fact that we know something is related to the question of whether we should seek more evidence. This problem has been discussed in the philosophical literature under the name of "the dogmatism puzzle." In this chapter, I use the multi-armed bandit model to argue for a new solution to the dogmatism puzzle: knowledge often requires proper maintenance. Chapter 2 presents a novel account of significance testing, one of the most important practices in science. It shows that we can make sense of this practice if we accept the claim that predictions are better than accommodation. I then use this account to answer some of the many objections to significance testing. Finally, chapter 3 argues that what we know often depends on our choices, and not merely on what is given to us. We can gain knowledge by choosing something, whereas if we fail to choose, or attempt to choose too many things, we fail to gain knowledge. I then use this idea to offer a new solution to the lottery paradox, to help understand inductive knowledge, and, once again, to make sense of significance testing and related practices.

Mechanisms of genetic risk in Alzheimer’s disease

Mon, 01 Sep 2025 00:00:00 GMT

Mechanisms of genetic risk in Alzheimer’s disease von Maydell, Djuna Sporadic Alzheimer's disease (AD) accounts for the majority of dementia cases worldwide, yet effective treatments remain limited. Genetic variants associated with AD provide insight into disease etiology and highlight potential therapeutic targets. The ε4 allele of the APOE gene is the strongest genetic risk factor for AD, and rare variants in the ABCA7 gene are among the next most significant. Both genes encode lipid transporters, suggesting an important role for lipid metabolism in AD etiology. However, the exact cellular mechanisms through which these variants increase AD risk remain incompletely understood. After a brief introduction in Chapter 1, Chapter 2 demonstrates that damaging ABCA7 variants disrupt neuronal phosphatidylcholine metabolism and mitochondrial function. These defects were reversed by supplementation with the phosphatidylcholine precursor cytidine diphosphate-choline (CDP-choline). Chapter 3 shows that APOE4-expressing oligodendrocytes exhibit altered cholesterol transport and impaired myelination. Pharmacological modulation of cholesterol transport in the brain reversed these defects, improving cognitive function in mouse models. These findings suggest that lipid-related mechanisms represent a class of targetable drivers of AD risk, but it remains unclear whether lipid-targeted treatments would be broadly applicable across AD or restricted to specific disease subtypes. Chapter 4 introduces a practical framework for identifying disease subtypes in high-dimensional biological data based on principles from machine learning and data attribution, and applies it to explore transcriptional subtypes among AD brains. Together, these studies reveal potential mechanisms of genetic risk in AD, highlight lipid disruptions as upstream mediators, and propose a practical framework for uncovering AD subtypes.

The Realm of Grace

Mon, 01 Sep 2025 00:00:00 GMT

The Realm of Grace Mathew, Abraham This dissertation examines three familiar interpersonal phenomena—repentance after wronging another, forgiveness in the wake of being wronged, and reciprocation after being helped—and what they teach us about moral obligation in general. When a wrongdoer sincerely repents, we tend to see that as a reason to forgive them. But why? Chapter 1 offers an answer that I call the ‘Redemptive View’. Repentance, I contend, involves changing how one relates to a past misdeed, acknowledging its wrongness and committing to moral betterment. Consequently, the wrong comes to play a new role in the wrongdoer’s life, becoming a source of moral learning and an impetus towards moral growth. As a result, the wrong is imbued with a new, positive significance that it lacked so far, generating a reason to forgive. But are we ever obliged to forgive our wrongdoers? Many think not. On the orthodox view, all obligations to others correlate with demandable or enforceable rights, and since no one has the standing to demand or enforce forgiveness, no one can be owed it. Chapter 2 disputes this orthodoxy, arguing that forgiveness is sometimes obligatory, even if no wrongdoer has a right to it. In particular, if you’ve previously accepted a gracious offer of forgiveness and are now in a position to extend an equally or less gracious offer to one of your wrongdoers, then you must forgive. Otherwise, you would be imposing on others a harsher standard than you accepted for yourself. It turns out that disparate instances of forgiving and being forgiven within a life are connected in surprising ways: accepting forgiveness in the past can bear on whether present forgiveness is discretionary or obligatory. Chapter 3 turns to cases of standing in debt to someone who helps us—debts we can repay by reciprocating. Some debts are transactional: they can be claimed or waived, and once repaid, the parties return to the status ex ante. Others resist this structure: they cannot be claimed or waived, and reciprocation only generates a persisting, often alternating cycle of mutual aid. What explains this distinctive normative profile? I argue that such non-transactional debts arise when an act of 2 care tacitly proposes a more intimate relationship. When the beneficiary responds in kind, the relationship is reshaped, and new constitutive norms take hold. These norms ensure persistence, but also a normatively healthy pattern of alternation: after all, each act in the cycle is an undemandable gift extended in a time of need, and each response an act of due gratitude. In all, this dissertation challenges the orthodoxy that if we owe something to others, they must have a right to it. We learn that the moral landscape isn’t exhausted by the realm of rights. There is also a ‘realm of grace’—a realm in which we are required to do much for others that they cannot demand of us.

Presuppositionless proxies of politeness: An (eventually) Optimality-Theoretic account

Mon, 01 Sep 2025 00:00:00 GMT

Presuppositionless proxies of politeness: An (eventually) Optimality-Theoretic account Wang, Ruoan Recent research has seen growing interest in the nature of social meaning, “the constellation of qualities and properties that linguistic forms convey about the social identity of language users” (Beltrama 2020, see also Eckert 2008). This dissertation concerns those linguistic forms termed polite pronouns. They convey one particular aspect of social identity: the social distance between language users, where a large social distance mandates the expression of politeness. Most existing work on polite pronouns has modeled polite meaning via dedicated grammatical mechanisms, such as a dedicated feature ([hon], Ackema & Neeleman 2016) or a dedicated dimension of meaning (McCready 2019). This dissertation shows that such dedicated mechanisms are superfluous, as existing grammatical mechanisms can be appropriated to describe and explain both the form and meaning of polite pronouns. Building on Wang (2023), I use a purpose-built typological sample of polite pronouns in >220 genetically and geographically diverse languages to establish the shapes of polite pronouns, showing that polite pronouns are obtained by asymmetrical recruitment of existing ϕ-featural values. Furthermore, the shapes of polite pronouns converge precisely with the shapes of semantic defaults, those ϕ-featural values underspecified in meaning, which must emerge when the context provides little or no information about number or person. To explain this convergence, I use the notion of negative politeness: respecting an interlocutor’s right to be unimpeded (Brown & Levinson 1987). I argue that semantic defaults and polite pronouns are morphologically identical because they are pragmatically identical: underspecification makes them well-suited to be avoidance mechanisms in the service of negative politeness. This captures the intuition that avoidance behaviors are a core component of expressing politeness. Specifically, polite pronouns enable speakers to avoid making presumptions about aspects of the interlocutor. Hence, presuppositionlessness, proxies, and politeness are what this dissertation is all about. I implement this intuition in Optimality-Theoretic terms, where polite grammars are defined by an outranking of Markedness (which mandates speakers to avoid presupposition-rich forms) over Faithfulness (which mandates speakers to be as informative as possible). The resulting system is restrictive but powerful, delivering a factorial typology which exactly mirrors the recruitment asymmetries. With no stipulations specific to polite pronouns, the account is furthermore able to make concrete predictions about politeness phenomena beyond the ϕ-featural domain. Happily, the resulting account is as lean as can be. Its main innovations are methodological and theoretical. Methodologically, the investigation is led by a large cross-linguistic sample, and the additional consideration of use conditions alongside morphological exponence. Theoretically, recruitment affords us with a fresh look at how the universal inventory of features is organized. This dissertation shows that recruitment is not only viable as a meta-grammatical operation, but even desirable for reasons of economy or computational efficiency.

Aft Fuselage Boundary-Layer Ingesting Propulsion Systems for Turbo-Electric Aircraft

Mon, 01 Sep 2025 00:00:00 GMT

Aft Fuselage Boundary-Layer Ingesting Propulsion Systems for Turbo-Electric Aircraft Chen, Zhibo This thesis focuses on a rigorous assessment of a tube-and-wing turbo-electric boundarylayer ingesting (BLI) aircraft, including (i) definitions of the aerodynamic attributes for the best fuel burn benefit, (ii) design guidelines to achieve these attributes, and (iii) a conceptual design of tail-BLI aircraft. The assessment of the BLI benefit relative to a baseline conventional aircraft is based on a TASOPT-CFD analysis framework, using a CFD body-force model for fan representation and a power balance for aircraft performance analysis. The aircraft mission is to carry a 17500 kg payload over 5500 km range at a cruise Mach number of 0.8. The baseline aircraft has two next-generation geared turbofan engines with fan pressure ratio (FPR) of 1.35. The tail-BLI aircraft has six integrated propulsors with electric fans of 1.40 FPR in addition to two underwing turbofans; it is estimated to achieve an 8.5% fuel burn benefit compared to the baseline. To achieve the benefit, the tail-BLI aircraft has a non-axisymmetric aft fuselage that creates axial vorticity in the tail-mounted propulsor inflow, providing co-swirl to reduce rotor incidence variations to improve the fan efficiency and stall margin. The best propulsor configuration, with six 24-inch fans, is established balancing the boundary-layer kinetic energy defect ingestion and propulsion system weight. The integrated propulsor aerodynamic design includes an upstream inlet extension, an inlet leading edge design tailored for each propulsor, a supercritical nacelle, an annular nozzle, an elliptic nozzle plug, and a non-axisymmetric tail cone, to minimize shock loss and achieve attached flow at cruise and takeoff. The BLI fan pressure ratio was selected based on a trade between engine propulsive efficiency and propulsion system weight. Non-axisymmetric stator inlet angles are also used to reduce the stator incidence variations. Fan forcing analysis suggests that the BLI rotor unsteady aerodynamic loading has negligible impact on blade life. Fan stability analysis shows that the BLI inlet distortion reduces the rotor stall margin by 4.5% and 2.7% relative to the rotor in uniform flow, at cruise and takeoff, respectively. The tail-BLI propulsors are thus estimated to have appropriate operability with BLI inlet distortion. In conclusion, the distributed aft fuselage boundary-layer ingesting propulsion system is suggested to offer a relevant pathway for tube-and-wing configurations with a potential major advancement in fuel burn reduction.

Adaptive Optimization Algorithms for Step-Size Selection and Online Problem Parameter Estimation

Mon, 01 Sep 2025 00:00:00 GMT

Adaptive Optimization Algorithms for Step-Size Selection and Online Problem Parameter Estimation Cavalcanti Vilela, João Vítor Optimization algorithms have long been fundamental tools across science and engineering, and now are also at the center of the rise of machine learning and artificial intelligence. However, extracting good practical performance from many of these algorithms depends on careful manual calibration and tuning. In fact, the algorithms with best theoretical guarantees do not always perform best in practice. In this light, reducing the effort and skill required to set up optimization algorithms can save immeasurable amounts of time and resources. This thesis makes two contributions to this end, proposing optimization algorithms that require less supervision by adaptively selecting step-sizes and estimating problem parameters online. First, we revisit the foundational subroutine called backtracking line search (BLS). Typically, a base algorithm calls BLS to search for a parameter (e.g., step-size) such that the iterates of that algorithm satisfy a given condition (e.g., Armijo, descent lemma) that leads to desirable behavior (e.g., reducing the value of the objective function). To find a feasible parameter, BLS successively adjusts a parameter candidate by a constant factor until the given condition is satisfied. We propose to instead adjust the parameter candidate by an adaptive factor that takes into account the degree to which the given condition is violated. This adaptive BLS (ABLS) subroutine adds no computational burden relative to BLS, but can lead to significantly better practical results. Experiments on over fifteen real-world datasets demonstrate that ABLS can be more robust than BLS to problem set ups and require significantly fewer condition evaluations to return higher-quality parameters. At the same time, we prove that ABLS enjoys essentially the same theoretical guarantees of BLS. The second contribution of this thesis is a parameter-free algorithm for smooth and strongly convex objective problems called NAG-free. To our knowledge, NAG-free is the first adaptive algorithm capable of directly estimating the strong convexity parameter without priors or resorting to restart schemes. We prove that NAG-free converges globally at least as fast gradient descent, and achieves accelerated convergence locally if the Hessian is locally smooth and other mild additional assumptions hold. Prominent classes of machine learning problems with locally smooth Hessian include the regularized logistic loss, ridge regression, exponential family negative log-likelihoods with bounded natural parameters, and Moreau envelope smoothing. We present real-world experiments in which NAG-free performs comparably well with restart schemes, demonstrating that it can adapt to better local curvature conditions represented by the smoothness and strong convexity parameters.

Expanding Structural Complexity in Condensed Phosphates: P(V) Reagents for Controlled Phosphoanhydride Bond Construction

Mon, 01 Sep 2025 00:00:00 GMT

Expanding Structural Complexity in Condensed Phosphates: P(V) Reagents for Controlled Phosphoanhydride Bond Construction Qian, Kevin At the outset, Chapter 1 begins by examining the central importance of phosphorus in both nature and industry, situating the chemistry of polyphosphates within a broader historical and conceptual context. A brief overview of the history of polyphosphate research in the life sciences is given, as well as a discussion on the persistent ambiguities in condensed phosphate nomenclature. To frame the work in the following chapters, the idea of the "hydrocarbon analogy" is introduced: a conceptual strategy that draws parallels between the structure and reactivity of organic molecules and that of inorganic phosphate constructs, thus offering a new way of thinking about molecular complexity in this underexplored chemical space. Building from this foundation, Chapter 2 details the discovery of a diphosphorylation reagent, identified to be a mixture of neutral zwitterionic adducts of P4O10 and pyridine. This reagent emerged serendipitously from our efforts to activate trimetaphosphate and has proved to be a powerful tool for synthesizing functionalized cyclic metaphosphates. Chapter 3 is the extrapolation of our strategy to activate otherwise inert metaphosphates by forming ring-strained bicyclic ultraphosphates. We discuss the reactivity of [P5O14]3–, the oligophosphate analog of the bicyclic hydrocarbon housane. Attempts to push this strategy further toward the synthesis of a hexaphosphorylation reagent were ultimately unsuccessful but provided valuable insight into the limitations of this ring-strain activation paradigm. The methods developed in earlier chapters set the stage for our collaboration with the Fielder group, described in Chapter 4. We designed new reagents to chemoselectively conjugate polyphosphates to densely functionalized peptides and proteins. These synthetic strategies pave the way for the study of recently discovered, but poorly characterized post-translational modifications featuring novel phosphorylation modes. Finally, Chapter 5 presents a series of unpublished studies that expand on the themes of the previous chapter. Together, these investigations contribute to a growing body of knowledge aimed at broadening the chemical space of condensed inorganic phosphates.

Unifying electric field-mediated heterogeneous catalysis

Mon, 01 Sep 2025 00:00:00 GMT

Unifying electric field-mediated heterogeneous catalysis Dinakar, Bhavish Electric fields in the catalytic active sites of enzymes, originating from charged amino acid residues in the protein scaffold, have been invoked as a key factor governing the extraordinary activities and selectivities of enzymes for catalyzing chemical transformations. Models and experimental measurements of these electric fields have been utilized to quantitatively rationalize reaction kinetics, understand active-site solvent structures, and design better enzymatic catalysts. Despite the success of using electric fields to study enzymatic systems, this electric field approach has not yet been widely used in studying liquid phase heterogeneous catalysis, largely due to the difficulty in quantifying electric fields at active sites as well as challenges in designing heterogeneous catalysts with precise control of electric fields. In this thesis, we extend this electric field methodology into the realm of liquid-phase heterogeneous catalysis, both to accelerate heterogeneously catalyzed reactions and to understand solvent structures in catalyst pores. First, we design an electrochemical basket reactor system to apply an electric field to the surface of Brønsted-acidic carbon nanotube catalysts via an applied potential, and we examine the effect of electric field on acid-catalyzed alcohol dehydration rates of 1-methylcyclopentanol to 1-methylcyclopentene in an electrolyte-containing acetonitrile solution. We observe that the reaction rate is remarkably sensitive to electric field, with reaction rates increasing ~100,000 fold over 0.5 V of applied potential, and we propose a model that explains the rate-potential scaling and dependence on ionic strength. In further support of our model, we experimentally observe a theorized “isokinetic potential” where the reaction rate is independent of ionic strength. Through reactive base titrations, we quantify the density of active sites and demonstrate that the rate promotion is due to increasing the site activity, rather than increasing the number of active sites. Next, we demonstrate that this electric field effect does not require a special electrochemistry setup to manifest and can also be observed when a catalyst particle is simply touching another electrically conductive material. Through a carefully designed suite of experimental controls, we show that the intrinsic activity of acidic carbon nanotubes changes by an order of magnitude when they are in electrical contact with inert, thermally reduced carbon nanotubes, via the same mechanism that we previously observed when rates changed with an applied potential. We then shift gears to examine solvent structures in confined zeolite pores using vibrational Stark spectroscopy, an infrared spectroscopic technique developed for measuring electric fields at enzyme active sites. Using Ti-Beta zeolite as a test case, we develop a method that quantifies the electric field experienced by a probe molecule at the Ti active sites in solvent-filled pores. We find that the electric field varies with solvent identity and also with zeolite hydrophobicity, indicating that the secondary sphere interactions of the solvent with the zeolite framework result in distinct solvent structures in the hydrophobic and hydrophilic zeolites. Furthermore, we observe that this method identifies distinct electric fields between hydrophobic and hydrophilic zeolites even in the absence of solvent, offering this technique as a method for distinguishing types of active site environments. Finally, we apply this approach to examine hydrogen-bonding interactions inside Ti-Beta zeolite pores when filled with secondary alcohol solvents, motivated by previous work which observed that intraporous solvent structures varied significantly with changes in zeolite hydrophobicity. Using an infrared liquid flow cell that we designed to achieve record signal-to-noise and in-pore selectivity, we find that hydrogen-bonding interactions between a probe molecule and solvent are different between the ensemble-average in-pore environment and at the zeolite active site, revealing that there may be a discrepancy between typically observed intraporous bulk solvent structures invoked to explain kinetic phenomena and the kinetically relevant active-site solvent structures. Collectively, this thesis demonstrates that techniques and models of electric field-induced catalysis previously applied to understand biological systems can also be utilized to improve catalyst activity and understand the structure of solvent under confinement in liquid-phase heterogeneous catalysis. We hope that future work will expand on our methods, applying them to further investigate the effects of applied electric field on reactivity and utilizing vibrational probes to aid in understanding local structure at catalyst active sites.

Development of Modular Strategies for Enhancing Biomanufacturing in Komagataella phaffii

Mon, 01 Sep 2025 00:00:00 GMT

Development of Modular Strategies for Enhancing Biomanufacturing in Komagataella phaffii Shi, Shuting The landscape of protein therapeutics is advancing towards more complex modalities that offer greater medical potential but present significant challenges to existing biomanufacturing frameworks. Alongside these technical challenges, socio-economic factors drive an urgent need for accelerated, cost-effective biomanufacturing to increase rapid response capabilities, ensure equitable access to advanced treatments, and alleviate the economic strain on global healthcare systems. This creates a significant and unresolved gap: the cost-effective production of increasingly complex protein therapeutics under an accelerated timeline. Komagataella phaffii holds great potential to fill this gap, as it is a eukaryotic microorganism well-established for the rapid and cost-effective expression of recombinant proteins. However, realizing this potential requires advanced engineering strategies to extend K. phaffii’s strengths to these complex modalities. This thesis presents the development of modular strategies to enhance the biomanufacturing of such complex therapeutics in K. phaffii. The first part of this thesis focuses on providing alternative manufacturing strategies for VLP vaccines, a promising next-generation vaccine platform whose adoption has been limited by manufacturing challenges. To streamline VLP vaccine development and manufacturing, a modular production framework was developed. This framework’s core strategy involves the secretory production of VLP scaffold subunits in K. phaffii followed by their in vitro assembly, which then allows for flexible "plug-and-display" antigen attachment to the pre-formed scaffold. This approach can be adopted for versatile antigen adaptation and can be stockpiled for rapid response. More importantly, this framework circumvents the manufacturing challenges associated with traditional intracellular VLP production, such as complex downstream purification that leads to increased production cost and decreased yield and quality. This transition not only makes each modular step more suited for current available operational units, especially downstream processing, significantly increasing scalability and reducing cost, but also holds promise for integration into future continuous manufacturing frameworks. A specific implementation and key outcome of this approach was the development of the SpyCatcher::I53-50 modular VLP scaffold, which was then validated by demonstrating the high-fidelity display of a SpyTag-fused HIV Env trimer antigen with preservation of critical neutralizing epitopes, showcasing its potential as a promising modular VLP vaccine platform. Enabling the secretory production of the scaffold’s multimeric subunits required overcoming significant manufacturability challenges. For instance, proteolytic degradation of the SpyCatcher-153-50A fusion was resolved via protein engineering, while secretion of the aggregation-prone 153-50B subunit was achieved through a multi-pronged approach combining process optimization, host engineering, and a novel pseudo-chaperone strategy. These rational engineering efforts were not only crucial for producing these key building blocks but also serve as a practical roadmap for tackling other hard-to-produce targets. The challenges encountered in efficiently optimizing VLP subunit production, even with extensive rational engineering, underscored a broader imperative for more powerful and systematic optimization tools. This realization motivated the second part of this thesis: the development of a modular high-throughput screening (HTS) platform to accelerate the engineering of K. phaffii for enhanced production of complex biologics, using monoclonal antibodies (mAbs) as an industrially significant proof-of-concept. The HTS platform is built on a dual-mode yeast surface display (YSD) system. By establishing a quantitative correlation between surface display and secretion for a full-length mAb, this work unlocks the potential of YSD for reliably screening secretion phenotypes, enabling, for the first time, screening under normal cultivation conditions at an unprecedented scale. Its successful application in a genome-wide CRISPR/Cas9 knockout screen identified numerous novel host gene knockout targets. Individual validation of 20 top-ranked candidates confirmed that 15 led to statistically significant increases in mAb specific productivity, with the most effective target yielding an 8-fold improvement over the baseline strain. This represents a substantial technological advancement for K. phaffii engineering, offering a powerful engine to shift from empirical, low-throughput optimization towards systematic, data-driven cell line development. Designed as a modular platform, it holds promise for screening other perturbation libraries and for optimizing a wide range of protein targets beyond mAbs.

Development of a targeted lipid nanoparticle platform for in vivo RNA delivery to hematopoietic stem and progenitor cells

Mon, 01 Sep 2025 00:00:00 GMT

Development of a targeted lipid nanoparticle platform for in vivo RNA delivery to hematopoietic stem and progenitor cells Shi, Dennis Hematopoietic stem cells (HSCs) are rare cells residing in the bone marrow that are responsible for the generation and maintenance of the body’s immune system through a process known as hematopoiesis. Because of their self-renewal capability and crucial role in producing immune cells, HSCs have garnered a lot of therapeutic interest for the treatment of genetic blood disorders. However, current HSC gene therapies are autologous ex vivo transplantations which consists of three major steps: 1) mobilization and harvest of the patient’s own stem cells, 2) ex vivo editing of those cells, and 3) reinfusion of the edited cells back into the patient. While the results of these ex vivo therapies are quite promising, there are many limitations associated with the current process. First, the manufacturing process is logistically complex which results in high costs and lengthy times. Second, prior to re-infusion of the edited cells, patients must undergo a conditioning regimen (done with a chemotherapeutic) to deplete the existing cells from the bone marrow and make space for the edited cells to graft. This conditioning has many deleterious side effects including organ damage, increased risk of infection, and infertility. One strategy to bypass the existing limitations of ex vivo HSC therapy is to directly edit the HSCs in vivo. Here, we describe the development of a targeted non-viral lipid nanoparticle (LNP) delivery system that can deliver RNA to hematopoietic stem and progenitor cells (HSPCs) in vivo following a single intravenous injection. We targeted CD117, a receptor that is expressed on HSCs, and conjugated an antibody against CD117 to our LNPs for receptormediated delivery of RNA. We demonstrated that modulation of certain LNP parameters such as circulation time and ligand density increase delivery to the bone marrow. Using this targeted platform, we demonstrated LNP uptake and delivery of both siRNA and Cre mRNA into HSPCs. In addition, in the Ai14 mouse model, we showed that HSCs transfected with our targeted LNPs maintain their stemness and functionality to produce mature immune cells. We also evaluated the overall biodistribution of our anti-CD117 LNP and investigated the downstream effects of delivery to organs other than the bone marrow. Finally, we explored in vivo gene editing using a variety of approaches. We optimized our LNP formulation to increase protein expression in bone marrow HSCs and used our optimized formulation for in vivo base editing.

A 3D human liver tissue model of the hepatobiliary junction

Mon, 01 Sep 2025 00:00:00 GMT

A 3D human liver tissue model of the hepatobiliary junction Westerfield, Ashley D. Cholestasis, or disruption in bile flow, is a poorly-understood feature of many liver diseases and is a well-established indication for liver transplant. Despite this clinical significance, many tissue engineering strategies for modeling or treating liver disease fail to recapitulate physiological bile flow. Recent advances in the field of tissue engineering and organoid technology have enabled the culture of human hepatocytes and bile duct cells in vitro, these models lack a key function of the liver which is bile transport. In this thesis, I first describe developments in bioengineering technology that have allowed for the culture and manipulation of bile duct cell organoids. I then present a 3D multicellular spheroid model that captures the structure and function of the human hepatobiliary junction—the interface between liver and bile duct cells that is often disrupted in liver disease. By co-aggregating primary human hepatocytes and bile duct cells, I engineer a liver spheroid model that recapitulates physiological bile flow through a functional connection between the two cell types. These spheroids maintain cell polarity and transport bile from hepatocyte canaliculi to bile duct structures. This function is quantified by leveraging a high-throughput imaging assay with AI-assisted analysis to track junction formation and bile flow over time. I also use this system to model ischemic injury of the bile duct, a common complication of liver transplant, by tuning the oxygen parameters of the spheroid culture. In this injury model, I observe and describe two processes that potentially contribute to injury: a reversible loss of canalicular function during hypoxia, followed by selective bile duct cell death after reoxygenation. This human-based, scalable platform provides a new tool to study bile duct biology, understand mechanisms of biliary injury after liver transplant, and support drug discovery efforts for cholestatic liver diseases.

Modeling and Control of a Continuous Lyophilizer

Mon, 01 Sep 2025 00:00:00 GMT

Modeling and Control of a Continuous Lyophilizer Kadambi, Rohan Patrick Lyophilization, or vacuum freeze-drying, is a separations process in pharmaceutical manufacturing used to stabilize aqueous drug products by removing nearly all the water at cryogenic temperatures. This process is often applied to final-dose formulations of injectable drugs already dispensed in vials. Despite its benefits, freeze-drying is a slow process, with typical cycle times exceeding two days. To meet demand, lyophilization is applied to large batches of thousands to tens of thousands of vials at time. These large batches are plagued by non-uniformity, non-optimal operating conditions, and slow technology transfer across production scales. Continuous manufacturing offers a solution to many of these problems, with its demonstrated history in improving efficiency, homogeneity, and control through its inherent parallelization of operations. However, the need to interact directly with vials, as well as modify heat transfer to them, has delayed the development of a continuous lyophilization process. This work presents the design, control, and implementation of a modular continuous lyophilizer. The continuous lyophilizer is composed of a series of custom aluminum chambers around a magnetic levitation system, which facilitate the heat transfer and motion required. The modular nature ensures that critical geometry is conserved when production rate is scaled between laboratory-, pilot-, and industrial-scales. This system demonstrated continuous operation for four days on various solutions in standard 10R vials. In this work, novel continuous freezing and vacuum systems were designed to support the operation. Additionally, an innovative mass sensor was designed to monitor each vial traveling through the system independently. When combined with per-vial temperature data available from thermal imaging, this system enables a more comprehensive understanding of the process dynamics. This continuous process opens opportunities for expanding the use of lyophilization by simplifying technology transfer during scale-up, improving product uniformity, and increasing process productivity through optimal control.

Atomistic Insights into Disordered Proteins and Condensates via Molecular Simulations

Mon, 01 Sep 2025 00:00:00 GMT

Atomistic Insights into Disordered Proteins and Condensates via Molecular Simulations Wang, Cong Biomolecular condensates formed by intrinsically disordered proteins (IDPs) play essential roles in cellular organization and function, attracting broad scientific interest. In addition to experimental approaches, molecular dynamics simulations—particularly atomistic simulations—serve as powerful tools for gaining mechanistic insights into IDP conformations and inter- and intramolecular interactions within condensates. In this work, we developed a multiscale simulation strategy that balances efficiency and accuracy by combining coarse-grained and atomistic modeling. We further applied dimensionality reduction techniques to extract meaningful features from high-dimensional simulation data. Using this integrated framework, we investigated three scientific problems: (1) the sequence-dependent conformational ensembles of IDPs, (2) nonspecific yet selective condensate–drug interactions, and (3) the mechanism of formation of monocomponent, multiphasic condensates formed by tetrapeptide sequences.

An Analysis on Aircraft Overflight Noise Distribution on Airport Adjacent Communities

Mon, 01 Sep 2025 00:00:00 GMT

An Analysis on Aircraft Overflight Noise Distribution on Airport Adjacent Communities Wang, Z. Juju Aircraft overflight noise and can be a source of noise pollution and can also be the limiting factor in airport operations [1]. This research studies the distribution of overflight noise near 22 airports across 12 metropolitan areas in the United States. It uses a fast noise model to generate noise data from surveillance flight track data and correlates noise data with population data, income, and noise complaints when data is available. Findings reveal that population noise exposure is significantly influenced by urban density and proximity of the airport to city centers. Airports located farther from major cities, particularly newer and larger facilities like IAD, DFW, and DEN, tend to decrease noise exposure due to their expansive land areas and remote siting. Noise exposure is also impacted by operational procedures. Arrival procedures typically follow straight in paths aligned with runway centerlines, concentrating noise along specific corridors. We find a few exceptions to this trend in complex airspaces which have turns in their arrival procedures, often enabled by Area Navigation (RNAV) technology. This allows flights to avoid adjacent airspaces and also overfly targeted lower population areas. The implementation of Area Navigation and Performance Based Navigation technology has enabled greater use of arrival paths involving turns, which were previously limited to visual conditions. In contrast, departure paths and contours from departures are more varied due to routing flexibility. These are often shaped by noise abatement strategies and airspace constraints. Runway orientation relative to urban centers also plays a critical role in determining noise exposure, with runways directed away from cities generally resulting in fewer person-noise events. In a socioeconomic analysis, we find lower-income communities are more frequently located near high-noise zones, though exceptions exist in areas with desirable geographic features near the airport such as waterfront property.

Scaling Bayesian inference for generative models via probabilistic programming

Mon, 01 Sep 2025 00:00:00 GMT

Scaling Bayesian inference for generative models via probabilistic programming Loula Guimarães de Campos, João This thesis addresses these challenges for the field of data science, developing probabilistic programming methods that enable rational AI agents in that domain. The work is organized into two parts: Part I introduces GenLM and Adaptive Weighted Rejection Sampling for translating natural language instructions into structured programs with both syntactic and semantic constraints, outperforming existing approaches across a number of domains. Part II develops Bayesian generative models for tabular data that can answer a wide range of questions, yield stable inferences across subpopulations of different sizes, and scale to hundreds of millions of rows on GPUs; as well as early work on Large Population Models that unify heterogeneous datasets. Together, these contributions provide first steps towards a unified framework for creating AI agents that can rationally formalize and answer questions about data.

Folding and Quality Control Mechanisms of Procollagen

Mon, 01 Sep 2025 00:00:00 GMT

Folding and Quality Control Mechanisms of Procollagen Kim, Seo Yeon Procollagens undergo a complex folding process with the assistance of various ER proteostasis network components and enzymes that introduce post-translational modifications. In Chapter 2, we investigate the biological role of one of the post-translational modifications, the highly conserved N-glycosylation on procollagen C-propeptides by generating and characterizing multiple mouse models that lack the N-glycan on procollagen-I. We show that the lack of N-glycan can affect procollagen folding inside the cells, which translates to reduced bone mass in mice lacking the N-glycan. These data provide new insights into the roles of the evolutionarily conserved, yet underappreciated collagen’s N-glycan. Mutations in procollagen genes and other proteins important for procollagen folding often cause misfolding defects in procollagen that can ultimately lead to disease. In Chapter 3, we explore the various molecular mechanisms that the ER proteostasis network can use to recognize diverse classes of misfolding procollagen variants. Specifically, we apply cell engineering and quantitative mass spectrometry to elucidate the interactomes of diverse osteogenesis imperfecta-causing procollagen α1(I) variants with distinct folding defects, and show that ER proteins differentially engage with these misfolding procollagen variants. These findings provide a foundation for illuminating quality control pathways that the different types of misfolding procollagen variants are subject to during their biogenesis. Taken together, the work in this thesis meaningfully advances our knowledge in the mechanisms of procollagen folding and quality control.

Tribochemistry of Frictional Ignition in High-Pressure Oxygen

Mon, 01 Sep 2025 00:00:00 GMT

Tribochemistry of Frictional Ignition in High-Pressure Oxygen Garcia Jimenez, Andres Frictional heating of metals at sliding contacts in high-pressure oxygen environments can result in catastrophic metal fires. This phenomenon, known as frictional ignition, presents an ongoing challenge in the development of oxygen-compatible components for next-generation reusable rocket engines. Early NASA investigations on frictional ignition indicated that ignition-resistant materials developed a robust oxide tribolayer. Breakdown of this tribolayer was hypothesized to drive the onset of frictional ignition. While this early work ranked the relative ignition resistance of several engineering alloys, it lacked a physical explanation for the mechanisms of tribolayer breakdown and frictional ignition, limiting its utility in predicting ignition conditions and designing ignition-resistant components. This thesis addresses this knowledge gap through a combination of experiments and modeling, which reveal the fundamental mechanisms governing frictional ignition of metals. These insights are then developed into a quantitative framework to predict ignition conditions and identify safe operating limits for sliding systems in high-pressure oxygen environments. The present work considers frictional ignition in the context of thermal ignition theory. Using high-speed dry sliding experiments and finite element modeling, we establish two conditions that must be satisfied for frictional ignition. The first condition is tribolayer breakdown, which we confirm through in situ measurements of the friction coefficient and complementary thermochemical modeling. Three distinct tribolayer breakdown mechanisms are identified – oxide melting, substrate metal melting, and solid-state mechanical failure. The dominant breakdown mechanism is found to depend on alloy chemistry and operating conditions. The second condition for frictional ignition is satisfaction of the thermal ignition criterion for thermal runaway, i.e., when the oxidative heating rate exceeds the rate of heat loss. Numerical modeling shows that the tribolayer acts as a diffusion barrier that slows oxidative heating. Thermal runaway is only possible in the absence of this tribolayer once the temperature at the sliding surface exceeds a critical threshold. The critical temperature provides a conservative estimate for evaluating ignition risk, since below this temperature, ignition cannot occur. This framework is used to explain the ignition behaviors of all materials with available frictional ignition data, highlighting the exceptional ignition resistance of the Nibase alloy MA754. Experiments show that this behavior derives from the unique mechanical properties, microstructure, and growth kinetics of the MA754 tribolayer. The above framework is extended to assess the ignition behaviors of dry dissimilar metal sliding systems. Frictional ignition experiments revealed that tribolayers may form through oxidation of the parent metal or via material transfer between counter-surfaces, potentially resulting in tribolayers with disparate chemistry from the base alloy. The dynamics of material transfer depend on the contact geometry, operating conditions, and material-pair combination. We find that favorable material transfer may result in the formation of thick, lubricating, and protective oxide tribolayers on both surfaces that suppress ignition. We develop expressions to establish safe operating bounds for dissimilar contacts with different geometries – symmetric contacts and a pin-on-disk geometry. These expressions highlight the effects of material-pair combination and contact geometry on ignition conditions. This thesis concludes by providing materials selection and component design strategies for ignition-resistant tribosystems. The first strategy is to select or design materials with high thermal conductivity, low enthalpy of oxidation, and favorable oxidational wear behaviors, i.e., rapid formation of a refractory, delamination-resistant tribolayer that lubricates the rubbing surface and protects against oxidation. The second strategy is to enhance cooling by optimizing component design (e.g., contact geometry) or modifying operating conditions (e.g., working fluid). Collectively, this thesis provides a roadmap for designing ignition-resistant sliding components capable of withstanding extreme oxygen pressures, with direct application to the development of safer oxygen-compatible hardware for next-generation reusable rocket engines.

Personalizing Robot Assistance under Uncertainty about the Human

Mon, 01 Sep 2025 00:00:00 GMT

Personalizing Robot Assistance under Uncertainty about the Human Li, Shen Robots have the potential to enhance human well-being by assisting with daily activities, particularly for older adults and people with disabilities. One example is robot-assisted dressing, where a robot helps a person put on clothing. However, no two individuals are alike. Each person has unique preferences, behaviors, and needs, making personalization essential for effective assistance. A central challenge is that robots often operate under uncertainty about the human they are helping. This uncertainty may involve the person’s preferences, hidden physical states, or reactions to assistance. If not properly addressed, such uncertainty can lead to ineffective, undesired, or even unsafe outcomes. This thesis asks: How should a robot behave when it is uncertain about the human? To answer this, I present a unified framework for uncertainty-aware personalization in humanrobot interaction, spanning three core components of robot intelligence: preference learning, state estimation, and motion planning. I propose methods that (1) reduce uncertainty using implicit cognitive signals, (2) represent and respect uncertainty through set-based state estimation, and (3) act under uncertainty using relaxed safety constraints. First, I introduce an approach that uses response time, a subtle yet informative cognitive signal, as implicit feedback for preference learning. While traditional methods rely solely on binary choices, I developed the first algorithm that integrates both choices and response times to infer not just what a person prefers, but how strongly they feel about those preferences. Theoretical analysis reveals that response times significantly reduce uncertainty about user preferences, especially when users have strong preferences. In simulation studies, this method decreased misidentification of the most preferred option by up to 55 %, enabling faster and more accurate personalization without extra user input. Second, I address the problem of estimating hidden human states during physical interaction. For example, in dressing, parts of the body, such as the elbow, may be occluded. I introduce the first set-based estimator that represents and respects uncertainty from human behavior and sensing models trained on limited data. Instead of outputting a point estimate, the method constructs a geometric set, such as a 3D box, guaranteed with high probability to contain the true human state. In dressing experiments, the estimator achieved 92 % inclusion using significantly smaller boxes than prior methods, balancing reliability and precision, supporting safe and responsive physical assistance. Third, I consider how a robot should plan motion when it is uncertain about future human behavior. Traditional safety constraints typically prohibit any contact, which can cause the robot to freeze when uncertainty is high. I propose a more flexible definition of safety that allows either collision avoidance or low-impact contact. Integrated into a learning-based control framework, this approach enables efficient motion while maintaining safety. In dressing tasks, it reduced task time by 78 % without compromising safety. Together, these contributions show how robots can reduce, represent and respect, and act under uncertainty to personalize their assistance. This thesis lays a foundation for robots that not only respond to commands, but also understand and adapt to the nuanced, evolving, and uncertain nature of human behavior.

Interfacial Engineering of Perovskite Solar Cells

Mon, 01 Sep 2025 00:00:00 GMT

Interfacial Engineering of Perovskite Solar Cells Lu, Yongli The efficiency of the lead halide perovskite solar cells has improved from 3% up to above 27% in a decade. However, the long term stability of the device still need to be improved in order to compete with traditional photovoltaic technologies, such as Silicon and GaAs. Hybrid organic and inorganic interfaces in the devices are the origin of many degradation pathways. Understanding the nature of these interfaces and chemical and physical mechanism behind their evolution under electrical, light and thermal bias is the subject of this thesis. In the following chapters, I focus on developing a electron transport layer (ETL) based on chemical bath deposition (CBD) for the synthesis of a tin dioxide (SnO₂). The conventional CBD recipe uses thioglycolic acid (TGA) to facilitate attachments of SnOx particles onto the substrate. However, nonvolatile TGA is reported to harm the operational stability of PSCs. A volatile oxalic acid (OA) is introduced as an alternative to TGA. OA, a dicarboxylic acid, functions as a chemical linker for the nucleation and attachment of particles to the substrate in the chemical bath. Moreover, OA can be readily removed through thermal annealing followed by a mild H₂O₂ treatment, as shown by FTIR measurements. Synergistically, the mild H₂O₂ treatment selectively oxidizes the surface of the SnOₓ layer, minimizing nonradiative interface carrier recombination. EELS (electron-energy-loss spectroscopy) confirms that the SnOₓ surface is dominated by Sn⁴⁺, while the bulk is a mixture of Sn²⁺ and Sn⁴⁺. This rational design of a CBD SnOₓ layer leads to devices with T₈₅ = 1500h, a significant improvement over the TGA-based device with T₈₀ = 250h. The champion device reached a power conversion effciency of 24.6%. This work offers a rationale for optimizing the complex parameter space of CBD SnOₓ to achieve efficient and stable PSCs. In addition to developing a electron transport layer (ETL) based on chemical bath deposition (CBD) for the synthesis of a tin dioxide (SnO₂), a perovskite ink additive, bis(2- oxo-3-oxazolidinyl) phosphinic chloride (BOP-Cl), was developed with the following benefits: (1) The phosphoryl and two oxo groups form six-membered intermolecular hydrogen-bonded rings with the formamidinium cation (FA), mitigating ion migrations. (2) The hydrogen bonding reduces the electrophilicity of the ammonium protons by donating electron density, therefore reducing its reactivity with the surface oxygen on the metal oxide. Furthermore, the molecule can react to form a protecting group on the nucleophilic oxygen at the tin oxide transport layer surface through the elimination of chlorine. As a result, we achieve perovskite solar cells with an efficiency of 25.0% and improved MPP stability T₉₃ = 1200h at 40–45 °C compared to a control device (T₈₆ = 550h). In addition, we show a negative correlation between the strength of hydrogen bonding of different phosphine oxide derivatives to the organic cations and the degree of metastable behavior (e.g., initial burn-in) of the device.

Colloidal Semiconductor Nanocrystals: Tools For and Insights From First Principles Investigations

Mon, 01 Sep 2025 00:00:00 GMT

Colloidal Semiconductor Nanocrystals: Tools For and Insights From First Principles Investigations Alexander, Ezra A. Colloidal semiconductor nanocrystals, also known as quantum dots (QDs), are at once systems of great promise for diverse applications, systems with complex quantum physics that remain poorly understood at a fundamental level, and systems that are difficult to describe with conventional first principles computational chemistry methods due to their large size. Whereas QDs made from toxic materials like Cd and Pb are able to emit and absorb light efficiently and precisely, non-toxic alternative III-V QDs suffer from difficult to control defects that interfere with their radiative processes. Motivated primarily by the problem of understanding defects in III-V quantum dots, in this thesis we develop and apply new frameworks for the computational study of quantum dots. These frameworks include orbital localization techniques for de-convoluting ground-state band structures, a ∆SCF procedure for modeling the x-ray photoelectron spectra (XPS), and a low-cost machine learning framework for predicting Hamiltonians that can be used to extend molecular dynamics simulations. Through these frameworks, we reveal a more comprehensive picture of the defects that interfere with the optical performance of III-V quantum dots. We find that both three-coordinate indium and phosphorus can cause trap states in InP, explore how geometry and charge modulate trap depth, discover and explain a new source of four-coordinate trap states in InP and GaP, and assign shifts in P 2p XPS to specific III-V surface defects.

Cloud-chamber study of cosmic ray showers in lead plates

Sat, 01 Jan 1938 00:00:00 GMT

Cloud-chamber study of cosmic ray showers in lead plates Fussell, Lewis. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Electrical Engineering, 1938; Includes bibliographical references (leaves [113]-[118]).

Manipulation and measurement of charge transfer kinetics at chemically modified electrodes

Thu, 01 Jan 1981 00:00:00 GMT

Manipulation and measurement of charge transfer kinetics at chemically modified electrodes Lewis, Nathan S. (Nathan Saul) Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1981; Includes bibliographical references.

Higher Siegel-Weil formulae over function fields

Sun, 01 Feb 2026 00:00:00 GMT

Higher Siegel-Weil formulae over function fields Mkrtchyan, Mikayel In their seminal work, Feng-Yun-Zhang introduced function field analogues of Kudla-Rapoport cycles for moduli spaces of unitary shtukas, and initiated the study of their intersection theory. They proved a higher Siegel-Weil formula in the case of non-degenerate Fourier coefficients, relating the degrees of these cycles to higher derivatives of Siegel-Eisenstein series. In this thesis, we generalize their result in two directions: we 1) prove a higher Siegel-Weil formula for unitary groups for corank-1 degenerate coefficients, and 2) introduce analogous cycles on moduli spaces of symplectic shtukas, and prove a higher Siegel-Weil formula for such cycles in the non-degenerate case, relating their degrees to derivatives of Siegel-Eisenstein series on split orthogonal groups.

A Diverse Array of Synthetic Strategies for Phosphorus Group Transfer Chemistry: From Phosphinidenes to Phosphates

Sun, 01 Feb 2026 00:00:00 GMT

A Diverse Array of Synthetic Strategies for Phosphorus Group Transfer Chemistry: From Phosphinidenes to Phosphates Xin, Tiansi This thesis compiles the published scientific contributions of Tiansi Xin. Chapter 1 consists of a brief collection of eulogies from friends and colleagues, reflecting on his life and time at the Massachusetts Institute of Technology. The subsequent chapters describe the development of novel synthetic methods for the transfer of phosphorus-containing moieties, specifically metaphosphates and phosphinidenes. The work presented here has significant implications for both the fundamental understanding and practical advancement of synthetic inorganic and organic chemistry. Chapters 2 and 3 address the sustainable production and processing of phosphoruscontaining chemicals, focusing on mechanochemical methods to synthesize reduced phosphorus species while circumventing the need to access hazardous white phosphorus as an intermediate. In particular, Chapter 2 describes a solvent-free mechanochemical approach to producing phosphite (HPO₃²⁻) via hydride-mediated reduction of condensed phosphates. Using potassium hydride, a range of inorganic phosphate sources—including pyrophosphate, triphosphate, trimetaphosphate, fluorophosphate, and polyphosphate—were converted to phosphite in moderate to high yields. Mechanistic studies identified overreduction pathways leading to hypophosphite and other low-oxidation P-species. Chapter 3 similarly applies this mechanochemical approach to phosphorus–carbon bond formation, reporting the phosphorylation of acetylides with condensed phosphates to afford phosphonates. Biogenic polyphosphates were also shown to be viable precursors, a proof-of-concept to closing the modern phosphorus cycle using recycled inputs. These results demonstrate the possibility of accessing organophosphorus chemicals directly from condensed phosphates and may offer an opportunity toward a “greener” phosphorus industry. Chapters 4 and 5 shift focus to phosphinidene transfer chemistry and the synthesis of novel phosphorus-containing heterocycles. This expands on previously published studies from the Cummins group on the chemistry of dibenzo-7-phosphanorbornadiene “RPA” reagents. Chapter 4 reports the preparation and structural characterization of iron–phosphido complexes relevant to phosphinidene group transfer catalysis and describes the development of an improved catalytic system based on a simple diiron precursor (Fp₂), enabling efficient synthesis of phosphiranes from electron-deficient alkenes. The mechanism was thoroughly experimentally and computationally interrogated. Chapter 5 describes the novel synthesis of free, uncomplexed phosphet-2-ones via phosphinidene transfer to cyclopropenones, with experimental and theoretical studies supporting a mechanism involving ketene-derived intermediates and transformations to additional phosphorus heterocycles through subsequent reactions.

Structural geology of Eastern Massachusetts

Mon, 01 Jan 1934 00:00:00 GMT

Structural geology of Eastern Massachusetts Ilsley, Ralph, 1896- Thesis: Sc. D., Massachusetts Institute of Technology, Department of Geology, 1934; Vita.

Radiation transfer in massive binary x-ray systems

Tue, 01 Jan 1991 00:00:00 GMT

Radiation transfer in massive binary x-ray systems Lewis, Wayne Lloyd. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1991; Includes bibliographical references (leaves 167-173).

Mechanisms of Interaction Between Hydraulic and Natural Fractures in Shale Rocks

Mon, 01 Sep 2025 00:00:00 GMT

Mechanisms of Interaction Between Hydraulic and Natural Fractures in Shale Rocks Arzuaga García, Ignacio Martín Understanding the interaction between hydraulically induced fractures and pre-existing natural fractures in geologic formations is key for optimizing subsurface energy systems that rely on fluid injection into fractured rocks. These include Enhanced Geothermal Systems (EGS), CO₂ sequestration, hydrogen storage in depleted reservoirs, unconventional oil and gas development in shale formations, and nuclear waste disposal, among others. In all these applications, controlling fracture propagation and interaction is essential for ensuring operational efficiency, safety, and long-term integrity of the system. This thesis presents a comprehensive experimental and theoretical investigation of hydraulic fracture (HF) interactions with natural fractures (NFs), using Opalinus Clayshale as a representative anisotropic material. The experimental work involved a series of hydraulic fracturing tests on Opalinus Clayshale specimens under controlled quasi-true-triaxial stress conditions, comparing normal and dried states. Novel monitoring techniques, including high-resolution imaging, high-speed video, acoustic emissions (AE), and pressure tracking, were employed to capture the fracturing process in real-time. Three dominant interaction modes (Crossing, Arrest, and Opening) were systematically characterized and linked to key parameters, including stress ratio, fracture geometry, and injection rates. A critical stress ratio (σ₁/σ₃) of approximately 20 was identified as the threshold for achieving fracture crossing under our experimental conditions: cohesionless, “open” natural fractures, with a low viscosity injection fluid, in a toughness-dominated regime. In dried specimens, high flaw pressurization rates were necessary to overcome matrix fluid loss and achieve crossing. To complement and interpret the experimental results, existing theoretical models were reviewed and implemented. Furthermore, a simplified version of the OpenT model (Chuprakov et al., 2014) was developed and applied for Opalinus Clayshale, incorporating stress, energy, friction, and permeability effects. By integrating laboratory results with theoretical frameworks, this thesis offers an integral approach to predictive understanding of fracture propagation in naturally fractured rocks, stating that not only the characteristics of the discontinuity or the far-field stresses involved in the process are important in determining the mechanism of interaction, but also the dynamic energy balance at the fracture tip, which is influenced by injection rate, fluid viscosity, and discontinuity properties. Overall, this thesis bridges the gap between laboratory experiments and theoretical models, advancing a more comprehensive understanding of fracture propagation in naturally fractured media. The findings highlight the importance of considering both mechanical and hydraulic parameters, particularly in low-viscosity, toughness-dominated regimes, for accurately predicting fracture behavior.

Satellite Drag and Sustainable Space Operations in a Dynamic Thermosphere

Mon, 01 Sep 2025 00:00:00 GMT

Satellite Drag and Sustainable Space Operations in a Dynamic Thermosphere Parker, William E. Earth’s orbit has become increasingly congested and contested in recent years. The surge in launched payloads, combined with satellite failures, explosions, and collisions, has contributed to a large and growing population of orbital debris objects that can remain in orbit for decades, centuries, or longer. Meanwhile, decreasing launch costs and maturing satellite technology have created conditions favorable for rapid commercialization across orbital regimes, especially in low Earth orbit (LEO). Today, a small number of commercial entities operate the large majority of the world’s active satellites as part of proliferated LEO constellations. Sustaining productive activity in an increasingly crowded orbital environment has made satellite conjunction assessment and collision avoidance essential for safe operations. These efforts require not just accurate trajectory predictions, but also credible estimates of uncertainty. In LEO, variability in atmospheric drag is by far the dominant source of propagation error, often leading to deviations of several kilometers per day due to unpredictable solar and geomagnetic activity. Even over short timescales, trajectory prediction is challenging because existing forecasts exhibit limited predictive skill. Although forecast errors are often non-Gaussian and heteroscedastic, operational products are generally presented as deterministic, and atmospheric models rarely provide rigorous uncertainty characterization. This work introduces a new approach for probabilistic satellite drag modeling based on historical correlations between space weather drivers and satellite dynamics. Unlike traditional methods, it models satellite behavior directly without reconstructing thermospheric mass density or requiring detailed knowledge of satellite properties such as the ballistic coefficient. This end-to-end strategy offers substantial computational and operational advantages for many space domain awareness tasks. Capturing both trajectory predictions and their associated uncertainty is critical for enabling informed collision avoidance decisions, particularly during geomagnetic storms when current infrastructure frequently fails. Because the orbital lifetime of debris objects can exceed hundreds of years, population dynamics in space critically depend on long-term variability in the composition of Earth’s thermosphere. Rising concentrations of carbon dioxide and other greenhouse gases have caused warming in the troposphere but cooling and contraction in the upper atmosphere. This contraction decreases atmospheric density in LEO, reducing drag and extending the orbital lifetime of debris objects. Longer-lived debris populations pose a persistent collision hazard for all active satellites as long as they remain in orbit. Even natural events, such as a prolonged grand solar minimum, could further reduce thermospheric density and contribute to longer debris lifetime in LEO. With little ability to predict such an event, it is necessary to understand the potential consequences and to identify strategies that enable the continued safe and productive use of LEO. This work models the impact of such long-term environmental changes on limits for sustainable satellite deployments. LEO is a finite respource increasingly at risk of overexploitation. Conserving it and sharing it fairly requires that we first understand its fundamental capacity and our current occupation of that capacity. Some metrics have been proposed to measure the satellite carrying capacity of Earth’s orbit, but none have previously accounted for the potential influence of a changing space climate. This work develops new methods for defining carrying capacity as a common currency, enabling clear constraint-driven thresholds on activity and a better understanding of how existing and proposed missions consume available capacity. These new metrics provide insight into how environmental variability may affect the long-term sustainability of operations in LEO. Respecting and understanding this influence that the natural environment has on our collective ability to operate spacecraft in LEO is critical to preventing the overexploitation of this regime and protecting it for future generations.

Essays in Financial Economics and Econometrics

Mon, 01 Sep 2025 00:00:00 GMT

Essays in Financial Economics and Econometrics Orestes, Victor M. This thesis comprises three essays in finance and econometrics. The first two essays focus on the role of credit access and liquidity in shaping real firm outcomes. The first essay examines the transmission of modern monetary policy through corporate asset markets. Exploiting quasi-experimental variation in the Central Bank of Brazil’s collateral framework and implementing a novel dynamic regression discontinuity design, it shows that monetary policy can ease expected future borrowing constraints, reduce firms’ precautionary cash holdings, and stimulate employment. The second essay analyzes how receivables financing through factoring helps firms smooth cash flows. Using a shift-share instrument and matched administrative data, it finds that cheaper liquidity leads firms to rely more on permanent labor. The third essay develops a new method for distributional inference—nonparametric quantile mixture models. This framework can be applied to financial settings such as tail risk estimation and density forecasting, as well as to causal inference when the objective is to estimate the distributional effects of interventions. It is used here to quantify the heterogeneous wage effects of a major environmental disaster. The first chapter (joint with Luis Alvarez and Thiago Christiano Silva) studies how modern monetary policy tools, which increasingly operate through corporate asset markets, affect real firm outcomes. We exploit quasi-experimental variation from the inclusion of specific corporate debt instruments in the Central Bank of Brazil’s collateral framework and implement a novel dynamic regression discontinuity design. We find that eligibility increases firms’ debt issuance, modestly lowers spreads, and reduces cash holdings, reflecting a decline in precautionary savings. These effects translate into higher employment and greater supply chain liquidity. We interpret the mechanism through the lens of segmented financial markets: by relaxing firms’ expected future borrowing constraints, the policy acts as a persistent borrowing subsidy and liquidity injection. This encourages firms to reduce cash hoarding and expand production. Using a semi-structural framework calibrated to our reduced-form estimates, we find that an induced 0.8% borrowing subsidy leads to a 1% increase in debt issuance, a 0.2% reduction in cash holdings, and a 0.4% increase in the wage bill. The second chapter (joint with Thiago Christiano Silva and Henry Zhang) shows that firms experience large increases in sales and purchases after receiving cheaper liquidity. We focus on factoring, defined as the supplier-initiated sale of receivables. In Brazil, receivables funds (FIDCs) securitize receivables for institutional investors. By assembling a novel transaction-level dataset of factoring with other credit operations for all registered firms and FIDCs, we construct a shift-share instrument for factoring financing supply based on FIDC flows. We then use a novel combination of electronic payments, trade credit, and employer-employee matched data to estimate the impacts. A flow-induced increase in receivables demand reduces firms’ factoring interest rate. In response, firms demand more permanent labor and less temporary labor. In our model, these effects arise from factoring’s purpose of reducing cash inflow volatility, helping firms match inflows to outflows, which firms otherwise achieve at an efficiency cost through substitution across labor types. The third chapter (joint with Luis Alvarez) introduces nonparametric quantile mixture models as a computationally convenient and flexible alternative to standard nonparametric density mixtures, which are widely used in Statistics and Econometrics but face significant computational and inferential challenges. We propose a sieve estimator based on a generalized method of L-moments and develop a full inferential theory. In doing so, we contribute to the statistical literature by extending a numerical bootstrap method to high-dimensional settings. As a direct application of our theory, we provide the first inference method for the distributional synthetic controls of Gunsilius (2023), a novel tool for counterfactual analysis that previously lacked formal inference procedures. We apply this method to evaluate the effects of the Brumadinho dam collapse—a large-scale environmental disaster—on the local wage distribution. The results reveal substantial heterogeneity across the distribution, with evidence of displacement effects in which median-paying jobs are replaced by lower-wage contracts. JEL Codes: C1, E4, E5, G2, G3

Signaling at the Tumor-Immune Interface in Glioblastoma

Mon, 01 Sep 2025 00:00:00 GMT

Signaling at the Tumor-Immune Interface in Glioblastoma D'Souza, Alicia D. Glioblastoma (GBM) is a devastating brain cancer, and the standard of care has not changed in over 20 years. GBM tumors are composed of a milieu of cancer cells and innate immune cells, which are co-opted by the cancer cells to promote an anti-inflammatory environment. Despite tremendous success in immunotherapy in several cancers over the past 10 years, immunotherapies have failed to show efficacy in GBM. A systems biology approach to characterizing temporal changes in tumor-immune interface of glioblastoma could illuminate new strategies to activate an anti-tumor immune response by examining changes in cell signaling and antigen presentation. In the first part of my thesis, I investigated how macrophages alter their phenotype in response to tumor co-culture and how these changes are reflected at the level of the phosphoproteome. To characterize signaling changes in distinct cell populations during co-culture, I developed a method to preserve and analyze cell-type-specific signaling using fixation. This approach enables phosphoproteomic profiling of two interacting cell types, capturing dynamic signaling events with cell-type resolution. I applied this method to study co-cultures of glioblastoma (GBM) cells and primary human macrophages. When cultured together, GBM cells induced an anti-inflammatory, immunosuppressive phenotype in macrophages, mirroring features observed in the glioblastoma tumor microenvironment. Phosphoproteomic analysis revealed that this phenotypic shift was accompanied by distinct signaling alterations in macrophages, including the upregulation of ABL kinase activity. To test this finding, I treated macrophages with an ABL kinase inhibitor and observed a reduction in the anti-inflammatory phenotype, suggesting that ABL signaling plays a role in supporting immunosuppressive macrophage polarization. Furthermore, in a mouse model of GBM, treatment with an ABL kinase inhibitor led to a reduction in the abundance of anti-inflammatory macrophages within the tumor and was associated with a modest extension of survival. In the second part, I examined changes in antigen presentation and signaling in glioblastoma tumors in response to treatment with an oncolytic virus (OV). In patient derived tumor (PDX) models in mice, mice treated with OV have increased antigen presentation, pointing to the use of OV therapy to reshape the tumor micro-environment to a more inflammatory state. Finally, tissue obtained from serial biopsies of GBM patients treated with OV shows an increase in antigen presentation and both Class I and Class II MHC protein expression. We also observed an increase in interferon alpha and interferon gamma signaling pathways as well as early induction of apoptotic pathways. These findings highlight the role of therapeutics in altering the tumor microenvironment and potentially priming it for combination immunotherapies. This thesis explores the dynamic nature of the tumor and immune compartments in glioblastoma and underscores how therapies can act on the immune compartment to promote anti-tumor activity.

Intercellular flow-mediated force relaxation measurement on the three-dimensional multicellular tissue

Wed, 01 May 2024 00:00:00 GMT

Intercellular flow-mediated force relaxation measurement on the three-dimensional multicellular tissue Liu, Fan Three-dimensional (3D) multicellular tissues are prevailing over 2D monolayer or single cells; their mechanical properties like stiffness, surface tension, and viscosity have been shown to relate to diseases like fibrosis or tumor metastasis. Multicellular tissues have been traditionally modeled as a viscoelastic material due to their apparent shape rearrangement, which hardly considers the internal structure, including the extracellular matrix (ECM) and resulting intercellular water flow. These intercellular communications usually provide significant information on diseases such as tumor invasion, but immediate supporting evidence of this behavior is lacking. In this work, we investigate the bulk response of 3D multicellular tissues due to such intercellular flows and explore the related mechanism through a tailored micro-mechanics platform. Firstly, we design and establish a micro-mechanics platform based on the parallel plate compression (PPC) method. We adopt a precise micro-balance as the sensor to detect the force variation of the sample during compression. A piezo linear stage is incorporated to exert such tiny vertical displacement. Besides, a lateral microscope is designed to monitor the compression process instantaneously. This platform has proved to be applicable to various samples, including hydrogels, cell spheroids, and natural tissues or organs. Then, we propose the critical criterion, the size dependency of force relaxation time, to distinguish a material's properties, i.e., viscoelasticity and poroelasticity. For poroelastic material, the force relaxation is due to water redistribution; hence, the speed highly depends on the sample sizes. In contrast, for viscoelastic material, it is determined by the bulk material properties, thus independent of the size. We theoretically verify this criterion via Abaqus simulation and experimentally on classic poro-/visco-elastic materials with various dimensions. Next, we apply the size-dependency criterion on the 3D multicellular tissues to distinguish the poro-/visco-elasticity in this biomaterial. We take the PPC on multiple cell spheroids with different sizes through the platform. It is observed that the force relaxation times are linearly proportional to the size of all tested cell lines, demonstrating poroelasticity in our experimental time range. Intriguingly, we take tests on the natural organs of the mouse islets and find such linear correlation as well. Hence, both cultured spheroids and natural tissues are poroelastic. Finally, we explore the mechanism determining the poroelasticity inside the 3D multicellular tissues. By inhibiting the cell-cell junctions, we demonstrate the intercellular water flow through the extracellular gaps dominates this poroelastic force relaxation in the biomaterial. Further experiments show that the stiffness of the structure and the extracellular gaps inside the 3D multicellular tissues couple to contribute to the intercellular water flow, i.e., the stiffer the structure and/or the larger the gaps, the faster the water flows, thus quicker the force decays after compression. These findings highlight the fundamental role of intercellular water flow in the mechanical properties of 3D multicellular tissues. The designed micro-mechanics platform is also beneficial to research at the tissue level with micro-newton forces owing to the development of artificial organoids for early disease diagnosis and treatment.

Language Comprehension, Production, and Reasoning in Humans and Neural Language Models

Mon, 01 Sep 2025 00:00:00 GMT

Language Comprehension, Production, and Reasoning in Humans and Neural Language Models Eisape, Tiwalayo How closely do neural language models mirror human language processing, and what can this alignment teach us about cognition? This dissertation presents convergent evidence in comprehension, production, and reasoning that neural language models (LMs) can serve as productive instruments for understanding naturalistic human language use at scale. Studies 1-2 examine comprehension with complementary methods. First, Cloze Distillation—a novel method for aligning models with human next-word predictions—improves both language modeling and reading time prediction, demonstrating that LMs and humans make distinct, complementary predictions. Second, new methods for identifying syntactic information in LM hidden states demonstrate that models learn to implicitly represent incremental syntactic state. These probes also enable targeted interventions, allowing us to manipulate representations to resolve (or induce) temporary misinterpretations, confirming mechanistic understanding. While these studies demonstrate prediction’s role in comprehension, a complete account requires examining whether these mechanisms also shape how humans produce language in real-time. Study 3 analyzes a massive corpus of 2.3 million competitive typing events from TypeRacer.com, uncovering the first evidence of in-context predictability effects in this domain of production. Finally, Study 4 compares human and LM reasoning systematically—LMs achieve higher syllogistic reasoning accuracy than humans while still replicating several fine-grained human-like error patterns that are orthogonal to logical accuracy, including premise ordering effects. These converging findings reveal prediction as a fundamental mechanism in comprehension, production, and reasoning in both humans and LMs. While models achieve this through statistical learning rather than specialized cognitive architecture—often outperforming humans yet replicating their systematic biases—this alignment supports predictive processing theories of cognition. This work establishes LMs as scalable cognitive laboratories that can complement traditional experiments, and contributes psycholinguistically principled methods for understanding and controlling LMs.

Product architectures for solar-powered drip irrigation (SPDI) systems in the Middle East and North Africa

Mon, 01 Sep 2025 00:00:00 GMT

Product architectures for solar-powered drip irrigation (SPDI) systems in the Middle East and North Africa Grant, Fiona R. To feed the growing global population, agriculture production must be intensified using existing land and resources. Sustainable agriculture intensification is particularly important in the Middle East and North Africa (MENA), the most water-stressed region in the world. Solar-powered drip irrigation (SPDI) has the potential to increase water use efficiency and reduce fossil fuel use for irrigation. Despite these benefits, SPDI adoption is limited by its high investment cost and the misalignment between farmers' risk tolerance and broader sustainability goals. Past work has explored three areas of SPDI innovation: low-pressure drip emitters, system cost optimization, and precision irrigation control. This thesis integrates previous innovations in an end-to-end design process to generate SPDI architectures that are accessible to resource-constrained farmers. A market study was conducted to understand farmers' priorities and constraints and articulate SPDI value propositions for the target users. Stakeholder surveys were conducted in Jordan and Morocco for farms ranging from 1–130 hectares. Three market segments were identified, grouping farmers who face similar economic and knowledge barriers. While farmers generally prioritized irrigation reliability and low system costs, the observed variety in farm size, production volume, and technical expertise suggested that SPDI architectures must be tailored to each market segment. This thesis proposes an energetic framework that captures system parametric relationships to identify feasible SPDI design trade-offs. The optimized solar power systems were 14%–80% less expensive than conventionally-sized designs. Despite significant changes to the hydraulic operating parameters, the proposed SPDI architectures were as reliable as existing systems. For farms with long irrigation times, it was optimal to pair low-pressure drip emitters with an irrigation schedule that tracks the daily solar profile, termed “solar profile matching” (SPM), to maximize direct solar power use. The SPM schedule reduced system cost by minimizing the battery capacity. An economic analysis demonstrated that the optimal SPDI designs could be made cost-competitive with grid power through SPDI retrofit subsidies, which some local governments already support. Researchers and industry professionals could use the energetic framework and techno-economic analysis presented in this thesis to inform system design and policy decisions and promote SPDI adoption. Finally, this work created guidelines for designing a precision irrigation controller in resource-constrained markets. A controller was conceptualized to implement the SPDI-SPM architecture. The controller functional requirements and design specifications were iteratively defined with stakeholders, and a prototype was tested on two farms in the MENA region. The controller reduced water and energy use by up to 44% and 43%, respectively, while maintaining crop yield. However, the controller relied on battery power to execute the irrigation schedule. A yield loss sensitivity analysis found that using 72%–79% of the available solar energy on average, an increase of about 40% from the experiment SPM schedules, would have been sufficient to reliably irrigate with solar alone. The results suggest that, with software modifications, the proposed controller could eliminate the need for a battery and enable low-cost SPDI systems. If adopted, the proposed controller could make sustainable irrigation practices more accessible to farmers.

Topics in Geometric Machine Learning

Mon, 01 Sep 2025 00:00:00 GMT

Topics in Geometric Machine Learning Tahmasebi, Behrooz Recent advances and the widespread adoption of neural networks have revolutionized machine learning and artificial intelligence. These developments demand learning paradigms capable of processing data from diverse applications and sources. In structured domains such as molecules, graphs, sets, and 3D objects, as well as fields such as drug discovery, materials science, and astronomy, models must account for data structures. The emerging field of geometric machine learning has gained attention for enabling neural networks to handle geometric structures, unlocking novel solutions across scientific disciplines. Despite recent advances, theoretical gaps remain. This thesis aims to address these gaps by studying the benefits and limitations of leveraging geometric structures and symmetries in data. We explore sample complexity, generalization bounds, hypothesis testing for the presence of symmetries in data, time complexity of learning under symmetries, and regularization and optimization in symmetric settings. The goal is to build a robust theoretical framework that validates recent successes and sheds light on unexplored aspects, fostering future progress in geometric machine learning.

Chemical exposures in drinking water: contaminant analysis and physicochemical behavior

Mon, 01 Sep 2025 00:00:00 GMT

Chemical exposures in drinking water: contaminant analysis and physicochemical behavior Bugher, Nicolette A. Environmental chemical exposures pose an understudied risk to human health. The quality and accessibility of data on occurrence in the environment and physicochemical behavior of industrial chemicals are integral for accurate exposure risk assessment. In this dissertation, analytical chemistry techniques were developed and leveraged to characterize human exposures to contaminants in drinking water and improve methods for assessing such risks. The occurrence of organic industrial pollutants in domestic well waters was investigated, with a particular focus on the impacts of region-specific industrial activity (e.g., hydraulic fracturing), legacy pollution sites (e.g., Superfund sites), and geochemistry. The exposure risk to water contaminants of domestic well users was further interrogated by evaluating trends in contaminant concentrations resulting from the implementation and maintenance of in-home water treatment devices. The results show widespread, low-dose mixtures of organic pollutants, where the efficacy of removal by in-home water treatment varied by water contaminant class and maintenance frequency. Additionally, analytical methods were optimized to quantify a group of organic water contaminants (i.e., probable carcinogens, N-nitrosamines), improving method sensitivity and critically identifying false-positive interferences. Finally, methods were evaluated and deployed for the determination of physicochemical properties of N-nitrosamines. The results of which demonstrate gaps in existing experimental data, provide a valuable methodological intercomparison (two experimental and two computational approaches), and contribute novel partitioning data. This dissertation addresses gaps in occurrence data, analytical method sensitivity, and reliability of physicochemical parameters for risk assessment. The combination of method development and implementation enables the study of exposures to water contaminant mixtures at health-relevant concentrations, representative of prevalent exposure pathways.

Optimizing Data Layouts for Evolving Cloud Table Storage

Mon, 01 Sep 2025 00:00:00 GMT

Optimizing Data Layouts for Evolving Cloud Table Storage Sudhir, Sivaprasad Modern data analytics platforms increasingly adopt disaggregated architectures, storing data in cost-effective cloud object stores. While this approach enables a clean separation of concerns, allowing each layer to be independently managed and scaled, it introduces significant performance bottlenecks due to expensive data movement. Effective data layouts, which organize data to minimize unnecessary data reads, are thus critical to achieving high query performance. However, existing techniques typically rely on manually specified layouts, collect limited metadata, or lack mechanisms to dynamically adapt to changing data and workloads. This thesis investigates adaptive, metadata-rich, expressive data layouts for cloud table storage. First, we introduce Pando, a correlation-aware layout technique that leverages rich metadata on query predicates to significantly improve data skipping. Next, we propose CopyRight, a partial replication strategy that selectively replicates subsets of data and optimizes each replica differently, efficiently serving heterogeneous query patterns. Finally, we describe Self-Organizing Data Containers (SDCs), a practical table storage layer for the cloud that incrementally reorganizes complex data layouts based on changes in data and workload distributions.

Deposition and characterization of very low pressure CVD silicon/silicon-germanium heteroepitaxial structures

Wed, 01 Jan 1992 00:00:00 GMT

Deposition and characterization of very low pressure CVD silicon/silicon-germanium heteroepitaxial structures Tsai, Curtis. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1992; Includes bibliographical references (leaves 135-146).

An experimental study of the law of parity conservation in electromagnetic interactions.

Fri, 01 Jan 1965 00:00:00 GMT

An experimental study of the law of parity conservation in electromagnetic interactions. Hegblom, Edwin Richard. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1965

Rules for ring closure and aspects of organolithium chemistry

Tue, 01 Jan 1980 00:00:00 GMT

Rules for ring closure and aspects of organolithium chemistry Dupont, William Alan. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1980; Vita.; Includes bibliographical references.

Weak Identification and Network Measurement Error in Peer Effects Estimation

Mon, 01 Sep 2025 00:00:00 GMT

Weak Identification and Network Measurement Error in Peer Effects Estimation Wang, William Wei The growing availability of social network data has enabled a surge of research on social interactions. In particular, peer effects, once considered unidentifiable, have now been shown to be identified given knowledge of the network structure. Despite this positive result, questions remain about the existence and nature of peer effects, due to concerns about identification strength and the reliability of network data. This work investigates two key threats to the estimation of peer effects: weak identification and network measurement error. We show that weak instrument problems arise in moderately dense networks due to rapid averaging, leading to slow convergence rates even when estimators remain consistent. On the measurement error side, we show that additive edge weight errors can be mitigated in such networks due to the same averaging phenomena, but the error remains a relevant threat to consistency in sparser networks. We further demonstrate that when both issues are present, the resulting estimators exhibit non-vanishing bias, suggesting that the combined effect of weak instruments and measurement error can be more severe than either problem in isolation. Overall, our results aim to clarify how these non-standard estimation challenges impact our ability to study peer effects using network data.

Seeing Beyond Limits with Physics-Informed Priors

Mon, 01 Sep 2025 00:00:00 GMT

Seeing Beyond Limits with Physics-Informed Priors Liu, Yang Conventional imaging systems are limited by dimensionality and visibility: standard sensors capture only two-dimensional data, while light diffuses or scatters across surfaces and through complex media. This dissertation reformulates imaging as an interplay of optical encoding and neural decoding. It models forward physical processes and iteratively refines them using deep denoisers. By embedding physics-informed priors into this optimization, it aims to surpass conventional limits in dimensionality and visibility. First, I develop Privacy Dual Imaging using an ambient light sensor. This approach tackles both dimensionality and visibility challenges when imaging with a single-point, non-imaging component on smart devices. Inspired by 1984’s “Big Brother” telescreen, I demonstrate how subtle light intensity fluctuations can reveal unseen image information; however, the goal is to highlight privacy concerns, not exploit them. It addresses two visibility limits—pixel-less and lens-less imaging—by using the screen as a spatial modulator and exploiting involuntary motion to create a virtual pinhole effect. A quantized, physics-informed prior improves reconstruction from heavily quantized sensor measurements. Second, I propose Snapshot Compressive Imaging (SCI) augmented with deep plug-and-play physics-informed priors to overcome the dimensionality limit of 2D sensors. SCI compressively encodes multiple temporal, spectral, or angular frames into a single measurement. A deep plug-and-play prior algorithm introduces high-dimensional priors learned from images and videos into the iterative reconstruction process, improving fidelity, speed, and flexibility. Experiments show notable gains in reconstruction quality and efficiency across different SCI datasets, including largeformat 4K UHD scenarios. Third, I introduce Rank-Reduced physics-informed priors, showing that large pretrained AI models—especially diffusion models—can act as general visual priors across both dimensionality and visibility challenges. A relax-then-tighten strategy handles ill-conditioning by applying truncated singular value decomposition to reduce rank deficiencies, followed by a Stable Diffusion refiner (SDEdit) plug-and-play prior that constrains reconstructions to valid image spaces. Simulations and passive non-line-of-sight imaging experiments verify the approach’s stability and effectiveness. Physics-informed priors promise to extend the boundaries of imaging, enabling us to see beyond current dimensionality and visibility limits and to unlock new applications from macro-scale to micro-scale observations.

Foundational Abstractions for Quantum Programming

Mon, 01 Sep 2025 00:00:00 GMT

Foundational Abstractions for Quantum Programming Yuan, Charles Bringing the promise of quantum computation into reality requires not only building a quantum computer but also correctly programming it to run a quantum algorithm. To obtain asymptotic advantage over classical algorithms for applications including simulation, search, and optimization, quantum algorithms rely on the ability of data in quantum superposition to exhibit phenomena such as interference and entanglement. In turn, an implementation of the algorithm as a program must correctly orchestrate these phenomena in the states of qubits. Otherwise, it would yield incorrect outputs or lose quantum computational advantage. Given a quantum algorithm, what are the challenges and costs of realizing it as a program that can run on a physical quantum computer? In this thesis, I answer this question by showing how the basic abstractions of programming upon which many quantum algorithms rely – such as data structures and control flow – can fail to work correctly or efficiently on a quantum computer. I then demonstrate how we can leverage insights from research in programming languages to re-invent the software stack – including abstractions, libraries, and compilers – to meet the demands of quantum algorithms. This approach holds out a promise of expressive and efficient tools to program a quantum computer and thereby practically realize its computational advantage.

Learning Simple Chemical Heuristics to Model and Discover Materials

Mon, 01 Sep 2025 00:00:00 GMT

Learning Simple Chemical Heuristics to Model and Discover Materials Ma, Andrew Computational approaches have long played an important role in the field of materials science, driving both the scientific study of materials’ fundamental properties and the design of materials for technological applications. Currently, mainstream methods in computational materials science typically rely on either first-principles calculations or deep learning models. In this thesis, we take a different direction by developing remarkably simple data-driven models for predicting fundamental properties of materials, including electronic topology, metallicity, and band gap. These models take the form of highly interpretable chemical heuristics. A key finding of this work is the surprising result that electronic topology diagnosis – often regarded as a highly complex task – can, in fact, be performed heuristically using a simple and intuitive model. We further integrate this model into a workflow for discovering new topological materials. Altogether, this work revisits the classic idea of chemical heuristics through a modern data-driven lens, shedding new light on fundamental problems in materials science.

Language Modeling from Visually Grounded Speech

Mon, 01 Sep 2025 00:00:00 GMT

Language Modeling from Visually Grounded Speech Lai, Cheng-I Jeff Recent advancements in spoken language processing have significantly reduced automatic speech recognition (ASR) error rates, driven by large-scale supervised training on paired speech–text data and, more recently, self-supervised pre-training on unpaired speech and audio. These methods have facilitated robust transfer learning across diverse speech and audio tasks. However, fully leveraging multimodal inputs, particularly visual context, remains underexplored. This thesis addresses this gap by developing novel language modeling techniques directly from visually grounded speech. We first introduce the Audio-Visual Neural Syntax Learner (AV-NSL), an unsupervised parser that recovers constituency trees directly from raw speech paired with images, demonstrating how visual context effectively bootstraps grammar induction without textual supervision. Next, we investigate Audio-Visual Word Discovery for Speech Translation, using the Fisher Spanish–English corpus to train a series of speech-to-speech translation models based on pseudo-word units discovered via audio-visual grounding. This study highlights that simplistic acoustic tokens and limited training data degrade re-synthesis and translation quality, underscoring two crucial missing ingredients: richer semantic tokens and large-scale training. Guided by these insights, we present Audio-Visual Gemma (AV-Gemma), a family of multimodal foundation models that condition jointly on images and learned semantic speech tokens. At scale, AV-Gemma generates visually coherent spoken captions and transfers robustly to tasks such as video-to-speech generation and spoken visual question answering, significantly advancing multimodal spoken-language processing.

Scalable Assembly of General Objects

Mon, 01 Sep 2025 00:00:00 GMT

Scalable Assembly of General Objects Tian, Yunsheng In this thesis, I present a scalable system towards fully automated and flexible robotic assembly that generalizes over diverse geometries and complex structures. Most real-world objects are assemblies composed of multiple parts. Assembly presents significant challenges for robots to execute long-horizon, contact-rich manipulation with both reliability and generalization. However, most manufacturing facilities today still rely heavily on manually programmed assembly lines, which require significant labor, time, and setup costs yet offer no flexibility to object variations. My proposed system synergizes global multi-step planning with local reactive learning-based control to enable generalizable and precise assembly. Such an integrated paradigm effectively leverages the best of both worlds, accomplishing results that neither planning nor learning could achieve alone. For planning, I leverage guidance from physical simulation and learned feasibility networks to efficiently search for part sequences, precise motions, and stable grasps for dual-arm robots over long horizons. For learningbased control, I train robust policies via reinforcement learning for submillimeter-level insertion across different part geometries, assembly paths, and grasp poses. I introduce and open-source the largest-scale assembly dataset to date and demonstrate my system’s generalization on thousands of simulated assemblies as well as through end-to-end real robot experiments. By integrating planning and learning, I showcase the first system to achieve complete and generalizable real-world multi-part assembly without domain knowledge or human demonstrations. Although the system plans and learns purely in simulation, it transfers zero-shot to the real world and achieves 80% successful steps. Finally, I will share insights that further scale up robotic assembly and opportunities to extend to general manipulation, and discuss future directions to equip general-purpose robots with multi-step, precise manipulation capabilities.

Learning to Tackle Task Variations in Control - A Transportation Context

Mon, 01 Sep 2025 00:00:00 GMT

Learning to Tackle Task Variations in Control - A Transportation Context Jayawardana, Vindula Muthushan Real-world control tasks are messy and often exhibit task variations. Practical solutions to these problems must exhibit generalization across task variations. For example, in the task of controlling traffic signals, control strategies must adapt to different intersection topologies (the variations), each with distinct dynamics. In this thesis, we consider the challenge of coping with task variations in the context of transportation problems, specifically in roadway interventions where many such variations are both common and imperative to handle. We develop machine learning techniques to address three key challenges: 1) quantify the impact of task variations in control, 2) model them to align with the real world, and 3) optimize in the presence of them. To this end, we begin with a large-scale case study of cooperative eco-driving and illustrate how explicitly modeling task variations can surface otherwise overlooked insights. Building on this, we argue for the necessity of formally incorporating task variations into problem specifications, emphasizing that task underspecification due to loosely defined task variations can severely impair decision-making. We then introduce a contextual reinforcement learning algorithm capable of leveraging the structure of task variations to generalize effectively in cooperative eco-driving with autonomous vehicles. We also present IntersectionZoo, a benchmark designed to promote the development of learning algorithms that generalize by exploiting task variation structures, thus standardizing progress in the field. Last, we explore task variation modeling through a generative modeling lens, using human driver behavior modeling as a case study. Overall, this thesis lays the groundwork for robust control methods by leveraging machine learning to tackle task variations, specifically in roadway intervention designs.

Modern methods for causal inference and missing data

Mon, 01 Sep 2025 00:00:00 GMT

Modern methods for causal inference and missing data Xia, Eric The proliferation of data-driven approaches in a wide array of settings is one of the defining characteristic of the modern era. With this rise, there has been much focus on using data to answer causal questions, e.g. whether A causes a change in B. Furthermore aspects of data collection has given rise to datasets that are often quite messy, sometimes missing important entries. These are both problems that are incredibly relevant to practitioners in a variety of disciplines, including policy-makers looking to make critical decisions that can influence lives of many. On the surface these problems seem quite distinct, yet the literature has highlighted deep connections between these two settings. Indeed, many methods for addressing one question can often be repurposed to address the other. These two settings are quite classical and approaches to address the are still quite so, but there has been great interest recently to develop techniques and algorithms to address them that harness modern developments in statistics and machine learning. This thesis contributes to the literature by providing new methods as well as novel understandings of existing ones.

Steering Vision at Scale: From the Model Weights to Training Data

Mon, 01 Sep 2025 00:00:00 GMT

Steering Vision at Scale: From the Model Weights to Training Data Materzyńska, Joanna We study the interpretability and controllability of multimodal and generative models, with a particular focus on text–image representation models and text-to-image diffusion systems. We begin by addressing limitations in CLIP’s multimodal embeddings, specifically the entanglement between visual and textual concepts within images. We demonstrate the consequences of this entanglement in both generative and discriminative tasks, and introduce a method for disentangling visual and textual representations. We showcase the utility of these disentangled embeddings in typographic attack resistance, improved image generation, and robust out-of-domain OCR detection. Building on this foundation, we explore methods to enhance the controllability of diffusion models. First, we tackle the challenge of unwanted concept generation. We introduce a technique to remove specific visual concepts using only their names, leveraging negative prompts and guidance to suppress target content without modifying training data or requiring model retraining. This approach enhances ethical alignment and enables greater user control in generative systems. We then turn to the complementary problem: incorporating new concepts. We present a few-shot motion customization technique for video generation models, which transfers motion patterns from a small set of examples to novel subjects. This method maintains the generalization capabilities of the base model while enabling consistent, subject-agnostic animation that preserves both identity and temporal coherence. To improve the fine-grained control of visual outputs, we propose a method for continuous manipulation of image attributes. This framework introduces smooth, intuitive controls, that allow for dynamic, continuous steering of generated images. Unlike prompt engineering or token-level interventions, our approach offers real-time adjustment without sacrificing output realism. Finally, we examine whether artistic styles in diffusion models require large-scale pretraining or can be learned in a lightweight, post-training manner. To this end, we train a base model on art-free data and introduce a compact adapter method that learns stylistic concepts from a small set of exemplar artworks. Our findings suggest that artistic domains can be integrated efficiently and ethically, without reliance on web-scale scraped datasets.

How Data Drives ML Models Performance

Mon, 01 Sep 2025 00:00:00 GMT

How Data Drives ML Models Performance Khaddaj, Alaa Data has been been playing an increasingly more important role in the machine learning (ML) pipeline. This thesis deepens the understanding of the effect of the data on model performance and reliability. First, we study how choice of training data affects model performance. We consider a transfer learning setting and present a framework for selecting from a large pool of data a pretraining subset that improves model performance on downstream tasks. Our approach, however, requires training multiple target models which becomes prohibitively expensive at large-scale. To that end, we explore using smaller—and cheaper—proxy models to approximate large model behavior and select the pretraining data using that cheaper model. We show the effectiveness of this approach in two dataset selection settings: language modeling and imitation learning. Second, we explore the role of data in model reliability and consider two different threat models: backdoor attacks and malicious data editing. In this first threat model, an adversary injects a few doctered samples into the training set to control model predictions at inference time. We study the effect of these malicious samples on model behavior and then propose a framework for detecting and removing them from the training data. In the second threat model, an adversary leverages generative models such as diffusion models to maliciously modify personal data and generate harmful digital content. We focus on image editing and investigate how we can imperceptibly modify personal images to mitigate editing using diffusion models and raise and the cost of hamrful content generation. Overall, this thesis contributes to the understanding of the role of the data in driving model behavior. Through these efforts, we aim to provide mechanisms for (i) training models that perform better and (ii) are more reliable when deployed in the real world.

Understanding and Overcoming Optimization Barriers in Non-convex and Non-smooth Machine Learning

Mon, 01 Sep 2025 00:00:00 GMT

Understanding and Overcoming Optimization Barriers in Non-convex and Non-smooth Machine Learning Gatmiry, Khashayar At their core, our machine learning systems are trained by solving an optimization problem, where the goal is to minimize a predefined objective function by adjusting model parameters based on the data. Despite the wealth of structure and prior knowledge present in the data and feedback, our training methods remain relatively simple and independent of this structure. In spite of, or perhaps because of, this simplicity, these methods are often lacking in theoretical guarantees. To design machine learning algorithms that are less data-hungry while ensuring theoretical guarantees on both computational efficiency and output validity, it is essential to better understand and leverage the rich structure within the learning setup and the data distribution, e.g. by altering the geometry of the solution space or adjusting the objective function to induce a more effective learning procedure. This approach moves beyond classical algorithm design, which focuses primarily on handling worst-case instances. This thesis investigates the optimization landscape of central learning problems and develops geometric and analytic schemes adapted to their structure, leading to algorithms with superior computational and statistical performance. In addition, it seeks to advance our mathematical understanding of the principles underlying the success of deep learning.

The Causal Effects of Mandatory Quarterly Earnings Guidance on Corporate Information Environment and Corporate Short-Termism

Mon, 01 Sep 2025 00:00:00 GMT

The Causal Effects of Mandatory Quarterly Earnings Guidance on Corporate Information Environment and Corporate Short-Termism Wang, Yuting I examine the causal effects of mandatory quarterly earnings guidance using a regulatory mandate in China that required a subset of listed firms to issue bundled quarterly earnings guidance from 2007 to 2018. A difference-in-differences analysis shows that when these firms are no longer required to issue such guidance, their corporate information environment deteriorates, evidenced by reduced analyst coverage, fewer site visits, and lower price timeliness, meaning that stock prices incorporate less information about current and future earnings. However, these firms increase R&D and SG&A spending, consistent with alleviated managerial myopia as short-term market pressure eases. These findings highlight the dual-edged nature of the mandatory quarterly earnings guidance and offer insights for both practitioners and policymakers.

Bespoke Threat Models: Achieving Realistic Privacy Guarantees for Deployed Protocols

Mon, 01 Sep 2025 00:00:00 GMT

Bespoke Threat Models: Achieving Realistic Privacy Guarantees for Deployed Protocols Hogan, Kyle This thesis focuses on the question of what degree of privacy is achievable in the real world for long-running applications. We explore this question in two main settings: private advertising and anonymous communication. In doing so, we consider constraints each application may have in practice and what adversarial model is realistic for the context in which the application will be deployed. For real world applications, achieving perfect privacy — especially against a worst case adversary — can be impossible. That is, perfect privacy, while achievable in theory, may in practice require assumptions that conflict with usability, deployability, or utility requirements. This presents a challenge as privacy-preserving technologies can, necessarily, only provide privacy for the people who use them. Because of this, designing around user experience is critical, even if doing so requires compromises in the theoretical degree of privacy a system can provide or the strength of adversaries considered in its threat model. In the space of private advertising, we first propose a novel protocol, AdVeil, that eliminates leakage of user data beyond that revealed by the input/output of the ads ecosystem as a whole. We then provide a minimal modeling of the functionality of digital advertising which we use to prove that, even for systems like AdVeil with minimal leakage, the advertising metrics released at the end of the protocol are sufficient to leak information about end users to advertisers when combined with their audience targeting criteria. In the space of anonymous communication, we propose ShorTor, a new routing protocol for Tor that utilizes techniques popular with content distribution networks (CDNs) to reduce latency while maintaining Tor’s existing anonymity guarantees. We evaluate this protocol using a dataset of over 400,000 latency measurements we collected between the 1,000 most popular Tor relays.

Operationalizing Reliable Machine Learning: From Data Collection to Model Presentation

Mon, 01 Sep 2025 00:00:00 GMT

Operationalizing Reliable Machine Learning: From Data Collection to Model Presentation Balagopalan, Aparna Automated systems driven by machine learning (ML) have made exciting progress across a spectrum of applications. Despite such progress, encoded biases and other failure modes may create barriers to the real-world utility and reliability of such systems. For example, nonrandom data missingness, biased algorithmic optimization objectives, or model presentation strategies that incorrectly impact user trust can all cause models to fail in practice. In this thesis, guided by such observations and prior work on pipeline-awareness in machine learning, we aim to operationalize reliable ML. Under this goal, we propose a framework consisting of the following three components: responsible data collection, robust algorithm development, and fair model presentation. We first conduct two case studies to advance responsible data collection. We investigate whether standard procedures for acquiring data can be repurposed when training models to mimic human judgments about norm violations. We also demonstrate patterns of delayed demographic data reporting within a longitudinal healthcare dataset and show that timevarying missingness due to such delays can distort disparity assessments. Second, we introduce two novel algorithms to improve reliability: a method that leverages representations from vision-language models to filter noisy training data, and a method to produce fair rankings that account for properties of search queries. Finally, since the presentation design of predictions impacts trust in model consumers, we propose metrics to quantify the fairness of post-hoc explainability techniques. Thus, with this thesis, we re-evaluate measurements throughout the machine learning pipeline and contribute to the broader goal of reliable machine learning.

Anti-phage defense as a driver of molecular innovation

Mon, 01 Sep 2025 00:00:00 GMT

Anti-phage defense as a driver of molecular innovation Doering, Christopher Ross Bacteriophages, or phages for short, pose a near-constant threat to the bacteria they infect. Billions of years of conflict has been a catalyzing force for the creation of bacterial defense systems and corresponding phage evasion strategies. To counter phage predation, bacteria have developed a vast diversity of enzyme chemistries and molecular sensing mechanisms whose study has produced new biotechnological tools and insights into our own immune systems. In this work, I have investigated anti-phage defense mechanisms at multiple scales using a combination of genetic, biochemical, and bioinformatic approaches. First, I characterized the mechanism of action of the anti-phage defense system CmdTAC, a toxin-antitoxin-chaperone system that recognizes a viral structural protein to activate a novel mRNA ADP-ribosyltransferase, thereby halting infection. Next, I examined the diversity and distribution of anti-phage mechanisms encoded by E. coli lysogenic phages – phages capable of integrating into and lying dormant within their bacterial hosts. This analysis uncovered overlooked classes of lysogenic phages harboring novel candidate defense systems, including one newly validated system with no detectable homology to previously known mechanisms. Together, this work broadens our understanding of bacterial immune systems, expands the pool of known enzyme chemistries, and highlights areas where continued study can reveal additional mechanisms of anti-phage defense.

Shaping Function Through Space: The Role of Spatial Organization in Microbial Communities

Mon, 01 Sep 2025 00:00:00 GMT

Shaping Function Through Space: The Role of Spatial Organization in Microbial Communities Toneatti Vercelli, Gabriel Spatial organization plays a critical role in microbial community function, influencing how cells exchange metabolites, coordinate behavior, and compete for resources. This thesis investigates the consequences of spatial structure in natural microbial systems and introduces a novel method to engineer these systems with high precision and scalability. First, we examine the colonization of chitin particles by marine bacteria, a model for particulate organic matter degradation. Using high-throughput phenotyping of natural isolates, we show that vitamin cross-feeding is essential for successful colonization of chitin-particles by many auxotrophic strains. We then model two distinct vitamin cross-feeding mechanisms: lysis and secretion. Using a resource-explicit modeling approach, we leverage metabolic-flux and physiological measurements to predict the colonization success of auxotrophic cross-feeders in this spatially structured environment. Second, we introduce a new chemical method for engineering microbial cell surfaces that enables covalent attachment of molecules such as enzymes and DNA strands to the cell surface. We show that this surface functionalization procedure leads to the acquisition of new phenotypes like antibiotic resistance and programmable adhesion. Altogether, this work reinforces the importance of spatial organization for microbial community function and introduces a new technique to harness this community feature and turn it into a design principle for synthetic microbial systems.

Developing Methods for Enhanced Measurement of DNA Single-Strand Breaks and Somatic Variants

Thu, 01 Feb 2024 00:00:00 GMT

Developing Methods for Enhanced Measurement of DNA Single-Strand Breaks and Somatic Variants Elacqua, Juniper J. Maintenance and repair of DNA are essential for proper cellular functioning and preventing the emergence of disease states. As cells divide, mutations accumulate in the genome which contributes to aging phenotypes and can result in genetic diseases such as cancer. The rate at which a cell develops mutations can be accelerated through exposure to genotoxic agents that introduce lesions which, if left unrepaired, prevent accurate replication of the genome. As such, it is crucial to understand the ways in which DNA becomes damaged, how cells respond to various types of damage, and how this damage contributes to mutagenesis and the development of genetic disease. These fields of study have been greatly advanced by improvements in DNA sequencing technologies, and here we present two sequencing-based methods that aim to enable deeper study of DNA damage, repair, and mutagenesis. First, we demonstrate DENT-seq, a method that identifies single-strand breaks with single-nucleotide resolution. Single-strand breaks are the most common form of DNA damage, occurring at rates of ~10,000 per cell per day, but have to date been understudied due to lack of an unbiased, high-resolution method for their detection. Second, we improve upon lineage sequencing, a previously reported method that uniquely measures somatic single nucleotide variants in dividing cells to achieve high specificity/sensitivity as well as the ability to temporally resolve variants and to relate sequenced genotypes to optically observed cellular phenotypes. Despite the high-quality data and unique capabilities offered by this method, it has so far been underused due to a need for complex, microfluidic-based cell collection. We demonstrate novel protocols for performing lineage sequencing that enable easy adoption of the method without the need for highly specialized equipment or expertise. In addition, we expand the repertoire of mutations measurable with the technique to include indels and variants that arise specifically in response to a genotoxic treatment. The methods we show can be applied to reveal novel findings regarding the causes and consequences of DNA damage and mutagenesis that underly numerous genetic diseases.Maintenance and repair of DNA are essential for proper cellular functioning and preventing the emergence of disease states. As cells divide, mutations accumulate in the genome which contributes to aging phenotypes and can result in genetic diseases such as cancer. The rate at which a cell develops mutations can be accelerated through exposure to genotoxic agents that introduce lesions which, if left unrepaired, prevent accurate replication of the genome. As such, it is crucial to understand the ways in which DNA becomes damaged, how cells respond to various types of damage, and how this damage contributes to mutagenesis and the development of genetic disease. These fields of study have been greatly advanced by improvements in DNA sequencing technologies, and here we present two sequencing-based methods that aim to enable deeper study of DNA damage, repair, and mutagenesis. First, we demonstrate DENT-seq, a method that identifies single-strand breaks with single-nucleotide resolution. Single-strand breaks are the most common form of DNA damage, occurring at rates of ~10,000 per cell per day, but have to date been understudied due to lack of an unbiased, high-resolution method for their detection. Second, we improve upon lineage sequencing, a previously reported method that uniquely measures somatic single nucleotide variants in dividing cells to achieve high specificity/sensitivity as well as the ability to temporally resolve variants and to relate sequenced genotypes to optically observed cellular phenotypes. Despite the high-quality data and unique capabilities offered by this method, it has so far been underused due to a need for complex, microfluidic-based cell collection. We demonstrate novel protocols for performing lineage sequencing that enable easy adoption of the method without the need for highly specialized equipment or expertise. In addition, we expand the repertoire of mutations measurable with the technique to include indels and variants that arise specifically in response to a genotoxic treatment. The methods we show can be applied to reveal novel findings regarding the causes and consequences of DNA damage and mutagenesis that underly numerous genetic diseases.

Reconfigurable and Interference-Tolerant Receivers for Next Generation Wireless Systems

Mon, 01 Sep 2025 00:00:00 GMT

Reconfigurable and Interference-Tolerant Receivers for Next Generation Wireless Systems Araei, Soroush An “all-in-one” radio, programmable across the sub-7 GHz spectrum, offers significant hardware efficiency for 5G systems. However, addressing strong interferers in this wide and congested spectrum remains a major design challenge. N-path filters offer a promising solution for efficiently suppressing interference, thanks to their clock-controlled reconfigurability and excellent linearity against in-band and adjacent-channel blockers. While widely adopted in modern receiver architectures, these switched-capacitor circuits remain inherently vulnerable to blockers at clock harmonics, due to their hard-switching nature. These blockers, common in 5G bands, pose a key bottleneck, delaying the realization of fully integrated multi-band, multi-mode radios. This dissertation introduces fully passive topologies to address this challenge. The first design leverages simultaneous charge sharing and capacitor stacking to implement harmonic rejection filtering. It operates entirely without active circuitry and exhibits exceptionally low loss. A second-generation technique, termed “harmonic reset switching”, builds on this approach by rejecting harmonic blockers directly at the driving point of the N-path filter, achieving superior performance with reduced circuit complexity. As a result, existing reconfigurable receiver topologies can be seamlessly transformed into harmonic blocker–resilient architectures. For example, a taped-out mixer-first receiver adopting this technique achieves a 100× improvement in third-harmonic blocker tolerance compared to state-of-the-art broadband receivers. This dissertation also proposes a reconfigurable receiver for IoT-class radios that is tolerant to both close-in and far-out blockers. A scalable clock bootstrapping technique is introduced to enhance linearity while maintaining both power and cost efficiency. All designs are validated through prototypes fabricated in advanced 22-nm and 45-nm silicon-on-insulator (SOI) technologies. By addressing this long-standing challenge, this work paves the way for fully reconfigurable, interference-resilient radios for 5G and beyond.

Video as the Language of Embodied Intelligence

Mon, 01 Sep 2025 00:00:00 GMT

Video as the Language of Embodied Intelligence Chen, Boyuan Achieving general-purpose embodied intelligence remains a central challenge in artificial intelligence. While recent efforts have extended Large Language Models (LLMs) to robotics by incorporating additional modalities, these adaptations face critical limitations in perception, grounding, and control. For example, spatial reasoning—a simple yet indispensable capability for robots—reveals one of such shortcoming clearly: multimodal LLMs often fail even basic spatial perception tasks like estimating distances. This thesis begins by examining these failures through SpatialVLM, a system that augments vision-language models with 3D spatial reasoning. Although more effective in spatial estimation, this work reveals a deeper issue: the fundamental expressive limitations of language-only outputs in capturing sensorimotor dynamics. Based on these findings, the thesis advocates for a ground-up methodology for robot foundation models, starting with identifying an appropriate “language” for embodied AI, then architecting models and training regimes accordingly. We investigate video as the foundational language, integrated with model-based planning for decision-making. This new paradigm is instantiated through two core contributions. The first is Diffusion Forcing, a hybrid modeling framework that combines causal next-token prediction with full-sequence diffusion. This approach supports stable, coherent rollouts far beyond the training horizon and allows guided generation for decision-making tasks, bridging predictive modeling and planning. Building on Diffusion Forcing, we introduce the Diffusion Forcing Transformer (DFoT), a natural architectural extension designed for flexible video generation conditioned on variable-length histories. To further support long-horizon world-modeling, we propose History Guidance, a set of techniques that enhance sample fidelity, temporal consistency, and compositional generalization. Together, these methods enable robust modeling of visual dynamics across extended timeframes. Finally, we present a preliminary yet promising video foundation model for zero-shot robot motion planning, highlighting the potential of video as the foundational language of embodied intelligence.

Biologically Interpretable Representation Learning for Mechanistic Insights into Cancer Immunotherapy Resistance

Mon, 01 Sep 2025 00:00:00 GMT

Biologically Interpretable Representation Learning for Mechanistic Insights into Cancer Immunotherapy Resistance Tariq, Ifrah Resistance to immune checkpoint inhibitors (ICIs) remains a critical barrier to effective cancer therapy, driven by complex, multi-scale interactions that current biomarkers often fail to capture. This dissertation introduces the Biologically Disentangled Variational Autoencoder (BDVAE)—an interpretable deep learning framework designed to uncover mechanistic drivers of ICI resistance through multi-omic data integration. Using RNA-seq and wholeexome sequencing data from 366 patients across melanoma, renal cell, urothelial, and gastric cancers, BDVAE learns low-dimensional latent representations that are both predictive of response and biologically meaningful. The model reveals distinct latent dimensions aligned with immune regulation, tumorintrinsic signaling, metabolism, and neuroimmune interactions. SHAP-based interpretation and pathway analysis highlight key resistance-associated programs, including immunosuppressive cytokine signaling, metabolic signaling, and neuroactive pathways such as calcium and cAMP signaling. Unsupervised clustering identifies three tumor subtypes—responder-dominant, non-responder-dominant, and an intermediate group—suggesting plastic or transitional immune states. Survival analyses confirm the clinical relevance of these clusters and expose heterogeneity within standard RECIST categories. Overall, this work presents a novel, interpretable framework for modeling ICI response, offering insights into resistance mechanisms and actionable paths for biomarker discovery, patient stratification, and therapeutic innovation in precision immuno-oncology.

Geometric interpretations of structural demand for the analysis and reduction of design complexity

Mon, 01 Sep 2025 00:00:00 GMT

Geometric interpretations of structural demand for the analysis and reduction of design complexity Lee, Keith Janghyun This dissertation presents a computational framework to effectively interpret the distribution of structural demand that emerges from the design of large-scale structural systems, and develops methods for its quantification and manipulation. Structural demand is the required strength and geometry of individual building components that emerges from design as a result of global geometry, topology, and loading. Existing metrics of structural performance fail to consider how variations in demand at the component level can lead to designs that are theoretically efficient but difficult to construct. This has led to a rejection of low-carbon, high-performance design solutions in practice, or the need for extensive post-hoc rationalization, both under the presumption of untenable design complexity for conventional building practices. This dissertation argues that an explicit consideration of the distribution of induced structural demand can bridge this gap between design intent and construction feasibility. To achieve this, structural demand is interpreted as sets of geometric objects in n-dimensional feature spaces, where each dimension represents an independent component of demand, such as area, length, or stiffness. By directly visualizing the spatial distribution of demand, designers are presented with a richer context of non-physical structural design information, and can evaluate how decisions in structural form affect this distribution. Further, spatial interpretations of information allow for spatial metrics of similarity and variation to be defined, from which quantitative measures of design complexity are derived that account for the shape and distribution of demand. This framework, named \emph{Demand Space Analysis}, is explored in depth and applied to a range of structural scales, from the demand of truss elements and their connections, to the relationship between demand and fixed sets of capacity. Advancements in structural optimization are also presented, enabling more efficient and direct minimization of modern structural performance metrics, from which the relationship between design performance and demand complexity can be explored. Through case studies in each chapter, this dissertation demonstrates how geometric analysis of structural demand information can inform the designer of the implications of decisions on the perceived complexity of design, and provides tools for its quantification and reduction.

The Inhabited Arctic: Architecture, Time, and the Making of the Past in the Bering Strait (1760–1980)

Mon, 01 Sep 2025 00:00:00 GMT

The Inhabited Arctic: Architecture, Time, and the Making of the Past in the Bering Strait (1760–1980) Springstubb, Phoebe Our view of antiquity is not objective. From the eighteenth century on, the same actors and institutions involved in colonizing the Arctic shaped understandings of its deep past. Commercial whalers erected outposts on the Arctic Ocean’s edges; miners stripped tundra; trading companies raised forts. The demands of these projects complicated the Western imperial fiction of an Arctic without a past. Grappling with Arctic terrain, foreigners were confronted by a landscape inhabited not only by people and animals but by time and temporal imaginations that long preceded European colonization. They encountered contemporary Indigenous settlements coexisting with ancestral houses, fossil animals, the ruins of earlier colonial ventures, and ancient routes of exchange. This dissertation, centered on the Bering Sea and its adjacent geographies of eastern Siberia and Arctic North America, tells the story of how imperial upheaval and the rooting of colonial projects in the ground sparked a deliberate historiographic project to write the Arctic’s deep past. At the heart of this project was a conflict of different cultural views of time. Who had the right to narrate history in these northernmost borderlands? In episodes spanning two centuries, from the Russian empire’s claim to the Bering Sea to the rise of modern decolonial movements, this dissertation traces the central role of diverse Native architectures and technologies. Iñupiaq houses built from great whale skeletons, Unangax watercraft hewn from circulating driftwood, and Chukchi ice cellars carved into permafrost were both prisms for temporal explanations and sites driving change. Russian colonial administrators, British geologists, US ethnographers, Orthodox priests, and Soviet engineers co-opted them to the lineal, geological, eschatological, and paleolithic time that scaffolded imperial projects. Simultaneously, these material practices were vital sites for reinvention and identity, where Native nations built futures out of rupture. Illuminating how the ecological and epistemic limits to empire-building spurred new theories of Arctic time, this project shows history-making to be a crucial tool different states adopted to justify and naturalize their possessions of Native lands. At stake was not static historical truth but how politically situated temporalities structured their present-day actions. The ethical dimensions of deep time, imagined from the Bering Strait’s modern lands and seas, empowered empire’s practical work. How the past was conceived in different intellectual traditions informed whether animals and plants were exploitable resources or ancestors giving their bodies to architecture. This project contends that how people understood themselves as being in time was a decisive fulcrum ordering collective beliefs in what was owed to a larger, nonhuman world. Taking time as an analytical lens, this dissertation identifies repeated efforts to cleave the Arctic’s human history from nature’s past. Used to justify a wide range of colonial hierarchies and violence in the long nineteenth century, it underlies a contemporary bias toward seeing the Arctic as a region of deep naturalism. Viewed as a place where an “extreme” climate dominates manifold other historicities, the past so circumscribed continues to shape future possibilities.

Building the 3D Genome from the Ground Up: Local Interactions Give Rise to Global Order

Mon, 01 Sep 2025 00:00:00 GMT

Building the 3D Genome from the Ground Up: Local Interactions Give Rise to Global Order Athreya, Advait The three-dimensional organization of the genome within the nucleus plays a central role in determining gene regulation and establishing cellular identity, but the mechanisms by which local molecular interactions give rise to global chromatin architecture remain an active area of study. Interactions between nucleosomes—modulated by histone tail post-translational modifications, histone sequence variants, and the DNA sequence itself—are thought to be a major driver of this emergent structure. In this thesis, I address the question of how these intrinsic physicochemical properties of nucleosomes drive the formation of large-scale structures such as chromatin compartments. I develop a theoretical framework based on Flory-Huggins solution theory to derive pairwise internucleosome contact energies from the results of condense-seq, a novel experimental technique that measures the phase separation likelihood of native nucleosomes. I then use these derived energies to parameterize coarse-grained molecular dynamics simulations of chromatin at various resolutions, ranging from 25kb segments to simulate an entire chromosome, down to individual nucleosomes to simulate up to 10Mb genomic regions. These simulations demonstrate that the intrinsic nucleosome properties alone can capture a significant degree of A/B compartment formation observed in Hi-C experiments, despite the deliberate exclusion of all other factors such as loop extrusion and transcriptionfactor-mediated phenomena. This finding establishes that local nucleosome properties play a fundamental role in genome organization. To capture more detailed chromatin physics, I develop an extended chromatin force-field that incorporates anisotropic nucleosome stacking interactions and linker DNA properties using a novel approach for simulating reversible bond formation in molecular dynamics. This model reveals how nucleosome stacking strength, linker DNA geometry, and torsional stress collectively influence higher-order structures. Early results show that the linker-length-dependent DNA torsion contributes to nematic ordering of chromatin, consistent with experimental studies. Future development of this model will enable probing of discrete domain formation observed in imaging studies. Finally, I address a critical consideration for researchers in the chromatin organization field when analyzing Hi-C results. I compare two software tools — cooltools and dcHiC — highlighting the importance of careful parameter selection and analytical choices in designing workflows to ensure reproducible research. Taken together, this work establishes a quantitative, bottom-up modeling framework that directly links the local physicochemical properties of nucleosomes to the global principles governing three-dimensional genome organization. It provides a complementary approach to more data-driven top-down models that have made significant inroads but are challenging to interpret mechanistically. With further development, the work presented in this thesis will contribute towards predicting the structural consequences of specific epigenetic modifications and move us closer to understanding the molecular grammar of chromatin and its role in cellular function and disease.

On Subannual Variability in the Abyssal Ocean

Mon, 01 Sep 2025 00:00:00 GMT

On Subannual Variability in the Abyssal Ocean Chen, Si Yuan The abyssal ocean is a critical yet understudied component of the climate system and is of growing economic interest. This thesis combines field observations and numerical modeling to advance our understanding of subannual variability in the abyssal ocean and its broader implications. First, hydrographic measurements from the Clarion-Clipperton Zone of the tropical Northeastern Pacific are used to characterize the structure and variability of the bottom mixed layer (BML) in a region targeted for deep-sea mining. The observations reveal a spatially and temporally variable BML with a mean thickness of ~250 m and influenced by interactions with mesoscale eddies and abyssal thermal fronts. A simplified model of sediment transport suggests that such variations in BML structure could significantly influence the dispersal of sediments resuspended by seabed mining activities. Second, idealized model experiments are conducted to explore the genesis of benthic storms – episodes of strong near-bottom flows and sediment entrainment – underneath an unstable, surface-intensified jet resembling the Gulf Stream east of Cape Hatteras. In these experiments, the baroclinic instability of the jet gives rise to deep cyclonic and anticyclonic eddies through eddy barotropization and to high levels of eddy kinetic energy at abyssal depths through the convergence of vertical eddy pressure fluxes. The near-bottom currents are comparable in magnitude to those observed during benthic storms, with vertical shears strong enough to produce BMLs up to O(100) m thick. Deep cyclonic eddies transport particles from near the bottom over the entire BML and could contribute to benthic nepheloid layers. The results suggest that the abyssal response to the intrinsic instability of surface-intensified currents could contribute significantly to subannual variability near the seafloor. Third, a model simulation of western North Atlantic circulation is performed to study the deep cyclones (DCs) observed beneath Gulf Stream meander troughs. The characteristics of the simulated DCs compare well with field observations. The negative pressure tendency during cyclogenesis arises from a small imbalance between the sea surface depression and the vertically-integrated increase in seawater density. Vortex stretching is the primary source of cyclonic vorticity, while vortex tilting is a non-negligible sink. The deep pressure tendency, vorticity fluxes, and ageostrophic flows are diagnosed, and their similarities and differences with mid-latitude synoptic cyclones in the atmosphere are discussed. Near-bottom currents in DCs dominate the basin-scale bottom energy dissipation and transport fluid over ≥1000 km horizontally and O(100) m vertically within 3~4 months, suggesting that they provide an efficient mechanism for tracer and material transport in the abyssal interior. Collectively, this thesis highlights the importance of transient, mesoscale processes in contributing to subannual variability in the abyssal ocean, particularly near the seafloor. The findings have broader relevance for monitoring the environmental impacts of human activities, including deep-sea mining and carbon sequestration. While further questions remain for future investigation, this work underscores the need for sustained in-situ observations in the abyssal ocean and calls for the implementation of high vertical resolution in numerical ocean circulation models.

Democratizing High-Performance DSL Development with the BuildIt Framework

Mon, 01 Sep 2025 00:00:00 GMT

Democratizing High-Performance DSL Development with the BuildIt Framework Brahmakshatriya, Ajay Modern high-performance software from a variety of domains relies on hand-written and hand-optimized libraries to obtain the best performance. Besides general fine-grained operators that can be composed to write entire applications, these libraries also provide coarser-grained fused and hand-optimized operators that are much faster due to being optimized for a specific sequence of operations. However, as application needs keep growing, library writers are not able to keep up and have to make the tradeoff of either sacrificing performance or generality. Domain-specific languages or DSLs are able to break this tradeoff by automatically generating the best implementation for any arbitrary sequence of operations specified by the end user. However, DSL compilers suffer from a bigger challenge that they require a lot of compiler knowledge to implement parsers, IR, analysis and transformations, and code generation, which is outside the scope of a typical domain expert. To make compiler technology and the benefits of code-generation more accessible to domain experts, I propose the use of multi-stage programming to allow writers to write library-like code while also combining it to generate the most efficient implementation for any whole program. In this thesis, I discuss the design of different multi-stage programming systems, the benefits and drawbacks. Next, I propose Re-Execution Based Multi-Staging (REMS) that addresses a critical flaw in many imperative Multi-Staging systems - the side-effect leak problem. I introduce BuildIt, an implementation of REMS in one of the most popular languages for writing high-performance applications, C++ in a type-based, lightweight way without changing the compiler. I describe the internals of BuildIt and how it implements the key features of REMS. Furthermore, I describe a set of extensions implemented on top of BuildIt that facilitate the development of high-performance DSLs with ease. I show the application of BuildIt to create three DSLs - EasyGraphit, NetBlocks, and BREeze that target graph analytics, ad-hoc network protocol generation, and Regex matching. All these case studies show 10-100x reduction in the amount of effort required to implement these DSLs that perform on-par with or better than state-of-the-art compiler frameworks while targeting diverse architectures like CPUs and GPUS. Finally, I introduce D2X, a system that is designed to add extensible and contextual debugging support to DSL implementations without having to make any changes to off-the-shelf debuggers or mess with complex debugging formats. Next, I show how applying D2X to the BuildIt system greatly improves the debugging experience for all DSLs written with BuildIt.

Tailoring Li₄Ti₅O₁₂ Thin Film Carrier Kinetics Through Solid Solution Doping for Battery and Memristor Applications

Mon, 01 Sep 2025 00:00:00 GMT

Tailoring Li₄Ti₅O₁₂ Thin Film Carrier Kinetics Through Solid Solution Doping for Battery and Memristor Applications Buzzell, Drew E. A Lithium titanate, Li₄Ti₅O₁₂ (LTO4), due to its zero-strain behavior during cycling, excellent chemical stability and cyclability, is a promising anode material for solid-state batteries (SSB) applications. As a thin film, its applications expand to integrated circuits, sensors, flexible batteries, IoT devices, and memristors. Across these, precise control of mixed Li⁺ ionic–electronic transport is vital. While dopants have been shown to improve electron conduction and Li⁺ diffusion in LTO4 powders, thin-film studies remain limited. To bridge this gap, we investigate solid solution dopants (Nb⁵⁺, V⁵⁺, Mg²⁺, Cu²⁺) and their effects on LTO4 thin-film kinetics and performance in batteries and memristors. Films doped with Mg, Cu, Nb, and V with a 0.2M dopant concentration were deposited on Nbdoped SrTiO₂ substrates. Cyclic voltammetry and impedance spectroscopy show that Mg, Nb, and V improve kinetic metrics, while Cu reduces diffusivity but boosts electronic conductivity. Through galvanostatic cycling-based capacity, rate capability, and stability measurements, we found that while all dopants displayed enhanced rate performance, the capacity improved only with Mg, Nb, and V. Furthermore, the Mg-doped film was found to have an unstable capacity leading to Nb- and V-doped thin-films as the best overall performing battery anodes. For memristors, current–voltage cycling measurements revealed that low concentrations (0.05 M) of Cu and Nb doped devices presented the largest improvements in cycle-to-cycle stability, switching ON-voltages, ON-OFF current ratios, and lower loss in peak current with increasing scan rate. With increasing dopant concentrations however, devices would see relative drops in performance. In summary, the inclusion of dopants in LTO4 at the right concentration level leads to improvements in both battery and memristor performance allowing for one material multi-functional systems.

The Role of CH–π Interactions in Protein-Carbohydrate Binding

Mon, 01 Sep 2025 00:00:00 GMT

The Role of CH–π Interactions in Protein-Carbohydrate Binding Keys, Allison M. Protein-carbohydrate binding is essential for biological processes, including cellular recognition and immune signaling. Binding is driven by several types of non-covalent interactions: hydrogen bonding, metal ion coordination, and the less well-understood CH–π interactions. CH–π interactions are pervasive in protein-carbohydrate binding sites and have emerged as critical drivers of protein–carbohydrate recognition; however, the energetics of CH–π stacking interactions, their orientational landscapes, and their interplay with other non-covalent interactions have been unclear. In this thesis, I identified carbohydrate-aromatic CH–π stacking interactions from crystallographic structures in the Protein Data Bank. I performed quantum mechanical calculations to quantify interaction energies and found that CH–π stacking interactions can be more favorable than hydrogen bonds. Using atomistic simulations, I also demonstrated that CH–π stacking interactions are necessary for human galectin-3 binding to lactose. To assess the orientational landscape of CH–π stacking interactions, I evaluated the orientations of CH–π stacking interactions formed by β-D-galactose and found that numerous orientations are highly favorable. I then identified carbon atom distances that define an orientational landscape for these interactions. To assess the interplay between non-covalent interactions in protein-carbohydrate binding sites, I used CH–π distance features to bias metadynamics simulations of a curated set of protein–β-D-galactoside complexes. From these simulations, I found that while bound carbohydrates sample many CH–π stacking orientations, the hydrogen bonds in the protein binding site drive the optimal orientation of each ligand. Longer carbohydrate ligands with more hydrogen bonding constraints have more specific orientational dependence, while ligands in binding sites with a reduced number of hydrogen bonds occupy a broader range of orientations. Unlike hydrogen bonds, CH–π stacking interactions confer orientational flexibility: enzymes can exploit multiple CH–π stacking interactions to facilitate the translocation of polysaccharide substrates. Extending this analysis to other carbohydrates, I showed that carbohydrate stereochemistry drives the orientational preferences of CH–π stacking interactions; however, there is also a tradeoff between the presence of hydrogen bonds to charged amino acids and the CH–π interaction strength for each carbohydrate. Overall, this thesis demonstrates that CH–π interactions are favorable and confer high orientational flexibility and that hydrogen bonds act in concert with CH–π interactions to stabilize protein-carbohydrate binding. Tuning the number and positions of these interactions through protein engineering should alter protein selectivity and ligand movement in protein binding sites.

Quantum Networking using Waveguide Quantum Electrodynamics

Mon, 01 Sep 2025 00:00:00 GMT

Quantum Networking using Waveguide Quantum Electrodynamics Almanakly, Aziza The architectural principle of modularity enables the construction of complex systems from simpler components, each responsible for a particular function. The quantum computer is an intricate system comprising fragile, error-prone parts known as qubits. Entanglement distribution across a network of non-local processing modules facilitates robust and extensible quantum computation. In modular quantum architectures, photons are natural quantum information carriers which propagate through interconnects between processing nodes. In this thesis, we engineer a quantum interconnect between superconducting modules underpinned by the physics of waveguide Quantum Electrodynamics (wQED). First, we realize a multi-qubit module that exploits quantum interference to emit microwave photons into a waveguide with a specified propagation direction. Next, we construct the quantum interconnect by coupling two modules to a common waveguide and demonstrate directional (chiral) photon emission and absorption. Finally, using this chiral quantum interconnect, we generate remote entanglement, establishing a key resource for distributed quantum computation in an all-to-all network architecture.

Essays in Macro-Finance

Mon, 01 Sep 2025 00:00:00 GMT

Essays in Macro-Finance Batista, Quentin In Chapter 1 (joint with J.R. Scott), we revisit the high-frequency and narrative approaches to estimating the effects of monetary policy shocks. We find that state-of-the-art estimates using both approaches are biased: high-frequency estimates due to nonlinear predictability and narrative estimates due to regularization. To correct for the bias in these approaches, we propose a new estimation procedure called LP-DML that combines ideas from double/debiased machine learning with the local projections framework. We find that LP-DML results in significantly smaller effects of monetary policy on macroeconomic outcomes. In Chapter 2 (joint with Taisuke Nakata and Takeki Sunakawa), we study the following question: how a central bank credibly implement a ”lower-for-longer” strategy? To answer this question, we analyze a series of optimal sustainable policy problems—indexed by the duration of reputational loss—in a sticky-price model with an effective lower bound (ELB) constraint on nominal interest rates. We find that, even when the central bank lacks commitment, the central bank can still credibly keep the policy rate at the ELB for an extended period though not as extended as under the optimal commitment policy—and meaningfully mitigate the adverse effects of the ELB constraint on economic activity. In Chapter 3, I examine the impact of central bank real estate purchases on financial markets, focusing on the Bank of Japan’s (BoJ) intervention in the Real Estate Investment Trust (REIT) market. Using a regression discontinuity design that exploits a discontinuity in the BoJ’s policy rule, I find that a typical intervention — amounting to about 0.014% of market capitalization — leads to an increase of 0.1% to 0.2% of REIT prices in the hours following the intervention. However, at longer horizons, the interventions do not have a significant effect on REIT prices. These findings suggest that the BoJ did not achieve the program’s intended objective of significantly reducing the risk premium on real estate assets.

Toward an Integrative Study of Human-AI Interaction

Mon, 01 Sep 2025 00:00:00 GMT

Toward an Integrative Study of Human-AI Interaction Alsobay, Mohammed As artificial intelligence (AI) systems are increasingly embedded in the workflows of individuals and groups, designers and researchers of human-AI interaction (HAI) navigate a vast design space of possible configurations, making decisions that span algorithmic parameters, interface choice, and interaction protocols. This thesis develops an integrative approach that examines how design factors combine and interact to determine the outcomes of human-AI collaboration. Chapter 1 synthesizes prior HAI research into a coherent design space framework encompassing algorithms, interfaces, users, and task settings, motivating a research program for systematic exploration of interdependencies between these factors. Chapters 2 and 3 turn to group-AI interaction through large-scale behavioral experiments. Chapter 2 investigates how social information---both direct conversation and peer behavior indicators---affects individual reliance on algorithmic decision support. The study reveals that while social information modulates the effects of performance feedback and model explanations on reliance, it does not improve predictive accuracy, illuminating critical tensions between social mechanisms and system design. Chapter 3 examines large language models as facilitators of group deliberation in hidden profile tasks. While LLM facilitation increased information sharing volume, density, and breadth, it did not improve decision quality, highlighting fundamental challenges in group-AI system design beyond information aggregation. Chapter 4 advances an integrative approach to HAI research, emphasizing shared design spaces, systematic exploration strategies, and predictive models that generalize across contexts. The chapter provides methodological guidance and a tractable roadmap for advancing this integrative research agenda, laying the foundation for a more context-aware science of human-AI collaboration.

Leveraging design to build with less: Evaluating the embodied carbon reduction potential of architectural design across scales

Mon, 01 Sep 2025 00:00:00 GMT

Leveraging design to build with less: Evaluating the embodied carbon reduction potential of architectural design across scales Feickert, Kiley Reducing embodied carbon (EC) in structural systems -- the most significant contributor to EC in a building -- is urgent to address the simultaneous need to reduce global warming and increase urban density. Much of the policy and research to date to reduce EC has focused on material-scale interventions or substitutions. However, EC depends on both: 1) the carbon intensity of the processes used to manufacture construction materials, and 2) the volume of raw materials required. Architects have significant agency to reduce the volume of structural materials in a building (and the resulting emissions) since the required quantity depends on design decisions architects make, including column spacing, structural typology, massing, etc. To date, most methods used to estimate EC during early-stage design do not: 1) integrate with architects’ existing design workflows, 2) evaluate multiple material systems simultaneously, and/or 3) include structural analysis to estimate material quantities. This functionality is critical so that designers can understand which decisions EC is sensitive to and evaluate design and EC tradeoffs before significant carbon is locked in. To address this problem, this dissertation presents a method towards transparent estimation of structural material quantities, intending to inform architectural design and policy, or other emerging EC standards. This method is used to contribute an analysis of the effectiveness of emerging U.S. EC policies, which focus on different scales of intervention, at the building scale. These policies are evaluated in isolation and in combination with strategic design levers that take advantage of structural mechanics to reduce material quantities for various building configurations and material systems. It finds that the most prominent policy approach, “Buy Clean” materials, only reduces EC by ~9% and ~16% for steel and concrete systems, respectively, compared to strategic design choices that have the potential to yield savings of up to ~79%. This dissertation also identifies building massing as a key lever in the EC outcomes of structural systems and proposes a method to quantify the impact of massing using automated structural design and analysis. It finds that in some situations, cantilevered massing typologies can be materialized for no carbon penalty if efficient configurations are used. Indeed, if inefficient configurations are used, they can incur a significant carbon penalty (2.4x) compared to normative massing. The presented results highlight the potential of design to reduce demand-side EC across scales.

Generalizable Robot Manipulation through Unified Perception, Policy Learning, and Planning

Mon, 01 Sep 2025 00:00:00 GMT

Generalizable Robot Manipulation through Unified Perception, Policy Learning, and Planning Fang, Xiaolin Advancing robotic manipulation to achieve generalization across diverse goals, environments, and embodiments is a critical challenge in robotics research. While the availability of data and large-scale training has brought exciting progress in robotics manipulation, current methods often struggle with generalizing to unseen, unstructured environments and solving long-horizon tasks. In this thesis, I will present my work in robot learning and planning that enables multi-step manipulation in partially observable environments, towards general-purpose embodied agents. Specifically, I will talk about my work in 1) constructing a modular framework that estimates affordances with learned perception models with task-and-motion-planning (TAMP) for object rearrangement in unstructured scenes, 2) learning generative diffusion models of robot skills, which can be composed to solve unseen combination of environmental constraints through infeference-time optimization, 3) leveraging large vision-language models (VLMs) in building task-oriented visual abstractions, allowing skills to generalize across different environments with only 5 to 10 demonstrations. Together, these approaches contribute to the generality and scalability of embodied agents towards solving real-world manipulation in unstructured environments.

Towards High-Dimensional Generalization in Neural Networks

Mon, 01 Sep 2025 00:00:00 GMT

Towards High-Dimensional Generalization in Neural Networks Boopathy, Akhilan Neural networks excel in a wide range of applications due to their ability to generalize beyond training data. However, their performance degrades on high-dimensional tasks without large-scale data, a challenge known as the curse of dimensionality. This thesis addresses this limitation by pursuing three key objectives aimed at understanding and improving neural network generalization. 1. We aim to investigate the scaling laws underlying generalization in neural networks including double descent, a phenomenon in which as a model’s capacity or training data is increased, the test error temporarily increases at a certain point before continuing to decrease. In particular, we will have two goals: 1) a better understanding of when double descent can and cannot be empirically observed and 2) a better understanding of scaling laws with respect to training time. 2. Inductive bias refers to the set of assumptions a learning algorithm makes to predict outputs on inputs it has not encountered. We propose quantifying the amount of inductive bias required for a model to generalize well with a fixed amount of training data. By developing methods to measure inductive bias, we can assess how much information model designers need to incorporate into neural networks to improve their generalizability. This quantification can guide the design of harder tasks that better test a model’s generalization. 3. Finally, we aim to develop new methods to enhance neural network generalization, particularly focusing on reducing the exponential number of training samples required for high-dimensional tasks. This involves creating algorithms and architectures that can learn effectively from limited data by incorporating stronger inductive biases. In particular, we will focus on two inductive biases in particular: 1) learning features of the training loss landscape correlated with generalization and 2) using modular neural network architectures. We expect that these techniques can improve generalization, particularly in high-dimensional tasks. Together, these contributions aim to deepen our theoretical understanding and develop practical tools for enabling neural networks to generalize effectively from limited data.

Remote Control: Art, Technology, and the Politics of Distance (1966-1972)

Mon, 01 Sep 2025 00:00:00 GMT

Remote Control: Art, Technology, and the Politics of Distance (1966-1972) Wexelblatt, Nina Rrose Platforms carrying dancers across a stage, doors sliding open as if by magic, and simultaneous Happenings in Berlin and Buenos Aires: remote control promised thrills as postwar artists experimented with technologies of distance. Focused on the half-decade between 1966 and 1972, this thesis intervenes in the history of art and technology to argue that a desire to activate the supposedly empty space between artist, art object, and audience effected a new fixation on the nature of that distanced interval, leading artists to incorporate actual remote control technologies into their work. This impulse grew from an unorthodox reading of the work of modernist painters, particularly Jackson Pollock. Where a generation of critics had canonized “presentness” and medium specificity, a younger cohort read the work differently, finding in it permission to embrace remoteness, intermedia experimentation, and political messaging. Artists including Robert Rauschenberg, Allan Kaprow, Marta Minujín, Wolf Vostell, and Carolee Schneemann, among others, undertook radical experiments with remote systems, often in collaboration with engineers. Theirs was not a technocratically neutral position; this thesis demonstrates that these artists consciously cast the “remoteness” enabled by new technologies as a charged concept, just as controlled distance emerged to define military and industrial relations on domestic, urban, and geopolitical scales. Remote control enabled artists to incorporate, not reject, the expanding frames of reference taking place outside of the sanctioned spaces of the art studio or gallery, from automation to satellite communications to warfare. Artists’ uses of remote technologies intentionally surfaced questions about critical power relations, tying the stakes of their work to debates about the future of U.S. social and economic control and development. In doing so, it also crystallized a newly diffuse, participatory artistic subject: the controller. The introduction theorizes “remote control” in historical and historiographic context. A second chapter follows Automation House (1970-1972), a Manhattan art space that combined labor mediation and media art to experiment with the American postindustrial labor economy to come. A third chapter centers on Three Country Happening (1966), which took place in New York, Buenos Aires, and Berlin, supposedly mediated by satellite—foiled by the uneven development of the Cold War-era satellite system itself. A fourth chapter delves into Snows (1967), a multimedia performance in protest of the war in Vietnam, which incorporated audience-controlled feedback sensors. A concluding discussion traces the ongoing nature of remote control as it implicates artists and audiences alike in a network of shared responsibility.

Essays on Behavioral Economics and Sophisticated Procrastination

Mon, 01 Sep 2025 00:00:00 GMT

Essays on Behavioral Economics and Sophisticated Procrastination Chen, Xi Procrastination is a widespread yet complex behavior that resists simple explanation. This dissertation integrates theoretical modeling with experimental evidence to examine procrastination through the lens of sophisticated decision-making. It reframes procrastination not merely as a deviation from rationality, but as a behavior shaped by strategic trade-offs, self-awareness, and individual heterogeneity. The first essay develops a theoretical model of Perfectionistic Procrastination, proposing that individuals with high internal standards may delay tasks not as a simple lapse in self-control, but as a strategic response to the anticipated costs of sustained effort. In this framework, deadlines act as external constraints that help perfectionists limit open-ended striving and bring tasks to completion. An accompanying experiment tests the model’s prediction and finds that perfectionists are more likely to prefer deadlines. These results suggest that, in some cases, procrastination may reflect a structured strategy rather than a purely irrational failure of self-control. The second essay explores the phenomenon of Sophisticated Procrastination, challenging traditional models that attribute procrastination to naïveté. Instead, it proposes that even individuals who are aware of their tendency to delay may struggle to act on that awareness. Two experimental studies using a menu-choice framework examine how people choose task timings. In Study 1, participants preferred earlier deadlines when flexibility was available but shifted toward later options when required to commit, revealing a gap between intention and action. Study 2 identified diverse patterns of deadline preferences: while many participants actively avoided the latest possible deadline, their hesitation to commit to any specific deadline suggests a deeper tension rooted in uncertainty or discomfort with commitment. These findings provide early empirical support for Sophisticated Procrastination, indicating that self-awareness alone may not be sufficient to overcome procrastination. The third essay introduces the idea of Prosocial Procrastination, describing the tendency to delay tasks that benefit others, such as charitable activities, more than those with self-interested outcomes. Using two distinct experimental designs, one based on conjoint analysis and the other on single-attribute choice, the studies show that individuals are more likely to prefer longer deadlines when working for a charity than when working for themselves. These findings offer suggestive evidence for Prosocial Procrastination and contribute to the growing literature on the intersection of social preferences and time preferences.

Machine Learning Systems for Unsupervised Time Series Anomaly Detection

Mon, 01 Sep 2025 00:00:00 GMT

Machine Learning Systems for Unsupervised Time Series Anomaly Detection Alnegheimish, Sarah Modern assets – from launched satellites to electric vehicles – output dense, multivariate time series data that must be monitored for deviations from “normal” behavior. This monitoring task is referred to as time series anomaly detection. The current state of the industry still depends on fixed or heuristic thresholds that often drown operators in false alarms, and can miss the subtle, context-dependent faults that matter most. This thesis addresses unsupervised time series anomaly detection as an end-to-end problem, asking how we can learn, evaluate, and deploy models that judiciously flag anomalies while remaining intuitive to the end user. This thesis provides contributions in the form of both algorithms and systems. First, it introduces three models that enlarge the design space of unsupervised time series anomaly detection: TadGAN, which leverages adversarial reconstruction; AER, which unifies predictive and reconstructive objectives in a single hybrid score; and MixedLSTM, which explicitly incorporates interdependencies to improve anomaly detection in multivariate time series. We propose two range-based evaluation metrics that quantify detection quality over temporal intervals. Second, it presents our system Orion, which abstracts anomaly detection pipelines as directed acyclic graphs of reusable primitives, providing user-friendly APIs and enabling interactive visual inspection. Building on this infrastructure, OrionBench performs periodic, fully reproducible benchmarks, producing leaderboards that align research innovations with the needs of end users. Third, the thesis explores a new paradigm – foundation models for unsupervised time series anomaly detection – by formulating SigLLM , which employs large language models and time series foundation models for zero-shot anomaly detection via prompting and forecasting. This paradigm indicates a promising path to developing scalable models for anomaly detection. Finally, beyond evaluating our systems on publicly available datasets, we provide extensive experiments on two industrial case studies that demonstrate improved detection accuracy and practical usability of our system.

Techniques for Reliability and Robustness in Integrated Electronic and Photonic Systems

Mon, 01 Sep 2025 00:00:00 GMT

Techniques for Reliability and Robustness in Integrated Electronic and Photonic Systems Chakraborty, Uttara Reliability and robustness are key concerns in the development of novel electronic and photonic materials, devices, and systems. This thesis presents statistical and machine learning techniques for reliability analysis of heterogeneously-integrated systems, extraction of variations from photonic test structure measurements, making smart decisions about test configurations in the face of time and resource constraints, and robust design of photonic components. To estimate reliability model parameters from lifetime datasets where multiple underlying failure mechanisms are present, a differential evolution framework and a boundconstrained expectation maximization algorithm are developed; both these approaches significantly outperform the gradient-based L-BFGS-B algorithm. New schemes for strategic failure analysis on a subset of the failed units are presented, both for detecting the presence of a second failure mechanism and for improving two-mechanism reliability models. A regression-based protocol is also presented for optimally selecting reliability test conditions to verify physical failure mechanism models. A maximum-likelihood-estimation-based approach is demonstrated for the simultaneous extraction of waveguide index and thickness variations using integrated photonic direction couplers and Mach-Zehnder interferometers. Schemes are proposed for optimal selection of cut-back test structures and for propagation loss estimation with a Bayesian prior distribution for fiber-coupling error. Finally, a robust Bayesian optimization algorithm using a new tunable acquisition function is presented for photonic component design. The methods developed in this thesis are expected to be broadly applicable to a wide variety of electronic and photonic devices and systems.

Synthesis and Applications of Large-Area Monolayer Graphene

Mon, 01 Sep 2025 00:00:00 GMT

Synthesis and Applications of Large-Area Monolayer Graphene Wang, Zhien (Abigail) Graphene, renowned for its exceptional electrical, mechanical, and chemical properties, is a promising candidate for next-generation electronics, photonics, and biosensing. However, realizing its full potential depends critically on the ability to synthesize high-quality monolayer graphene. In this thesis, we present a robust chemical vapor deposition (CVD) approach for synthesizing large-area, adlayer-free, single-orientation graphene on Cu(111) foil and Cu(111) film/sapphire. A comparative analysis between these two substrates reveals critical differences in wrinkle density, grain size, and strain — offering insights for optimizing graphene growth. We further identify and characterize defective merging behavior in single-orientation graphene domains. Contrary to conventional assumptions, these merging regions contain permeable defects, revealing previously unrecognized limitations in using single-orientation stitched graphene as an impermeable barrier. To scale up production while reducing human error, we also develop an autonomous CVD platform with automated sample handling, growth and post-growth oxidation. This system enables high-throughput and reproducible graphene synthesis with minimal supervision. Building on these synthesis advances, we explore multiple applications of large-area monolayer graphene. We discover that graphene can promote interfacial oxidation of metals like aluminum and titanium during deposition, whereas metals such as nickel remain stable — a finding that informs the engineering of metal-graphene interfaces for electronic devices. In parallel, we explored diverse applications of graphene, including its role as a transparent, flexible electrode in organic solar cells, along with several collaborative efforts demonstrating its use as a sensor for cardiac microtissues, and as a tunable microheater in mid-infrared devices. Altogether, this work advances both the fundamental understanding and technological scalability of monolayer graphene, positioning it as a versatile platform for future applications across electronics, optoelectronics, and biointerfaces.

Mapping signaling networks and rapidly evolving genes in the developing Arabidopsis seed at single-nucleus resolution

Mon, 01 Sep 2025 00:00:00 GMT

Mapping signaling networks and rapidly evolving genes in the developing Arabidopsis seed at single-nucleus resolution Martin, Caroline A. Seeds are an exceptional evolutionary innovation that enables the conditional allocation of maternal resources to successfully fertilized ovules. During early development, seeds accumulate nutrients that are utilized either by the embryo or by humans who harvest seed crops for food, biofuel, and livestock feed. Moreover, the grains of maize, rice, and wheat provide approximately 60% of the calories consumed worldwide. Although seeds are a cornerstone for ecosystems and modern agriculture, fundamental aspects of their development are incompletely understood. In this thesis, I develop a transcriptional atlas of seed development using the model plant Arabidopsis thaliana to clarify the functional compartmentalization, diversity, and developmental dynamics of cell types in the seed. I focus my analyses on how seed cell types communicate with one another to ensure successful propagation, and how genetic conflicts in the seed may drive rapid evolution in specific cell types. After characterizing the extent of short, secreted peptide expression in specific seed cell types, I perform in silico screens to match potential peptide hormones with their receptors. In total, I show that the seed coat shows functional compartmentalization around the gateway for maternal resources into seeds, that seed genes differentially expressed in a maternal resource transfer structure are rapidly evolving, and that genes underlying brassinosteroid biosynthesis and response are expressed in adjacent tissues, among other findings. This thesis illuminates potentially new mechanisms for inter-tissue coordination and provides a transcriptional reference for future seed studies.

Optimization in Deep Learning: Structured, Realistic and Interpretable Learning for Decision-Making

Mon, 01 Sep 2025 00:00:00 GMT

Optimization in Deep Learning: Structured, Realistic and Interpretable Learning for Decision-Making Tsiourvas, Asterios In recent years, deep learning has emerged as a powerful tool for data-driven decisionmaking. However, its adoption in high-stakes applications is often constrained by challenges related to interpretability, fairness, and generalization in structured or complex environments. This thesis develops new optimization methodologies to enhance the realism, structureawareness, and interpretability of deep learning models in decision-making tasks. We begin, in Chapter 2, by addressing the challenge of optimizing trained neural networks for data-driven decision-making. Although neural networks can encode rich representations of preferences or outcomes, directly optimizing their outputs can be computationally intractable and often may produce unrealistic prescriptions. We introduce scalable algorithms that leverage the piecewise-linear structure of ReLU networks, reducing the original hard-to-solve mixed-integer program into tractable linear programs. To ensure realism, we introduce constraints that restrict decisions to lie on the data manifold. We then extend this framework to any differentiable neural network or MIP-expressible model and show that it scales for networks with millions of parameters. In Chapter 3, we focus on decision-making under observational data. First, we study personalized treatment recommendations under discrete treatments. We introduce the Prescriptive ReLU (P-ReLU) network, a piecewise-linear model that partitions the input space into polyhedral regions, assigning treatments uniformly within each, and that can be translated into an equivalent interpretable decision tree. We demonstrate that P-ReLU achieves strong prescriptive accuracy and accommodates structural/prescriptive constraints with ease. Next, we consider the problem of large language model (LLM) routing, where a query must be dynamically routed to the best model under competing metrics like accuracy and cost. We develop a causal, end-to-end approach that learns routing policies directly from logged observational data, minimizing directly decision-making regret. Finally, we tackle the problem of generating realistic, manifold-aligned counterfactual explanations. To address this problem, we present a MIP formulation where we explicitly enforce manifold alignment by reformulating the highly nonlinear Local Outlier Factor (LOF) metric as a set of mixed-integer constraints. To address the computational challenge, we leverage the geometry of the network and propose an efficient decomposition scheme that reduces the initial hard-to-solve problem into a series of significantly smaller, easier-to-solve problems. We further extend this framework to any differentiable neural network or MIP-expressible machine learning model. In Chapter 4, we focus on structured machine learning. We first address the problem of hierarchical time series forecasting, where predictions must be both accurate and consistent with the aggregation structure of the hierarchy. While prior methods rely on fixed projection matrices, we propose learning the optimal oblique projection directly from data. The proposed end-to-end approach jointly trains the forecasting model and projection layer, significantly improving accuracy and coherence. Next, we study the problem of creating a highly expressive, interpretable, and fair machine learning model. We propose Neural-Informed Decision Trees (NIDTs), a model that combines the predictive power of neural networks with the inherent interpretability of decision trees. NIDTs use axis-aligned splits on dataset features to form transparent decision paths, and at each leaf, apply a linear predictor based on both the original features and neural embeddings from a task-specific network to capture non-linearities. To generate NIDTs, we develop a decomposition training scheme that supports direct integration of fairness constraints via a constrained convex optimization problem solved at each leaf. Finally, in Chapter 5, we address fairness and efficiency in emergency department (ED) operations, where prolonged length of stay (LOS) has been linked to adverse outcomes such as increased mortality and higher risk of hospital-acquired infections. We focus on the patient prioritization and placement aspects of ED operations to improve throughput and reduce wait times. We propose a novel MIP predictive-prescriptive framework that decomposes predicted LOS into actionable components, enabling a more granular and operationally meaningful model of ED dynamics. Fairness considerations are explicitly incorporated into the formulation. To address uncertainty, we introduce a sampling-based solution approach. Our method increases ED throughput by 50–100% and reduces average wait time by 50–75%, depending on current utilization levels, while achieving near-optimal performance compared to a clairvoyant oracle. This work was conducted in collaboration with a major U.S. academic medical center. To facilitate practical implementation, we also design an interpretable metamodel that approximates the predictive-prescriptive algorithm with high fidelity. Together, these contributions provide a unified perspective on deep learning for reliable decision-making, grounded in optimization and encompassing interpretability, structure-awareness, and causal reasoning, well-suited for high-stakes operational environments.

Reverberation Mapping of Supermassive Black Holes using Machine Learning

Mon, 01 Sep 2025 00:00:00 GMT

Reverberation Mapping of Supermassive Black Holes using Machine Learning Lewin, Collin Accreting supermassive black holes at the centers of galaxies, known as active galactic nuclei (AGN), offer a unique window into the physics of accretion and feedback that shape galactic evolution. Yet, the small spatial scales of these regions remain inaccessible to direct imaging. Reverberation mapping circumvents this limitation by using time delays between correlated emission at different wavelengths to infer physical size scales. While X-ray reverberation probes the innermost accretion flow, continuum reverberation in the UV, optical, and infrared (UVOIR) traces reprocessing by the accretion disk and broad-line region (BLR). In this thesis, I develop and apply frequency-domain timing techniques based on Gaussian Process (GP) regression to study AGN reverberation across X-ray and UVOIR regimes. By modeling the empirical variability of AGN light curves with GPs, I interpolate onto an evenly sampled time grid, enabling robust estimation of Fourier-resolved time lags despite irregular sampling or large time gaps. I apply this method to NuSTAR observations of the Narrow-line Seyfert 1 galaxy Ark 564, introducing a multi-task GP model that jointly learns kernel hyperparameters across light curves. This enables the first simultaneous modeling of lag and flux spectra from both NuSTAR and XMM-Newton using a relativistic reverberation model to constrain black hole mass and disk properties. Recent reverberation campaigns with the Neil Gehrels Swift Observatory and ground-based telescopes have revealed significant discrepancies between observed inter-band lags and standard accretion disk theory. These include unexpectedly large lag amplitudes (the “accretion disk size problem”) and weak correlations between X-ray and UV/optical light curves. To investigate further, I analyze recent Swift campaigns of Mrk 335 and Mrk 817 using GP-based frequency-resolved lag analysis. In both sources, standard disk lags appear only on short timescales (high frequencies), while longer-than-expected lags dominate at low frequencies. These lag excesses are consistent with reprocessing at larger radii, similar to the BLR. Mrk 817 offers a rare opportunity to connect the inner and outer accretion flow: I detect the first simultaneous measurement of X-ray and UVOIR lags, effectively mapping the full disk. These lags vary significantly over the campaign, with longer delays during periods of stronger X-ray obscuration. This suggests that a disk wind may modulate the observed lags by introducing additional reprocessing and/or blocking ionizing flux from reaching more-distant material. To test this obscuration effect across a population, I conduct the first statistical study of UV/optical lag excess versus physical parameters across the Swift campaigns. The results show that the lag excess is driven entirely by obscured AGN, while the lags of unobscured sources are, on average, consistent with thin-disk theory. Regression analysis reveals that X-ray column density explains over 80% of the variance in lag excess. As for the X-ray/UV connection, obscured AGN also tend to show weaker correlations and more variable lags, suggesting that line-of-sight absorption not only contributes additional reprocessed emission that extends the UV/optical lags, but may also decouple or delay the X-ray and UV variability. To make GP-based time series analysis accessible to the community, I developed the STELA Toolkit, a fully documented Python package for computing frequency-domain data products using GPs. I also benchmark GP performance against other interpolation methods, including state-of-the-art transformers, paving the way for scalable, ML-enabled timing analysis in the era of time-domain surveys like Vera Rubin.

Learning Nonlinear Dynamics: Methods and Applications

Mon, 01 Sep 2025 00:00:00 GMT

Learning Nonlinear Dynamics: Methods and Applications Rossi, Baptiste T. Accurate modeling of dynamical systems through differential equations is essential for scientific prediction and prescriptive control. Traditional model development, which relies on expert knowledge, parameter fitting and validation, is often iterative, time-consuming, and complicated by real-world data complexities such as noise and missing observations. This thesis addresses these challenges by developing robust, scalable, and interpretable methods for learning nonlinear ordinary differential equations (ODEs) and partial differential equations (PDEs) directly from data, with a particular emphasis on applications in fluid dynamics. In Chapter 2, we introduce a novel methodology for learning arbitrary nonlinear ODEs using collocation methods combined with interpolation. This approach demonstrates enhanced robustness to noise and significant computational speed-ups compared to classical system identification techniques, including the popular SINDy framework. It also provides a constructive method for reconstructing unobserved system components, making it applicable to partially observed systems, and offers theoretical guarantees on accuracy traditionally absent in strong-form identification. In Chapter 3, we combine the approach from Chapter 2 with sparse regression to derive sparse ODEs from data, demonstrating enhanced robustness to observational noise. Our method shows improved performance in recovering the true structures and coefficients on canonical benchmark tests under significant noise, while the performance of traditional surrogate methods deteriorates even with minimal noise. In Chapter 4, we extend this methodology to Partial Differential Equations (PDEs) using the method of lines, addressing issues related to data scale and interpolation ill-posedness. With a focus on Computational Fluid Dynamics (CFD), we show that our method goes beyond recovering complex nonlinear PDEs, such as the Navier-Stokes equations, from simulation data. The method can also be used as an a-posteriori indicator of simulation quality, providing insights into the effective PDEs represented by a given simulation, and pinpointing error-generating areas to inform adaptive mesh techniques. Lastly, in Chapter 5, we introduce a novel data-driven framework for modeling turbulent phenomena, a long-standing challenge in aerospace and climate science. Our approach addresses the Reynolds-Averaged Navier-Stokes (RANS) closure problem, which involves modeling the unobserved eddy viscosity field. We tackle two interconnected inverse problems: reconstructing the eddy viscosity from flow data and discovering its governing partial differential equations (PDEs), thereby proposing a new pathway to uncover new or refined RANS closure models directly from high-fidelity simulations. This chapter establishes a tractable baseline using a composite loss function, which we evaluate on canonical turbulent flows. Our results demonstrate that while the approach can recover governing equations when the ground truth eddy viscosity is known, significant challenges remain due to noise and numerical errors. We conclude that a more advanced reconstruction methodology is essential for robustly discovering these models, underscoring the potential of this data-driven approach and identifying critical areas for future research.

The Electronic Compressibility of Rhombohedral Graphene Multilayers

Mon, 01 Sep 2025 00:00:00 GMT

The Electronic Compressibility of Rhombohedral Graphene Multilayers Aronson, Samuel H. In condensed matter systems, energy bands with narrow dispersion frequently host correlated electronic phases that arise from strong Coulomb interactions. When these bands also have concentrated Berry curvature, the correlated phases may be topologically non-trivial. The low-energy bands of rhombohedral graphene multilayers possess both of these ingredients, making this a promising class of materials in which to search for correlated topological electronic ground states. This thesis describes our electronic compressibility measurements on rhombohedral graphene multilayers, with a particular focus on the pentalayer system (R5G). We utilize a planar capacitance technique that probes the thermodynamic density of states and enables us to extract energy gaps of incompressible phases. We observe a variety of correlated electronic phenomena including half and quarter metals, layer antiferromagnetism, correlation-driven Chern insulators, and thermodynamic signatures of potential Wigner crystallization. We also study the electronic compressibility of R5G aligned to a hexagonal boron nitride (hBN) substrate to form a moiré superlattice. Motivated by the recent discovery of the fractional quantum anomalous Hall effect in this system when the electrons are pushed away from the moiré interface by an external electric displacement field, we study the opposite moiré-proximal limit, in which the superlattice potential is considerably stronger. We observe integer and fractional Chern insulator states that persist down to low magnetic fields in addition to numerous trivial and topological charge density waves. We map out a phase diagram that is highly sensitive to both displacement and magnetic fields, establishing the R5G-hBN superlattice as a highly-tunable system for studying the interplay between intrinsic band topology and strong lattice effects.

Hadronic Structure with Classical and Quantum Computing

Mon, 01 Sep 2025 00:00:00 GMT

Hadronic Structure with Classical and Quantum Computing Avkhadiev, Artur Calculations in lattice quantum chromodynamics (QCD) — presently the only known systematically improvable approach to describe the strong nuclear force in the nonperturbative regime from first principles — are playing an increasingly important role in revealing how hadrons emerge from the interactions of the underlying degrees of freedom: quarks and gluons. With computational and theoretical advances, more fruitful connections have emerged between lattice QCD and phenomenology, and the field is now well into a stage ripe for deriving tighter constraints on hadronic structure through joint analyses of numerical lattice QCD results with experimental data. This thesis summarizes lattice QCD calculations of the Collins-Soper (CS) kernel: a nonperturbative function whose inclusion in joint analyses has the potential to advance the study of multidimensional hadronic structure. The CS kernel is an anomalous dimension of transverse-momentum-dependent (TMD) distributions describing a three-dimensional structure of ultrarelativistic hadrons as a function of quark-gluon momenta collinear with and transverse to the hadron's motion. Constraints on the CS kernel at nonperturbative transverse-momentum scales are instrumental to relate TMDs across scales and processes. The kernel differs for quark and gluon TMDs, but is otherwise universal. This thesis presents the first lattice QCD determination of the quark CS kernel with systematic control over operator mixing, quark mass, and lattice discretization, and a proof-of-principle lattice calculation of the gluon CS kernel providing the first nonperturbative constraints on this quantity. Additionally, this thesis summarizes exploratory studies on how Hamiltonian calculations — realized with quantum-computer simulations and tensor networks — may be combined with conventional Monte Carlo calculations based on Lagrangian formulations in Euclidean space. These studies examine how constructions of interpolating operators, used in conventional calculations to map between the vacuum and a ground state of interest, may be optimized in Hamiltonian calculations to increase overlap with the target state. Results, limited to the Schwinger model, support further investigations of this approach in theories more closely resembling QCD as quantum-computing and tensor-network technologies continue to mature.

A Charcuterie Platter of QCD Matter

Mon, 01 Sep 2025 00:00:00 GMT

A Charcuterie Platter of QCD Matter Sun, Zhiquan One of the greatest current challenges in theoretical high energy physics is to understand the dynamics of Quantum Chromodynamics (QCD). In this thesis, I address a variety of questions in QCD using Effective Field Theory (EFT). The first question deals directly with the observed phenomenology of QCD: How can we use EFT to disentangle the complicated three-dimensional dynamics of how quarks and gluons, the fundamental degrees of freedom of QCD, combine to form the observed bound states in nature called hadrons? I initiate a new formalism using Heavy Quark Effective Theory to study this dynamical process known as hadronization. I shed new light on the transverse momentum-dependent fragmentation process of heavy (charm and bottom) quarks by making use of the fact that heavy quarks with masses much larger than the strong interaction scale decouple from the rest of the hadronization cascade. I also present exciting heavy quark phenomenology at existing colliders and the upcoming Electron-Ion Collider. The second question investigates the field theory structure of QCD: What can we learn about the nonperturbative structure of the quantum field theory through the abstruse emergent phenomenon in QCD called “confinement”, which traps quarks and gluons inside hadrons? I study a class of cleverly constructed observables known as energy correlators by using fieldtheory based methods to determine the leading nonperturbative contribution, and examine the universality of the nonperturbative matrix element that gives rise to this contribution. I also show that including the nonperturbative contribution has a significant impact on the extraction of the strong coupling constant, a fundamental parameter of the Standard Model, using tools such as factorization and resummation from EFT. Last but not least, the final question explores the underlying symmetry properties of QCD and its potential completions: How robust is the axion solution to the strong CP (ChargeParity) problem, and what are some of its implications beyond the realm of QCD? I examine the axion quality problem in post-inflationary QCD axion models with different symmetry properties and identify a new tension with standard cosmology. I further show that the axion string-domain wall dynamics is more complicated than commonly expected, undermining the reliability of a unique mass prediction for axion dark matter in post-inflationary models. I showcase the importance of considering both high-energy extensions and the EFT at low energy, and uncover new complexity of the axion solution to the strong CP problem.

Learning from pre-pandemic data to design and test future-proof therapeutics

Mon, 01 Sep 2025 00:00:00 GMT

Learning from pre-pandemic data to design and test future-proof therapeutics Gurev, Sarah Effective pandemic preparedness relies on predicting immune-evasive viral mutations to enable early detection of variants of concern and design vaccines and therapeutics that are resilient to future viral evolution. However, current strategies for viral evolution prediction are not available early in a pandemic and have limited predictive power – experimental approaches require host polyclonal antibodies and existing computational methods draw heavily from current strain prevalence. In addition, vaccines and therapeutics have been designed with an eye towards past or circulating variants, not towards future evolution. To address these challenges, we developed EVEscape, a generalizable framework that integrates fitness predictions from a deep generative model of evolutionary sequences with biophysical and structural information. EVEscape quantifies the immune escape potential of viral strains at scale and is applicable before surveillance sequencing, experimental scans, or 3D structures of antibody complexes are available. We demonstrate that EVEscape, trained on sequences available prior to 2020, performs as accurately as high-throughput experimental scans at anticipating pandemic variation for SARS-CoV-2 and is generalizable to other viruses including Influenza A virus, HIV, and understudied viruses with pandemic potential such as Lassa and Nipah. We investigate both alignment-based and protein language models to explore the best model of mutation effects across pandemic-threat viral families. We demonstrate the utility of EVEscape in three critical applications: (1) Surveillance efforts flagging high escape SARS-CoV-2 variants from their first appearance (2) Design of panels of viral antigens that mimic future viral variants for early, proactive evaluation of the future protection of vaccines and therapeutic; and (3) Design of a pan-sarbecovirus nanoparticle-based vaccine capable of eliciting broad, long-lasting protection against sarbecoviruses, including future variants. This three-pronged approach represents a paradigm shift in pandemic preparedness, offering a novel strategy to preemptively address viral families with pandemic potential and significantly bolster global prevention efforts.

Topics in quantum information theory and quantum many-body physics

Mon, 01 Sep 2025 00:00:00 GMT

Topics in quantum information theory and quantum many-body physics Balasubramanian, Shankar In this thesis we present two results relating to the intersection between quantum information theory and quantum many-body physics. The first pertains to quantum algorithms, where few computational problems are believed to exhibit exponential separation between quantum and classical performance. For those that are, natural generalizations remain elusive. One speedup that has especially resisted generalization is the use of quantum walks to traverse the welded tree graph, due to Childs, Cleve, Deotto, Farhi, Gutmann, and Spielman. We show how to generalize this to a large class of hierarchical graphs in which the vertices are grouped into “supervertices” that are arranged according to a d-dimensional lattice. Supervertices can have different sizes, and edges between supervertices correspond to random connections between their constituent vertices. The traversal time of quantum walks on these graphs are related to (a) the existence of small subspaces within which the quantum walk evolves and (b) the localization properties of the quantum walk within these subspaces. We find examples of hierarchical graphs that yield provable speedups over classical algorithms ranging from superpolynomial to exponential, depending on the underlying dimension and the random graph model. We also discuss how to relax criterion (a) to the existence of a small and approximate subspace by using techniques from graph sparsification. The second result pertains to fault-tolerant quantum memories. Storing a qubit in a noisy environment is crucial for developing full-scale quantum computers. While constructions of fault-tolerant quantum memories exist, they often assume that quantum operations are not local and assisting classical computation operates instantaneously and noislessly. In particular, constructing a topological quantum memory below four dimensions with local quantum and classical operations that is fault-tolerant under both quantum and classical noise is an open problem. We construct a local quantum memory for the 2D toric code using ideas from the classical cellular automata of Tsirelson and Gács. Our memory preserves a logical state for exponential time in the presence of both classical and quantum noise below a constant noise rate. While our 2D quantum memory is built from operations that depend on space and time, we construct a fault-tolerant quantum memory in 3D using stacks of 2D toric codes that can be built with time-independent operations.

Quantum Matter in the Era of Generalized Symmetries

Mon, 01 Sep 2025 00:00:00 GMT

Quantum Matter in the Era of Generalized Symmetries Chatterjee, Arkya The discovery of generalized symmetries has led to powerful new insights into quantum matter. They have been used to classify new families of quantum phases, place constraints on phases realizable in a given physical system, and conceptually unify seemingly disparate phenomena. In many ways, they prove just as powerful as traditional symmetries at organizing and constraining the theories that describe quantum matter. In this thesis, we attempt a unification of such constraints by developing a holographic correspondence between (generalized) symmetries and topological orders, called the Sym/TO correspondence. For any (finite internal) symmetry of a quantum system in d (spatial) dimensions, we associate with it a unique topological order in d + 1 dimensions, called its Symmetry Topological Order (SymTO). We devise an operator algebraic recipe to compute the SymTO data for any lattice spin model, demonstrating it in a number of examples. We then use the SymTO to classify possible quantum phases allowed by the symmetry—we call this a generalized Landau paradigm. Besides classifying phases, we also identify constraints on the phase transitions between them using a SymTO-resolved modular bootstrap. We test this framework in a quantum spin chain with non-invertible symmetries. We discover a new Kramers-Wannier-like duality and a rich phase diagram including a noninvertible symmetry-enriched incommensurate phase. The translation symmetry of the spin chain has a nontrivial interplay with the lattice Kramers-Wannier duality, which matches the anomaly of the corresponding non-invertible symmetry in the low-energy effective field theory. Finally, we explore such unusual anomaly-matching mechanisms in more detail in the context of the chiral anomaly of a single massless Dirac fermion, demonstrating a novel lattice realization of chiral symmetries and their anomaly.

Systems Materials Design of Ordered Nanocomposite Assemblies

Mon, 01 Sep 2025 00:00:00 GMT

Systems Materials Design of Ordered Nanocomposite Assemblies Thrasher, Carl James The ability to precisely organize matter across multiple length scales is a central challenge in modern materials science. In this dissertation, I develop a systems materials design approach to engineer hierarchically structured nanocomposite assemblies, integrating molecular recognition, supramolecular chemistry, colloidal assembly, and bulk processing into unified material platforms. At the molecular and nanoscale, I investigate how multivalent supramolecular interactions can be rationally programmed by controlling the architecture of polymer binders grafted to nanoparticle surfaces. Through systematic variations in polymer topology, recognition group density, and scaffold geometry, I demonstrate how polymer design dictates the thermodynamic strength and multivalency of nanoparticle superlattice assembly, enabling precise control of thermal stability, crystallographic symmetry, and collective bonding behaviors in massively multivalent systems. Building on these design principles, I develop a colloidal metallurgy framework to process selfassembled nanoparticle superlattices into dense macroscopic polycrystalline solids while preserving nanoscale order. By systematically studying the interplay of temperature, pressure, and time during colloidal sintering, I elucidate mechanisms of densification, defect evolution, and grain growth unique to colloidal systems, establishing processing–structure relationships that parallel but fundamentally diverge from atomic sintering. Finally, I extend these concepts to create stretchable nanocomposite supercrystals, embedding supramolecularly assembled superlattices into elastomeric matrices via co-engineered polymer chemistries that enable hierarchical strain transduction. These materials combine the nanoscale precision of supercrystals with mechanical robustness, reconfigurability, and stimuli-responsive optical properties, illustrating a scalable pathway to multifunctional metamaterials. Collectively, this work demonstrates how a systemslevel integration of molecular design, colloidal assembly, and bulk processing enables new paradigms for the synthesis of hierarchically ordered, functional nanocomposites.

The novel roles of BCL6 and BATF3 in regulating human CD8⁺ T cell dysfunction

Mon, 01 Sep 2025 00:00:00 GMT

The novel roles of BCL6 and BATF3 in regulating human CD8⁺ T cell dysfunction Traunbauer, Anna Katharina Reduced effector function and elevated inhibitory receptor expression are hallmarks of exhausted CD8⁺ T cells, yet the underlying molecular and epigenetic drivers remain incompletely defined. Here, we developed an in vitro repeated stimulation model to recapitulate features of human CD8⁺ T cell dysfunction and delineate transcriptional and epigenetic landscapes. Our analyses revealed that BCL6 and BATF3 are robustly upregulated in dysfunctional CD8⁺ T cells, with ATAC-seq demonstrating enhanced chromatin accessibility at their gene loci. Transcription factor footprinting shows increased BATF3 motif occupancy in chronically stimulated cells and integrative multi-omic analysis combining footprints, open chromatin regions, RNA-seq and ChIP-seq data revealed that putative BATF3 target genes may include master regulators of exhaustion. Moreover, overexpression of BCL6 or BATF3 markedly upregulates TIM-3 expression and suppressed cytokine release, establishing their capacity to induce T cell dysfunction. We further validated these findings ex vivo in antigen-specific CD8⁺ T cells from patients with advanced melanoma, as well as HCV and HIV infections, where cells were enriched for BCL6^high and BATF3^high subsets co-expressing canonical exhaustion markers such as PD-1, TIM-3 and CD39. Notably, Single-cell RNA sequencing of HIV-specific CD8⁺ T cells identified a distinct BCL6^high PD1⁻ progenitor population that gives rise to two distinct subsets via divergent differentiation trajectories: one branch generates effector-like BCL6^high PD1⁺ cells, whereas the other produces BCL6^high PD1⁺ cells that retain an exhaustion gene signature alongside partial memory-like feature. Collectively, these findings identify BCL6 and BATF3 as key mediators of human CD8⁺ T cell dysfunction and illuminate novel transcriptional and epigenetic pathways that may be leveraged for therapeutic intervention in cancer and chronic viral infections.

Aspects of Nonperturbative Heavy Quark Physics

Mon, 01 Sep 2025 00:00:00 GMT

Aspects of Nonperturbative Heavy Quark Physics Lin, Joshua The properties of charm and bottom quarks are an interesting corner of Quantum Chromo-Dynamics (QCD) due to the fact that their masses are much heavier than the typical QCD interaction energy ΛQCD. Due to this scale separation, it is possible to describe these heavy quarks by Effective Field Theories (EFTs) that simplify their equations of motion, make explicit additional symmetries that only appear for heavier quark masses, and simplify the theoretical calculations required for predictions. By discretising these EFTs in a lattice regularisation, nonperturbative calculations of observables of interest become possible. This thesis presents progress towards systematically controlled calculations of two such observables: the Spectator Effect contributions to the inclusive decay rates of b-hadrons, and the real-time dynamics of fermions propagating in a thermal medium. Standard EFT calculations in Lattice-QCD proceed by expressing observables as sums over perturbatively computed Wilson coefficients and nonperturbative matrix elements that can be calculated by path integral monte-carlo methods. Though it is possible to carry out this procedure within a regulator-independent renormalization scheme, in practice almost all such decompositions are computed in the modified minimal subtraction scheme MS which is only defined for the dimensional regulator (DR), due to its simplicity. Computing such observables therefore requires a matching between lattice regularised operators and operators renormalized in MS. In Chapter 2, both the dimensional regulator (DR) and the lattice regulator are reviewed, with a particular emphasis on techniques needed for calculations carried out in later sections. An interesting subtelty in DR is the need to introduce d-dimensional counterparts to the Dirac γ-matrices, which a-priori are only well defined in integer number of dimensions. This analytic continuation is of practical importance as it introduces additional Evanescent Operators (Sec. 2.1.4) that have physical consequences. In Sec. 2.1.5, traces of d-dimensional γ-matrices were related to Tutte polynomial evaluations [4], presenting a new graph-theoretic interpretation of the dimensionally regulated γ-matrices. One strategy of renormalizing lattice-regulated operators into MS involves first renormalizing into a regulator independent scheme, before perturbatively matching between the regulator independent scheme and MS. In Chapter 3, regulator independent position-space (X-space) schemes for renormalizing operators defined in the leading order Heavy Quark Effective Theory (HQET) are proposed [3]. Compared to other regulator independent renormalization schemes such as RI-xMOM, X-space schemes have the benefit that they are gauge invariant. The next to leading order matching calculations between X-space and MS are presented for heavy-light and heavy-light-light multiplicatively renormalizable operators, as well as ∆Q = 0 and ∆Q = 2 four quark operators relevant for heavy hadron decays and mixing, where Q refers to the static quark number. Due to their heavy masses, hadrons containing heavy quarks decay via the weak nuclear force. Experimental measurements of these lifetimes provide precision determinations of the fundamental parameters of the Standard Model. The Heavy Quark Expansion expresses the inclusive lifetimes of heavy hadrons in terms of matrix elements of HQET operators of increasing dimension. The Spectator Effects are contributions due to the ∆Q = 0 four-quark operators, where the light quark degrees of freedom within a heavy hadron participate in the decay. In Chapter 4, a Lattice-QCD determination of the static decay constant f HQET B and the isospin-nonsinglet portion of the Spectator Effect matrix elements for heavy-light mesons is presented. Fits of bare matrix elements were performed for three different lattice spacings, and renormalized with the schemes proposed in Chapter 3 before a continuum limit is taken. Due to the heavy masses mQ of the heavy quarks, it is possible to find temperatures T approximately satisfying a hierarchy ΛQCD ≪ T ≪ mQ. At these temperatures, QCD undergoes a deconfinement transition into the Quark-Gluon-Plasma (QGP) phase where the light degrees of freedom are no longer confined, and instead screen the long-range colour forces. The heavy quarks however are not thermalised, and act as probes of the QGP. Further understanding of the QGP requires first principles simulations of the heavy quark dynamics at finite temperature, however such calculations are difficult due to the enormous size of the Hilbert space. Variational approximations of the Hilbert space encode wavefunctions within a few parameters, and provide a practical method to simulate many particle systems. As a testcase, the variational approach was applied for the first time to simulate fermions at finite temperature in a simple QFT: the 1+1d U(1) gauge theory known as the massive Schwinger model. Both the real-time dynamics of string like states, and the properties of the thermal state were studied, and such variational methods are shown to be promising approaches to the more difficult case of a heavy quark effective theory in QCD.

Probing the Nonperturbative Physics of QCD with Normalizing Flows and a moderate number of Pions

Mon, 01 Sep 2025 00:00:00 GMT

Probing the Nonperturbative Physics of QCD with Normalizing Flows and a moderate number of Pions Abbott, Ryan William Quantum Chromodynamics (QCD) is a cornerstone of the standard model of particle physics, and the best known theory of strong nuclear interactions. The only known systematically improvable ab-initio method for accessing the nonperturbative physics of QCD is Lattice QCD is, and this thesis presents two advances in our understanding QCD using lattice-based methods. The first is a calculation using many-pion systems to map out the entire zero temperature, nonzero isospin density region of the QCD phase diagram. The calculation uses novel methods for working with many-pion systems that enables working with thousands of pions, and furthermore provides rigorous constraints on the baryon-dense region of the QCD phase diagram. The second is an application of methods from machine learning (namely normalizing flows) in order to accelerate sampling. This approach has the promise of eliminating issues such as critical slowing down, as well as introducing novel tools and methods that enable methods of calculation that would be possible otherwise.

Limits of QCD

Mon, 01 Sep 2025 00:00:00 GMT

Limits of QCD Gao, Anjie This thesis explores the fundamental kinematic limits of Quantum Chromodynamics (QCD), including the soft, collinear, and Regge limits, using soft-collinear effective theory (SCET). We begin by studying transverse momentum dependent (TMD) physics in semi-inclusive deep inelastic scattering (SIDIS), which probes the small transverse momentum regime arising from the soft and collinear limits of QCD. We derive all-order factorization theorems for azimuthal asymmetries in SIDIS at next-to-leading power (NLP). We also propose a new angular observable, q_∗, for probing TMD dynamics at the future Electron-Ion Collider (EIC), which enables an order-of-magnitude improvement in experimental resolution while retaining sensitivity to TMD distributions. Next, we apply the TMD formalism to a class of observables known as energy correlators. We study the transverse energy-energy correlator (TEEC) in the back-to-back limit, a dijet observable at hadron colliders, and the three-point energy correlator (EEEC) in the coplanar limit, a trijet observable at lepton colliders. For both observables, we derive allorder factorization theorems and resum large logarithms to next-to-next-to-next-to-leading logarithmic (N3LL) accuracy. Finally, we analyze the Regge limit of 2 → 2 QCD amplitudes. By factorizing these amplitudes into collinear jet and soft functions and studying their rapidity evolution, we define Regge-like anomalous dimensions in a gauge-invariant manner. At the level of the exchange of two Glauber gluons in the t-channel, we recover the BFKL equation from a purely collinear perspective. Extending to three-Glauber exchange, we derive the first closed-form renormalization group equations for Regge cut contributions in several nontrivial t-channel color representations, providing a systematic method for organizing non-planar QCD amplitudes at high energy.

Determining the Molecular Underpinnings of Iron Homeostasis in Human Cells

Mon, 01 Sep 2025 00:00:00 GMT

Determining the Molecular Underpinnings of Iron Homeostasis in Human Cells Lee, April Precise regulation of nutrient availability is crucial for cellular function and survival. Iron, in particular, is tightly regulated as it serves as an essential cofactor for numerous enzymes but can catalyze the formation of toxic radicals at elevated levels. To maintain the necessary cytoplasmic iron concentration, cells store excess iron in large proteinaceous cages called ferritin and, when available iron levels fall, they degrade these cages, liberating the stored iron for use. This thesis focuses on the molecular mechanisms underlying cellular iron sensing, as well as the molecular interactions supporting regulated ferritin degradation and subsequent iron release. Specifically, this work interrogates the protein interactions involved in ferritinophagy, a form of selective autophagy that leads to the lysosomal degradation of ferritin. Extending prior work that identified key components supporting ferritinophagy, including the selective autophagy receptor protein NCOA4 and its cognate autophagosomal receptor GATE16, experiments described here uncover the molecular contacts between these proteins. I found that NCOA4 bears two short linear motifs that each bind to GATE16 with weak affinity. However, these binding motifs are highly avid and, in concert, support high-affinity binding of NCOA4 to oligomerized GATE16. I further describe that ferritin degradation in cultured human cells relies on the contacts I identified biochemically. Moreover, I found that iron decreases NCOA4’s affinity for GATE16, providing a plausible mechanism for irondependent regulation of ferritinophagy. Taken together, this work suggests a general mechanism by which selective autophagy receptors can distinguish between inactive monomeric GATE16 and the active oligomerized forms that primarily drive autophagy. In related studies, I have biochemically probed the NCOA4•ferritin interface, with these experiments suggesting a novel function of NCOA4 in modulating ferritin cage structure – either through cage dismantling or through the formation of higher order structures. Taken together, these studies further define the molecular mechanisms by which NCOA4 aids cells in maintaining iron homeostasis, and they provide the requisite reagents for future work aimed at building a unified model for how mammalian cells regulate this vital but toxic metal.

Designing Electrocatalysts for the Production and Oxidation of Liquid Fuels

Mon, 01 Sep 2025 00:00:00 GMT

Designing Electrocatalysts for the Production and Oxidation of Liquid Fuels Zheng, Daniel J. With the ever-rising CO₂ levels in the atmosphere, it is paramount to cease reliance on fossil fuels to meet global energy demands. While the cost of electricity from renewable sources, such as solar and wind, continues to decrease and has even fallen below that of fossil fuels since 2014, these renewable energy sources suffer from intermittency, potentially causing shortages at peak demands. Thus, methods to store or economically use excess renewable energy are needed for full decarbonization. One promising avenue is to store the excess generated electrical energy in chemical bonds, creating molecules and materials with industrial or energy storage utility. In this proposed scheme, the renewable electricity would be used to electrochemically convert earth-abundant molecules into value-added chemical or fuels. These generated products could then be utilized as feedstocks in industrial applications or as a fuel source to generate electricity when needed by transforming back into their earth-abundant forms. Central to transforming earth-abundant molecules into value-added chemicals or fuels is the oxygen evolution reaction (OER), which is found in nearly every process. The plentiful nature of OER’s main reactant, water, and moderate thermodynamic potential of 1.23 V vs. the reversible hydrogen electrode, make OER an ideal reaction to pair with other transformations. However, the slow kinetics of OER significant hinder the efficiency of these processes. As such, discovering new OER catalysts with high activity and stability would have wide-spread impacts. On the other hand, one of the most promising renewable fuel sources is methanol, which boasts about 3 times the energy density of hydrogen and can be used as an alternative to hydrogen in proton exchange membrane fuel cells. However, the sluggish kinetics of the methanol oxidation reaction (MOR), even with current state-of-the-art noble metal catalysts causes direct methanol fuel cells to reach an efficiency of <40%, limiting their practical usage. While significant research has been invested in discovering new MOR electrocatalysts, PtRu has reigned for 5 decades, highlighting the need for a true breakthrough. In this thesis, electrocatalysts for OER and MOR are examined in depth. For OER, metal-hydroxide organic frameworks (MHOFs), a promising new class of hybrid organic-inorganic materials with potential to mimic the superior functionality of enzymes, are studied. Operando vibrational and absorption spectroscopy methods are used to characterize the degradation mechanisms and lattice oxygen exchange capacity as a function of the linkers. Using such knowledge, defects are engineered into the MHOF that increase both the activity and stability compared to the pristine material. Furthermore, the traditionally reported MOR mechanism is studied using isotope-labeled reactants and operando mass spectrometry. These experiments revealed that, in contradiction to typically accepted mechanisms, the C-O bond in methanol can be cleaved during MOR, with the resulting CO₂ molecule containing two water-derived oxygen atoms, opening a new paradigm for MOR catalyst design. Driven by the need to discover new materials at scale, a fluorescence-based OER catalyst screening method is developed that can screen an entire composition space simultaneously. In addition, an AI-driven, automated platform for screening a high-dimensional multimetallic space for MOR is presented.

Bridging the Gap: From Artificial Intelligence and Optimization Theory to Action

Mon, 01 Sep 2025 00:00:00 GMT

Bridging the Gap: From Artificial Intelligence and Optimization Theory to Action Petridis, Periklis S. Despite significant theoretical advances in Operations Research (OR) and Artificial Intelligence (AI), a persistent gap remains between these developments and their practical implementation in real-world settings. Despite significant progress in these fields, many OR and ML approaches struggle to scale to realistic problem sizes, lack robustness to uncertainty, or fail to address implementation constraints faced by practitioners in industry. Through four distinct works conducted in collaboration with industry partners, this research demonstrates how methodological advancements can bridge this theory-practice divide while maintaining rigorous theoretical foundations and guarantees. In the first part, we focus on optimization methodologies that scale traditional OR approaches to handle real-world problem sizes and uncertainty. In Chapter 2, we develop a stochastic Benders decomposition scheme for large-scale network design problems, a class of problems ubiquitous in logistics, transportation, and energy sectors. By incorporating sampling techniques within the decomposition framework, we achieve deterministic optimality guarantees while reducing computational costs, enabling solutions for networks with 700 nodes—an order of magnitude larger than previously tractable instances—while achieving optimality gaps of 5-7% compared to 16-27% for traditional deterministic approaches. In Chapter 3, we present a holistic framework for industrial decarbonization, developed with a major phosphate producer planning to quadruple energy consumption while transitioning to renewable sources. Our robust optimization approach combines strategic capacity expansion planning over a 25-year horizon with adaptive operational models, providing 95% reliability guarantees while balancing solar and wind integration with battery storage to meet a projected 12 TWh annual demand. In the second part, we shift our focus to developing AI systems that address the unique challenges of medical data abstraction and clinical decision support. In Chapter 4, we address the challenge of automating clinical data abstraction from electronic health records, collaborating with the Society of Thoracic Surgeons to populate their Adult Cardiac Surgery Database. Our AI pipeline combines 31 models per target variable with a two-tiered quality control system, achieving over 99% accuracy while automatically extracting 43-50% of registry variables, demonstrating how AI can dramatically reduce manual abstraction burden while maintaining clinical standards. In Chapter 5, we extend this healthcare AI focus by developing xHAIM (Explainable Holistic AI in Medicine), which addresses the limitations of current clinical AI systems in handling extensive patient records, providing interpretability, and incorporating medical knowledge. Through semantic similarity techniques and generative AI, xHAIM improves predictive performance while generating clinically grounded explanations that enhance trust and adoption by healthcare practitioners.

Multi-species genome-wide CRISPR screens identify conserved suppressors of cold-induced cell death

Mon, 01 Sep 2025 00:00:00 GMT

Multi-species genome-wide CRISPR screens identify conserved suppressors of cold-induced cell death Lam, Breanna During hibernation of Syrian hamsters, the core body temperature shows a remarkable decrease, going from 37°C to 4°C. Although this ability to survive at low temperatures could in principle be due to systemic factors that occur during hibernation, we and others have seen that cells from hibernating rodents cultured in vitro maintain this ability. Although others have studied characteristics of cells from hibernating and non-hibernating organisms, the genes and pathways that are involved in cold-induced cell death have not been systematically explored. In this thesis, we conduct two genome-wide CRISPR-Cas9 screens in both a cold-sensitive (K562) and cold-resistant (BHK-21) cell line, and uncover GPX4 and related selenocysteine incorporation genes as important for protection against cold-induced cell death. Using genetic knockdowns, along with overexpression of GPX4, we confirm our findings and demonstrate that levels of GPX4 may be limiting in K562 cells, contributing to their cold sensitivity. Additionally, pharmacological validation using inhibitors of GPX4 reveal that the catalytic activity of GPX4 is dependent on the selenocysteine in the active site. Our findings are extended across multiple cell lines and cell types across six species. Taken together, our results suggest that GPX4 may be a powerful and conserved suppressor of cold-induced cell death. Building on our initial findings, we go on to show that cold exposure leads to increases in membrane permeability. This membrane permeability is transient, as rewarming of the cells reduces permeability to baseline levels. We also test the role of lipid peroxidation in contributing to membrane permeability and find that although it contributes in some cell lines, it is not the sole contributor as ferroptosis inhibitors do not fully mitigate membrane permeability. We go on to test different membrane channels and do not see decreases in membrane permeability, potentially indicating pathway-independent effects of temperature on membrane permeability. Altogether, this work provides a foundation for understanding how cold exposure influences mammalian cells.

Why Landfills Endure: Quantifying economic barriers to material and energy recovery from municipal solid waste in the United States

Mon, 01 Sep 2025 00:00:00 GMT

Why Landfills Endure: Quantifying economic barriers to material and energy recovery from municipal solid waste in the United States Baidoo, Jacqueline E. Municipal solid waste (MSW) is a heterogeneous mixture of materials discarded by residential and nonresidential generators at end-of-life processing facilities for treatment and disposal. Conventional treatment methods reduce waste volumes through recycling via material recovery facilities, energy recovery via municipal solid waste incinerators, and biochemical conversion via composting. Even so, nearly 50% of total MSW generated in the United States was sent to landfills for final disposal in 2018 and almost half of all landfills currently in operation are expected to reach capacity by 2050. Waste planners seek to use developing resource recovery technologies like dry anaerobic digestion, gasification, and pyrolysis to narrow the gaps in end-of-life processing. Such technologies are posited to improve materials circularity and advance zero-waste landfill diversion goals by transforming residuals into electricity, fuels, and precursors to chemicals and fertilizers. However, despite demonstrated improvements to technical inefficiencies in waste valorization, numerous projects built on these technologies have failed to break through to commercial success. We investigate the contribution of regional and economic factors to the success of resource recovery projects through the lens of why landfills remain the predominant method of waste disposal. We build cost models of conventional and select developing treatment methods and use discounted cash flow analysis to estimate financial feasibility by local MSW compositions as reported in regional waste characterization studies. Findings indicate that the most critical factor to sustainable operation is consistent supply of waste materials at the quality and scale that maximize production efficiency, which is not achievable without rigorous data monitoring of MSW composition. Conversely, dependence on waste volume rather than composition makes land disposal a uniquely flexible pathway capable of subsidizing the costs of resource recovery. Progress towards landfill diversion is economically linked to the opportunity cost of avoiding landfill utilization. Unless municipalities are able to introduce subsidies elsewhere in the waste management ecosystem through gate fees and credits, projects will fail where marginal net costs of diversion exceed the revenues lost from avoided landfilling. Targeted processing of organic wastes can facilitate an average diversion of 24% for the compositions surveyed and was found to be viable for composting and dry anaerobic digestion projects at low to negligible financial losses compared to landfilling.

Enlightening Artificial Intelligence with Science

Mon, 01 Sep 2025 00:00:00 GMT

Enlightening Artificial Intelligence with Science Liu, Ziming Today’s artifciail intelligence (AI) systems, while remarkably capable, are largely black boxes. The black-box nature raises concerns for those who build AI – “How can we construct an understand AI in scientifically grounded ways?”, and those who use AI – “How can we trust systems we do not understand?”. This thesis takes a humble step towards addressing the black-box problem. Building white boxes with science (Science for AI): The prevailing paradigm in AI today – “scaling is all you need" – focuses on scaling up existing models. However, this approach often yields systems that are neither interpretable nor efficient. I argue that scientific principles offer fresh perspectives for designing more transparent and effective AI systems. This is demonstrated through Kolmogorov-Arnold Networks (KANs) inspired by mathematics, Poisson Flow Generative Models (PFGM) rooted in physical intuition, and brain-inspired modular training (BIMT) drawing insights from neuroscience, etc. Opening black boxes (Science of AI): Modern AI models exhibit a range of puzzling behaviors – such as grokking, neural scaling laws and emergent representation learning – whose underlying mechanisms remain poorly understood. I employed simplified “spherical cow” models to investigate these phenomena from the perspective of phase transitions. I will show that grokking is a special phase in the hyperparameter space, which can be controlled and eliminated. The learned algorithms after grokking also display distinct phases, called clock or pizza algorithms. AI for Science: With greater interpretability, AI systems can begin to function as “AI Scientists” capable of (re)discovering deep scientific structures from data. These include conservation laws, hidden symmetries, integrable systems, Langrangian and Hamiltonian formulations, modular structures, and high-precion solutions. I believe my research work contributes to the emerging interdiscipinary field that unites AI and Science. Building opon the foundation laid in this thesis, I envision a future in which science guides AI out of its current era of alchemy and into a true era of scientific understanding.

Quantitative modeling of 5' splice site subclass regulation and evolution

Mon, 01 Sep 2025 00:00:00 GMT

Quantitative modeling of 5' splice site subclass regulation and evolution Kenny, Connor Jens Pre-mRNA splicing is an essential molecular process required for eukaryotic gene expression. In this thesis, I present a previously unknown mechanism of splicing regulation where a family of splicing factors, the LUC7 family, compete to differentially impact 5→ splice site (5→ SS) selection in a sequence-dependent manner. I quantitatively characterize two major subclasses of 5→ SS in eukaryotes and outline distinctive features of 5→ SS in exons affected by the three human LUC7 paralogs: LUC7L2 and LUC7L enhance splicing of “right-handed” 5→ SS that exhibit stronger consensus matching on the intron side of the nearly-invariant / GU, while LUC7L3 boosts splicing of “left-handed” 5→ SS with stronger consensus matching upstream of the /GU. Using a range of experimental systems, from human cells to mutant plants, I show that LUC7 paralogs have opposing effects on these two 5→ SS subclasses and that this regulatory mechanism likely originated in the last common ancestor of animals and plants over 1.5 billion years ago. I further evaluate a competing model of 5→ SS subclass regulation involving METTL16- mediated U6 snRNA modification and reconcile both models by devising computational tools that identify sequence features predictive splicing dysregulation in transcriptome-wide datasets. Finally, I examine the evolutionary dynamics of left- and right-handed 5→ SS and propose a model of intron evolution in which codon and intron phase constraints in protein-coding genes shape both minor-to-major intron conversion and transitions between left- and right- 5→ SS subclasses.

Compiler-Hardware Co-Design for Pervasive Parallelization

Fri, 01 Sep 2023 00:00:00 GMT

Compiler-Hardware Co-Design for Pervasive Parallelization Ying, Victor A. Modern computer systems have hundreds of processor cores, so highly parallel programs are critical to achieve high performance. But parallel programming remains difficult on current systems, so many programs are still sequential. This dissertation presents new compilers and hardware architectures that can parallelize complex programs while retaining the simplicity of sequential code. Our new systems allow real-world programs to use hundreds of cores without burdening programmers with concurrency, deadlock, or data races. This dissertation follows a novel approach that eliminates the burden of explicit parallel programming to make parallel execution pervasive. This approach relies on four guiding principles. First, exploiting implicit parallelism preserves the simplicity of sequential execution. Second, dividing computation into tiny tasks, as short as tens of instructions each, unlocks plentiful fine-grained parallelism in challenging programs. Hardware-compiler co-design techniques can create many tasks in parallel and reduce per-task overheads to make tiny tasks scale to many cores. Third, new hardware and software mechanisms can compose parallelism across entire programs, removing serializing barriers to overlap executions of nested parallel subroutines. Finally, exploiting static and dynamic information for data locality reduces data movement costs while maintaining load balance on large multicore systems. This dissertation presents three systems that embody these four principles. First, T4 introduces automatic program transformations that exploit a novel hardware architecture to parallelize sequential programs. As a result, T4 scales hard-to-parallelize real-world programs to tens of cores, resulting in order-of-magnitude speedups. Second, S5 builds on T4 with novel transformations to remove needless serialization in a broad class of challenging data structures. Thus, S5 scales complex real-world programs to hundreds of cores, delivers additional order-of-magnitude speedups over T4, and outperforms manually parallelized code tuned by experts. Finally, ASH is an accelerator that demonstrates the same approach can be applied with simpler mechanisms tailored for digital circuit simulation. A small ASH implementation is 32x faster than a large multicore CPU running a state-of-the-art parallel simulator.

Atomistic Insights into Alloy Solidification using Machine-Learning Potentials

Mon, 01 Sep 2025 00:00:00 GMT

Atomistic Insights into Alloy Solidification using Machine-Learning Potentials Cao, Yifan Alloy solidification is a critical process in materials design and manufacturing, as it governs the formation of microstructures that determines the mechanical, thermal, and chemical properties of materials. However, direct in situ observation remains extremely challenging due the need for high spatial and temporal resolution under elevated temperatures. On the theory side, solidification is a complex phenomenon often studied using phase-field simulations, which rely on empirically fitted parameters and simplified assumptions about interfacial kinetics, limiting their predictive capability. Capturing this process at the atomistic level can yield more fundamental insights, but is hindered by the need for interatomic models that are both accurate and computationally efficient across relevant timescales and length scales. To overcome these challenges, this thesis develops and applies machine-learning interatomic potentials (MLPs) that capture the chemical complexity of metallic alloys, providing a physically accurate and computationally efficient backbone for large-scale atomistic simulations of complex alloy solidification. We first address a foundational challenge in deploying MLPs: the systematic construction of robust and transferable training datasets. Using CrCoNi as a model system, we evaluate various strategies for training MLPs to capture chemical short-range order (SRO), a critical feature in high-entropy alloys, and its effects on materials quantities of relevance for mechanical properties, such as stacking-fault energy and phase stability. It is demonstrated that energy accuracy on test sets often does not correlate with accuracy in capturing material properties, which is fundamental in enabling large-scale atomistic simulations of metallic alloys with high physical fidelity. Based on this analysis we systematically derive design principles for the rational construction of MLPs that capture SRO in the crystal and liquid phases of alloys. The resulting MLPs are validated against experimental measurements on key thermophysical properties, including melting points, heat capacities, thermal expansion coefficients, and enthalpy of SRO formation, confirming their suitability for predictive simulations. With these validated potentials, we then investigate the evolution of SRO during rapid solidification processes. Our simulations reveal that alloy processing can lead to nonequilibrium steady states of SRO that differ qualitatively from any equilibrium configuration. We attribute this behavior to an inherent ordering bias introduced by nonequilibrium dynamics during solidification. These findings suggest that conventional manufacturing processes offer new opportunities to tailor alloy properties by accessing a broader spectrum of nonequilibrium SRO states, expanding the alloy design space beyond the equilibrium spectrum. Finally, we conduct predictive solidification simulations of chemically complex alloys across experimentally relevant growth rates (0.15–2 m/s) , alloy compositions, interface orientations, and undercooling levels. These simulations capture the dynamic build up of solute partitioning at the solid-liquid interface and reveal kinetics-dependent segregation patterns that deviate markedly from equilibrium predictions. The developed framework enables direct evaluation of key kinetic properties under realistic growth conditions, including interface mobility, liquid diffusivity, and solute trapping. Altogether, this thesis develops machine-learning potentials capable of capturing the chemical complexity of metallic alloys with near DFT-level accuracy, and establishes a framework for extracting key kinetic properties through predictive simulations of alloy solidification. When combined with emerging advances in continuum-scale modeling, these results lay the groundwork for truly multiscale investigations of alloy solidification, enabling DFT-level predictive capabilities at scales directly comparable to experimental alloy design and additive manufacturing processes.

Sequential Resource Allocation and Applications in Revenue Management

Mon, 01 Sep 2025 00:00:00 GMT

Sequential Resource Allocation and Applications in Revenue Management Zhou, Zijie Sequential resource allocation is a fundamental problem in operations research, encompassing a wide range of applications where decisions must be made dynamically under uncertainty. This thesis develops new theoretical foundations, explores practical applications, and establishes evaluation methodologies for sequential resource allocation, with a focus on revenue management, robustness and fairness, and experiment design. On the theoretical side, this thesis advances the study of classical network revenue management, a long-standing challenge in dynamic resource allocation. We introduce the first LP-free algorithm, improving the regret bound from O(T ^1/2) to O(T ^3/8)—a significant step toward closing the gap between existing algorithms and the theoretical lower bound of O(1). Additionally, we enhance robustness in sequential resource allocation by developing algorithms that incorporate machine-learned advice, striking a balance between overly conservative worst-case models and overly optimistic stochastic assumptions. Furthermore, we integrate individual fairness into sequential decision-making, ensuring equitable resource allocation without compromising competitive performance. On the application side, we demonstrate the impact of sequential resource allocation in the hospitality management domain. Collaborated with Oracle Lab, we design an online upgrading mechanism that enables hotels to dynamically determine when and at what price to offer room upgrades. Additionally, we propose near-optimal, fast approximation algorithms for this mechanism, achieving a regret bound of O(logT), which is close to the natural lower bound of O(1). We also incorporate our upgrading algorithm to a hotel dataset, and improves more than 20% revenue in 2022. Finally, we introduce new methodologies for evaluating sequential decision-making policies, with a focus on online experiment design. Traditional A/B testing methods struggle with dynamically arriving data, leading to biased or inefficient experimental results. Our pigeonhole experimental design effectively reduces bias and outperforms several well-known experimental design policies, including matched pair design and completely randomized design, making it a more reliable approach for evaluating sequential decision-making strategies. By unifying theoretical insights, real-world applications, and online evaluation frameworks, this thesis contributes to the broader field of sequential resource allocation, providing fundamental advancements with practical implications across revenue management and experimental design.

Aspects of Moiré Quantum Matter

Mon, 01 Sep 2025 00:00:00 GMT

Aspects of Moiré Quantum Matter Paul, Nisarga The advent of moiré quantum matter has newly unified disparate themes in modern condensed matter physics, chief among them band theory, correlations, and topology. This thesis investigates how the interplay between these foundational elements leads to novel electronic phenomena uniquely enabled by moiré superlattices. We focus on modulated Landau levels, which is one of the simplest settings with all three of band dispersion, correlations and topology, yet is rich enough to capture much of the interesting phenomena of moiré quantum matter. We characterize emergent quantum phases that are newly unlocked by the moiré regime. Specifically, we discuss directional localization, formation of Hall crystals with tunable Chern numbers, and novel fractional Chern insulator collective mode physics in the context of modulated Landau levels. We also show that a class of models comprising itinerant electrons strongly coupled to skyrmion-like magnetic textures, closely connected with moiré transition metal dichalcogenides in which the fractional quantum anomalous Hall effect was observed, can host flat Chern bands, emergent Landau levels, and zero-field non-Abelian topological order. This thesis provides a framework for the study of the essential features of moiré quantum matter and demonstrates how moiré systems provide unprecedented opportunities to explore, design, and manipulate strongly correlated topological quantum matter.

An Optimized Bayesian Analysis Framework for the KATRIN Experiment

Mon, 01 Sep 2025 00:00:00 GMT

An Optimized Bayesian Analysis Framework for the KATRIN Experiment Xu, Weiran Neutrinos, which were originally predicted to be massless within the Standard Model of particle physics, have been confirmed to possess non-zero masses through the discovery of neutrino flavor oscillations. These oscillations precisely measure mass-squared splittings between neutrino mass eigenstates, establishing lower limits for the effective electron-neutrino mass at 0.009 eV for normal mass ordering and 0.050 eV for inverted mass ordering. However, the absolute neutrino mass scale remains a fundamental open question in both particle physics and cosmology. Precise spectroscopy of beta-decay spectrum provides a model-independent probe of the absolute neutrino mass via decay kinematics. The KArlsruhe TRItium Neutrino (KATRIN) experiment, utilizing a Magnetic Adiabatic Collimation and Electrostatic (MAC-E) filter spectrometer, sets the world's tightest upper limit of m_v < 0.45 eV (90% C.L.) based on its first five measurement campaigns. KATRIN is scheduled to complete its 1,000-day data-taking period by the end of 2025, targeting a final sensitivity of m_v < 0.3 eV}. Future improvements on neutrino mass measurements will depend on advances in differential detection techniques and the development of atomic tritium sources. This thesis presents an optimized modeling of the KATRIN beta spectrum and a comprehensive analysis of the first five measurement campaigns. An improved framework for computing the theoretical beta spectrum and the KATRIN response function is developed to address the complexities arising from the asymmetric field configurations in the main spectrometer. Benefiting from a computational speedup of four orders of magnitude and improved numerical stability, frequentist best-fit values for individual campaigns are reported, together with an upper limit on neutrino mass using the Lokhov-Tkachov confidence belt construction method. Parallel Bayesian analyses are conducted on the same dataset, yielding an independent and complementary statistical interpretation of the experimental results. Posterior distributions for the squared neutrino mass are sampled for each campaign under a flat prior on m²ᵥ using the parallel Stretch-Move algorithm, and are subsequently combined with a novel approach developed in this work to enhance computational efficiency. Convergence of each Markov chain is assessed through autocorrelation time analysis, and the robustness of the results is validated through cross-team comparison and consistency checks with profile likelihood. The Bayesian results reported here enable straightforward integration with constraints from oscillation measurements and cosmological observations, and the methodologies developed in this work are directly applicable to the final KATRIN dataset, providing a foundation for future neutrino mass analyses and searches for physics beyond the Standard Model.

Redox-Mediated Processes Toward Modular Electrochemical Systems

Mon, 01 Sep 2025 00:00:00 GMT

Redox-Mediated Processes Toward Modular Electrochemical Systems Mallia, Christopher T. Electrochemical technologies offer an attractive path toward a sustainable future where conventional methods of storing energy or producing critical materials are increasingly coupled to renewable electricity generation. To enable such a future, it is imperative that we have strong foundational understanding of electrochemical reactions that are useful to our needs. Redox flow batteries (RFBs) have emerged as a promising architecture for large scale storage of electricity to bridge the gap when renewable generation is unavailable. These devices operate by storing charge in the form of redox-active species that are dissolved into an electrolyte, and subsequently passed through an electrochemical cell to either store or release electrical energy. An extension of the concept of RFBs toward more general applications is to use the dissolved redox-active species to drive a reaction with another material, either to increase the energy storage density through an electrochemically active charge-dense material, or to drive a useful chemical reaction. This extension is termed a redox-mediated (RM) process, and inherits many of the complexities and intricacies of conventional electrochemical technologies, specifically that of RFB-type devices. The subject of this thesis is the development of knowledge and techniques for studying RM processes toward practical embodiments. While technical implementations of this concept are still nascent, many promising early results have been found in devices that use redox-mediated reactions to store electricity. Despite this, progress is frequently hindered by a lack of foundational knowledge from which to ideate better systems, and techniques to experimentally determine underlying physics. First, I establish the development of the RM concept over the past years as primarily through proof-of-concept electrochemical reactors which mimic RFBs. Second, we establish that the underlying nature of some RM reactions can be quantified and understood through corrosion principles, which guide our intuition for selecting chemistries and operating conditions. Third, I demonstrate that the behavior of many desirable RM chemistries is intrinsically coupled to passivation phenomena, and that this must be accounted for in reaction design. Fourth and finally, I provide experimental and practical guidance for researchers in this field, coupled with the design of some apparatus and techniques useful for characterizing RM reactions in specific and electrochemical processes in general. This body of work is broadly intended to advance understanding of electrochemically active interfaces and enable technology concepts which promote a sustainable future.

Predictive Modeling of Chemical Reactivity for Sustainability

Mon, 01 Sep 2025 00:00:00 GMT

Predictive Modeling of Chemical Reactivity for Sustainability Singhal, Avni Priya Predicting and controlling chemical reactivity is key to sustainable material and process design. However, modeling reactivity at scale remains challenging due to the computational demands of quantum chemical methods and the complexity of reaction mechanisms. This thesis explores how high-throughput computational approaches, rooted in quantum chemistry and enabled by automation, can be used to interrogate reactivity across large chemical spaces. We focus on two domains where reactivity governs process efficiency and sustainability: solvent-based carbon capture and polymer, specifically thermoset, manufacturing. We first investigate pi-conjugated heterocyclic nucleophiles as alternative carbon capture solvents to address the high regeneration energy and degradation rates of conventional amine-based systems. We combine synthetic template-based library enumeration, density functional theory (DFT), and machine learning models to evaluate binding energies, capture capacity, regeneration thermodynamics, and oxidative stability. Structure–property analysis reveals design strategies to enhance capture strength while balancing tradeoffs with desorption temperature and degradation resistance. We next focus on designing monomers for frontal ring-opening metathesis polymerization (FROMP), a polymerization mode that enables rapid, energy-efficient manufacturing of polymers. This self-propagating process harnesses exothermic reactions to sustain a polymerization front without continuous external heating, but it requires monomers with a finely tuned balance of thermodynamic and kinetic parameters. We develop a multi-level screening pipeline that integrates DFT-calculated properties with a reaction-diffusion model to predict front behavior directly from the atomistic structure of the monomer. We experimentally validate a preliminary pipeline, identifying a new class of FROMP-capable furan-benzyne monomers, and uncover additional candidates from unexplored chemical spaces that overcome limitations of known systems. Together, these studies demonstrate how high-throughput, mechanism-informed modeling can guide the discovery of molecules and materials that meet complex reactivity and performance criteria.

The Sequence Landscape of Bacterial Genes is Shaped by Long-Range mRNA Folding

Mon, 01 Sep 2025 00:00:00 GMT

The Sequence Landscape of Bacterial Genes is Shaped by Long-Range mRNA Folding Gill, Manraj Singh An evolutionary selection for optimal expression of genes in regulatory networks has led to discernable sequence patterns in bacterial genomes observed in nature. Such patterns result from gene regulatory strategies that leverage sequence-dependent interactions with key cellular machineries and regulatory molecules. While numerous regulatory strategies that shape bacterial gene sequence have been characterized, predicting functional consequences from sequence alone remains challenging due to the sheer vastness of the possible sequence space. Moreover, the primary gene sequence encodes information on secondary and tertiary topologies that the molecules of the central dogma can fold into. Specifically, though local messenger RNA (mRNA) structures are known to regulate bacterial gene expression, the role of long-range mRNA folding remains unclear despite the predicted prevalence of such interactions across mRNAs. In bacteria, a major regulator of mRNA decay and translation rates is accessibility of the ribosome binding site (RBS) to the ribosome. Sequences in the mRNA’s 5´ untranslated region (UTR) complementary to the RBS can decrease gene expression by base pairing and occluding ribosomes from binding. To determine whether such antagonistic sequences are also the primary determinants of sequence choice along the rest of the mRNA transcript, we measured the effect of all possible 8-nucleotide substitutions (65,536 variants) on mRNA levels when placed in multiple positions along a bacterial transcript. We find that, while the vast majority of substitutions in the middle of genes negligibly affect RNA level, 8mers with complementarity to parts of the RBS exhibit the strongest effects by increasing RNA degradation rates up to 4-fold. RBS-complementary sequences also decrease translation initiation rates when placed in a coding sequence, and are able to occlude ribosome binding even when they are located hundreds of nucleotides away from the start codon. The inhibitory effect of such secondary structures on gene expression likely explains a strong selection against sequences complementary to conserved parts of RBSs throughout coding sequences of genes from diverse bacterial genomes, which we uncover through computational analysis. Together, this thesis reveals the widespread impact of RNA intramolecular interactions in vivo on both mRNA stability and translation and uncovers a key constraint on gene sequences.

Engineering Materials for Non-Compressible Torso Hemorrhage and Internal Bleeding

Sun, 01 May 2022 00:00:00 GMT

Engineering Materials for Non-Compressible Torso Hemorrhage and Internal Bleeding Hong, Celestine Jia Huey Non-compressible torso hemorrhage (NCTH) and internal bleeding results in a significant number of preventable casualties worldwide among civilians and in the field. In particular, internal bleeding can only be diagnosed through changes in vital signs and then through imaging modalities that may only be available in a hospital setting. Over the past few decades, researchers in the field have sought to address these needs by developing hemostats that can rapidly expand, bind, or seal an exposed wound, or interact with wound-specific components when delivered intravenously to enhance preexisting hemostatic processes. The first part of this thesis investigates the effect of hemostatic nanoparticle size on their interactions with platelets. Small nanoparticles were observed to result in an increased percentage of specifically-bound single platelets under flow and intermediate nanoparticles were observed to result in the greatest degree of platelet recruitment to a platelet-collagen surface. Large nanoparticles were observed to result in the most nanoparticle mass bound to a surface, the shortest circulation time and retention, and the highest pulmonary accumulation. Ultimately, intermediate nanoparticles were shown to result in the most significant increase in survival relative to the saline control in a lethal inferior vena cava (IVC) injury model (84.6% vs 26.7%), as well as the greatest accumulation at the injured IVC relative to uninjured vessel controls. Subsequently, the intermediate nanoparticles from the prior study were functionalized with bio-orthogonal click-crosslinkable azide groups to achieve targeted crosslinking behavior. Commercial multiarm PEG functionalized with the corresponding clickable moiety, dibenzylcyclooctyne (DBCO), and DBCO-PEG-b-PLGA nanoparticles were delivered as the second part of this two-component system. This system was demonstrated to increase platelet recruitment, and decrease fibrin loss during plasminolysis in vitro. When challenged in a mouse liver resection model, the two-component system resulted in significantly increased survival relative to the nanoparticle-only system and higher accumulation in the remnant liver. Finally, a charge-inverting polymer was synthesized through controlled radical polymerization. The material was demonstrated to undergo rapid charge inversion when exposed to physiological pH, resulting in the near-complete lift-off within a minute of a layer-by-layer drug film into the dermis when coated on microneedles. This versatile release platform could be coated on wound dressings to facilitate the release of therapeutics to aid in healing, or other applications involving charged films. In sum, this thesis has investigated several new materials and assays for the treatment of traumatic hemorrhage, opening potential avenues for the development of more effective hemostats.

Advances in Nonconvex and Robust Optimization

Mon, 01 Sep 2025 00:00:00 GMT

Advances in Nonconvex and Robust Optimization Koukouvinos, Theodoros Nonconvex optimization presents significant challenges, as identifying the global optimum is often difficult. This thesis introduces novel algorithms to find the exact solution of a broad class of nonconvex optimization problems. The thesis is structured into four parts. In Chapter 2, we propose a novel method for solving nonconvex optimization problems, in which the nonconvex components are sums of linear times convex (SLC) functions. We introduce a new technique, called the Reformulation-Perspectification Technique (RPT), to obtain a convex approximation of the original nonconvex optimization problem. We then incorporate RPT within branch and bound to obtain the global optimal solution of the nonconvex optimization problem. By using the RPT, we obtain a convex relaxation by forming the perspective of each convex function and linearizing all product terms with newly introduced variables. To further tighten the approximation, we pairwise multiply constraints. Therefore, in Chapter 3, we analyze all possibilities of multiplying conic constraints, a very wide class of constraints. Further, we delineate methods for deriving new, valid linear and second-order cone inequalities for pairwise constraint multiplications involving the power cone and exponential cone, thereby enhancing the strength of the approximation. In Chapter 4, we address nonconvex optimization problems that involve polynomials. We derive valid SLC decompositions for polynomials, in which the linear functions are inequalities of the feasible region and the convex functions are quadratics. We prove the existence of such SLC decompositions for arbitrary degree polynomials. Further, out of the many possible SLC decompositions, we obtain the one that results in the tightest lower bound. Finally, in the numerical experiments we show that our method often outperforms state-of-the-art approaches for polynomial optimization. In Chapter 5, we propose a robust optimization framework that immunizes some of the central linear algebra problems in the presence of data uncertainty. Namely, we formulate linear systems, matrix inversion, eigenvalues-eigenvectors and matrix factorization under uncertainty, as robust optimization problems using appropriate descriptions of uncertainty. We show that for both linear systems and matrix inversion, the robust approach leads to more accurate solutions than the nominal, in the case of nearly singular matrices.

Fabricating and Tailoring Halide Perovskites for Photovoltaic Applications

Mon, 01 Sep 2025 00:00:00 GMT

Fabricating and Tailoring Halide Perovskites for Photovoltaic Applications Kadosh Zhitomirsky, Tamar Green energy is a contemporary global concern, and research of materials for solar energy harvesting is the heart of potential solutions for the energy crisis. Halide perovskites are leading candidates to replace silicon in next generation solar cells. This thesis focuses on halide perovskite materials, aiming to understand their structure, electronic and ionic properties and photo-activity; and to re-direct their fabrication techniques to address global market needs and requirements. In this work we developed alternative, vapor-based fabrication techniques, based on manufacturing-compatible, safe, rapid and scalable processes, that have the potential to improve material stability and efficiency. Vapor Transport Deposition (VTD) is investigated as a promising fabrication method for thin film halide perovskites and beyond. We explored the deposition parameter space and elucidated relationships and trends regarding composition, structure and deposition rate. We examined the morphology, crystal phase formation, optical and electrical properties, and finally the performance of the deposited films when incorporated into solar cells. We begin by exemplifying the viability of vapor transport co-deposition in fabricating active perovskite films, utilizing methylammonium lead iodide (MAPbI3) as a simplified model system. We then design an improved version of the vapor transport deposition system and transition to the more technologically attractive perovskite composition formamidinium lead iodide (FAPbI3). Learning from previous attempts to fabricate this material, we developed a novel technique that we call Hybrid two-step vapor-solution deposition in which we use VTD to deposit the inorganic 4 precursor, not readily dissolved in industry acceptable solvents, and then react it with a solution of the organic precursors dissolved in a benign solvent. This technique allowed us to fabricate functioning FAPbI3 based solar cell devices, in a safe, fast-paced, scalable and manufacturing compatible fashion. The deposition rate is significantly influenced by chamber pressure and source temperature, and by controlling all deposition parameters, we systematically reached rates of up to 1200 nm/min, that is orders of magnitude faster than current comparable techniques. We found the technique to be reproducible, yielding 13% efficient devices, with champion efficiencies of up to 15.3%. Based on the proposed novel fabrication process, we believe it offers an avenue for further improvement in solar cell stability and efficiency. CsPbBr3, a fully inorganic halide perovskite, also shows great promise as a photo and gamma ray detector and like the other halide perovskites is known to support halide ion conductivity that contributes to device instability and reduced sensitivity to irradiation. We choose this as a model system to apply concepts from defect chemistry and demonstrate the ability to measure and manipulate the ionic conductivity in the material by stoichiometry control and doping.

Membrane protein conformational dynamics and ligand-binding interactions in bacterial glycoconjugate biosynthesis

Mon, 01 Sep 2025 00:00:00 GMT

Membrane protein conformational dynamics and ligand-binding interactions in bacterial glycoconjugate biosynthesis Higinbotham, Hugh Membrane associated proteins are an essential component of the complex biochemistry that is carried out at the membrane interface and perform essential functions for cellular life. Biophysical characterization of protein structure-function relationships faces a unique set of challenges due to the constraints of phospholipid bilayer chemistry and geometry. Advances in x-ray crystallography and cryo electron microscopy have made progress in this regard, but dynamic structural features remain difficult to study. Small membrane proteins, such as those responsible for bacterial glycosylation, remain challenging to structurally characterize at all. Bacterial glycan synthesis pathways are essential for cell function yet highly variable between strains, making them promising systems for targeted antibiotic development. Many pathways have initiating SmPGTs that show incredible specificity for minute changes in glycan chemistry despite being small enough to streamline many computational methods, which makes them ideal model systems for developing multidisciplinary strategies to study membrane protein dynamics. This thesis presents a strategy that employs structural bioinformatics in Chapter 2, molecular dynamics simulation (MD) in Chapter 3, and single-molecule FRET microscopy (smFRET) in Chapter 4 to observe the ligand-dependent conformational dynamics of integral membrane proteins in situ. It focuses on representative members of the small monotopic phosphoglycosyl transferase (SmPGT) superfamily, which catalyze transfer of a phosphosugar from a soluble nucleotide-sugar donor to a membrane-embedded polyprenol phosphate acceptor in the initiating step of glycoconjugate biosynthesis in prokaryotes. The pipeline is employed to confirm the role of SmPGT conformational dynamics in substrate binding and informs the design of non-hydrolyzable substratemimetic inhibitors. Chapter 5 further sets the stage for the use of structural bioinformatics and molecular simulation to characterize subsequent glycosyl transferase (GT) enzymes down pathway and presents initial results characterizing inter-protein cooperative interactions. The integrated approach to incorporate computational and experimental characterization methods has significantly contributed to the understanding of SmPGT structure-function relationships and opened up new directions of inquiry into specific PGTligand interactions, the development of new inhibitory compounds, and the role of interprotein interactions in bacterial glycan synthesis.

Functional Genomic and Image-Based Screening Approaches for Probing Host-Pathogen Interactions

Thu, 01 Jun 2023 00:00:00 GMT

Functional Genomic and Image-Based Screening Approaches for Probing Host-Pathogen Interactions Carlson, Rebecca J. Host-pathogen interactions represent a complex interplay between hosts and pathogens that can evolve over millions of years. Interactions between bacteria or viruses and human cells, and the resulting evolved antipathogenic signaling pathways, are processes responsible for pathologies ranging from infectious diseases to autoimmune conditions and cancer. In addition, engineered designs inspired by pathogen interactions with hosts are increasingly being used to both treat and diagnose many pathologies that need not originate from infection with a pathogen. Therefore, it is critical to build and deploy scalable tools to better understand host-pathogen dynamics in order to both better treat conditions where pathogens or antipathogenic signaling contribute directly to disease pathology as well as to engineer new treatments to address a broader range of disease states. In this thesis, I describe approaches to leverage functional genomics and image-based screening to perturb and profile host-pathogen interactions, including responses to two RNA viruses, Sendai virus and Ebola virus. These provide case studies highlighting the utility of high-content image-based screening for revealing new genes regulating predefined phenotypes of interest as well as for generating single-cell imaging profiles that can be used to infer new genetic functions and phenotypic states directly from screening data without a priori specification. I also highlight an example of a genetic screen that revealed a robust negative result, leading to hypothesis and validation of a novel function of the STING protein as a proton channel.

The politics of metropolitan transportation.

Wed, 01 Jan 1964 00:00:00 GMT

The politics of metropolitan transportation. Colcord, Frank Carlton. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics and Social Science, 1964

On hypergraphs and hypergeometries.

Fri, 01 Jan 1971 00:00:00 GMT

On hypergraphs and hypergeometries. Helgason, Thorkell. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1971; Vita.; Bibliography: leaves 158-159.

Cocoa in the Ghanaian economy.

Fri, 01 Jan 1965 00:00:00 GMT

Cocoa in the Ghanaian economy. Bateman, Merril Joseph. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1965

Even denominator quantum numbers and termination of the fractional series in the fractional quantum hall effect

Sun, 01 Jan 1989 00:00:00 GMT

Even denominator quantum numbers and termination of the fractional series in the fractional quantum hall effect Willett, Robert L. (Robert Lee) Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1989; Includes bibliographical references (leaves 6-7).

Investigations in the theory of quantum corrections to classical solutions of the Yang-Mills equations

Mon, 01 Jan 1979 00:00:00 GMT

Investigations in the theory of quantum corrections to classical solutions of the Yang-Mills equations Callias, Constantine John. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1979; Includes bibliographical references.

Wave equations, particles and chronometric geometry.

Thu, 01 Jan 1976 00:00:00 GMT

Wave equations, particles and chronometric geometry. Orsted, Bent. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1976; Includes bibliographical references.

Systematic engineering of controlled, localized oligonucleotide delivery systems for wound angiogenesis

Wed, 01 May 2024 00:00:00 GMT

Systematic engineering of controlled, localized oligonucleotide delivery systems for wound angiogenesis Berger, Adam G. The standard of care for diabetic wounds has remained relatively unchanged for decades, resulting in patients with wounds that do not heal on meaningful time scales, referred to as ulcers, and high rates of recurrence for patients whose wounds do heal. This common complication of diabetes decreases quality of life, increases mortality, and raises health care costs. New paradigms to treat these wounds remains a formidable but critical challenge. Addressing diabetic ulcers at the molecular level may decrease healing time and prevent recurrence. Impaired blood vessel formation, or angiogenesis, in diabetic ulcers is an important target pathway. Angiogenesis is needed to bring oxygen, nutrients, signaling cues, and cells to newly formed tissue while removing waste. Nucleic acid oligonucleotide therapies, such as small interfering RNAs (siRNAs) or microRNA inhibitors (anti-miRs), that regulate gene expression at the post-transcriptional level, hold particular promise for promoting angiogenesis and wound healing; however, the large size and negative charge of these therapies require drug carriers to mediate their biological effect. In this thesis, we leverage sequential electrostatic adsorption of oligonucleotide therapy and polyelectrolytes into thin film coatings on commercial wound dressings through the layer-by-layer (LbL) process. These dressings package oligonucleotide, enhance its transfection efficacy, and control its temporal release locally to the wound bed. After initial validation experiments, we sought to systematically understand our drug carrier system and use this insight to engineer better wound dressings. First, we developed a proof-of-concept anti-miR-coated dressing and showed its efficacy in promoting both wound closure and sex-dependent angiogenesis. We found that therapy released from coated dressings had a preferential association with different wound cell types, particularly endothelial cells. We then sought to uncover how changes in the oligonucleotide structure itself may alter interactions with transfection polymers in thin film coatings. We found that binding with certain polyelectrolytes differed based on whether the therapy was a flexible single stranded anti-miR or a more rigid double stranded helix siRNA. We also showed how chemically modified nucleotides, such as locked nucleic acid and 2’-O-methyl RNA, can modulate affinity to polyelectrolytes and ultimately impact transfection efficacy. We also elucidated how physicochemical properties of the hydrolysable transfection-enhancing poly(β-aminoester) polymer mediate its efficiency in transfecting oligonucleotide therapy. We demonstrated that a more hydrophobic polymer enhanced transfection efficacy through its ability to facilitate permeation of biological barriers. Finally, we identified how modulation of the anionic excipients contained in these thin film coatings can be leveraged to vary the release kinetics from coated wound dressings. We engineered formulations that released on a fast or slow time scale. We observed that while both release time scales promoted efficacy in wound closure, they did so through potentially different mechanisms despite the same putative pro-angiogenic anti-miR therapy. In sum, this thesis elucidates how physicochemical properties and formulation of coated wound dressings alter their interfacial effects with biological systems. We use this knowledge to rationally design better drug carriers that can deliver pro-angiogenic oligonucleotide therapeutics to the wound bed. The findings have broad applications in the delivery of nucleic acid therapies for a wide host of diseases where local delivery to the injured tissue could prove beneficial. Ultimately, we also advance our pro-angiogenic coated wound dressing strategy towards clinical translation. Our strategy has the potential to provide a new, targeted therapeutic paradigm to help those suffering from diabetic ulcers.

Influence of Electronic Structure and Lattice Dynamics on Oxygen Ion Transport in Solid-State Ionic Conductors

Mon, 01 Sep 2025 00:00:00 GMT

Influence of Electronic Structure and Lattice Dynamics on Oxygen Ion Transport in Solid-State Ionic Conductors Vivona, Daniele Solid-state oxygen ion conductors are crucial for electrochemical devices such as separation membranes, solid-oxide electrolyzers, fuel cells, and sensors, serving as a technological link between renewable energy generation and consumption. Currently, these conductors are limited by slow transport rates and high operational temperatures, which pose challenges and increase costs. Developing faster conductors that operate at lower temperatures requires reducing activation energy and enhancing the pre-exponential factor in the Arrhenius equation of conductivity. However, our understanding of the fundamental processes in oxygen ion transport and methods to improve oxygen ion conductivity remain limited. This thesis focuses on understanding the fundamental mechanisms that regulate oxygen ion transport. First, the migration energy barrier in perovskite oxides is linked to an electronic energy penalty from local charge screening near the hopping ion. The energy of local electronic states is identified as a fundamental descriptor of the migration barrier. Next, migration entropy and phonon density of states (DOS) are highlighted as the main factors regulating the pre-exponential factor of oxygen ion conductivity across different materials. The phonons of oxygen ions near the hopping ion significantly contribute to migration entropy, suggesting that migration entropy can be tuned by designing the phonon dynamics of these atoms. These results imply that a widely observed correlation between increasing pre-exponential factors and activation energy arises from coupling local electronic energy states and phonons. The results are extended to the formation of oxygen vacancies and interstitials in perovskite and RuddlesdenPopper oxides. We find that defect formation energy rises with defect formation entropy, which is linked to electronic energy states interacting with phonons. In perovskite oxides, lower vacancy formation entropy is correlated with increasing oxygen phonon band center and shortening bond lengths with oxygen vacancy formation. In Ruddlesden-Popper oxides, lower interstitial formation entropy is associated with reduced octahedral tilting and local phonon changes. This thesis establishes a theoretical foundation for treating migration entropy and defect formation entropy as design variables in next-generation ionic conductors. By highlighting the impact of electronic structure and lattice dynamics on energy barriers and entropic drivers, the findings suggest new pathways for material design through the strategic separation of these factors and the intelligent design of lattice moieties in oxygen ion transport environments.

Being. Creative. Together. Designing Technologies That Center Human Connection, Co-creativity, and Calm in the Era of AI

Thu, 01 May 2025 00:00:00 GMT

Being. Creative. Together. Designing Technologies That Center Human Connection, Co-creativity, and Calm in the Era of AI Dhariwal, Manuj As Artificial Intelligence (AI) becomes increasingly interwoven into our creative, social, and learning experiences, we must ask: Will these technologies deepen our connection to the timeless human experiences of Being, Being Together, and Being Creative Together—or will they pull us apart, leaving us more anxious and isolated? In an era where AI systems are increasingly framed as our “co-creators” and “companions,” enabling hyper-personalized yet hyper-isolated interactions, this dissertation reclaims the prefix ‘co-’ as fundamentally interhuman—introducing a set of new paradigms that center human connection, co-creativity, and calm in the design of technologies. Central to this work, we’ve developed CoCo (coco.build), a general-purpose, real-time co-creative learning platform that empowers young people to engage in a wide variety of safe, shared creative experiences with their peers—spanning creative computing, AI education, digital art, writing, and more. Through the platform, we showcase how digital environments can move beyond isolated modes of learning and creating to support multiple ways of being creative together with others—introducing a new paradigm for real-time digital collaboration. We further illuminate how CoCo has been envisioned as a “self-less” social platform that de-emphasizes comparison-based, self-centric metrics (profiles, likes, followers) prevalent in most online systems for young people. We weave these interconnected ideas into the unifying theme of “Being. Creative. Together.”— values we believe are both timeless and especially timely in the AI era. We supplement the broader design, technical, practical, and pedagogical contributions of this work by sharing insights and feedback from pilots with over 2,000 young people and educators across diverse settings. Ultimately, we see this dissertation as both a contribution and a call—to preserve the human essence of co-, to distinguish it from the useful, powerful, but instrumental AI interactions, and to shape digital environments that nurture our capacity to co-imagine, co-create, co-learn, co-exist, and co-evolve—with and through one another. Note: This work has been co-developed with Shruti Dhariwal. See https://coco.build/thesis for suggested citation and updates on this work.

Toward the computational transformation of legal theory and practice

Thu, 01 May 2025 00:00:00 GMT

Toward the computational transformation of legal theory and practice Mahari, Robert This doctoral thesis seeks to advance the formalization of computational law as a distinct research discipline. It explores three interwoven key themes: the empirical understanding of legal systems through advanced computational methods; the development of computational tools to augment the capabilities of legal practitioners, thereby expanding access to justice; and the identification of novel, computationally-enabled regulatory interventions. This research directly confronts the global access to justice crisis and the shortcomings of conventional legal services that frequently leave businesses and individuals without adequate support. Furthermore, the thesis investigates innovative regulatory strategies for emerging technologies, aiming to synchronize legal frameworks with contemporary technological progress by exploring adaptive and forward-looking governance approaches.

Modular Development Platforms and Creative Ecosystems: Design & Deployment for Wide Impact Across Fields

Thu, 01 May 2025 00:00:00 GMT

Modular Development Platforms and Creative Ecosystems: Design & Deployment for Wide Impact Across Fields Shtarbanov, Ali Physical, digital, and conceptual tools and building blocks are fundamental enablers and accelerators of humanity’s progress in technology, science, medicine, art, and even in abstract fields like mathematics, philosophy, and social sciences. Hardware development platforms present a special class of tools and building blocks, facilitating and accelerating innovation, prototyping, and research. They drastically reduce prototyping time and complexity, improve efficiency for experts, democratize access to innovation, and even inspire entirely new ideas. This research investigates how to design, develop, and deploy development platforms in ways that maximize their real-world impact potential. It focuses not only on the technical and engineering aspects, but also on the complete ecosystem a platform needs in order to have impact, including community building, engagement with users and volunteers, content strategy, online presence, publicity, deployment, feedback loops modularity, financial viability, and symbiotic relationships. A comprehensive Design & Deployment Framework is introduced as a conceptual tool for creating high-impact platforms and creative ecosystems, recognizing and fostering the positive feedback loops that sustain them and that shape their evolution and growth. This framework is applied in the development and deployment of multiple novel platform and ecosystem projects, including FlowIO, SleeveIO, and ModiStrap, as well as the ecosystem SoftRobotics.IO. Those works have benefited thousands of people around the world, providing researchers, designers, and engineers with powerful, reconfigurable, modular enabling artifacts that streamline prototyping, accelerate research, and lower barriers in fields like soft robotics, haptics, assistive technology, shape-changing interfaces, interactive arts, and more. A multitude of research, art, and engineering projects made possible by FlowIO and SoftRobotics.IO are presented, as well as over a dozen case studies showcasing how other users across disciplines have adopted, utilized, and extended these systems to advance their own creative, educational, and technical endeavors. Additionally, this thesis also investigates various deployment models for hardware and introduces a new hardware deployment model for equitable access to expensive hardware that may otherwise be financially out of reach for many users, as well as an “earned open-source” model, which preserves the essence of the traditional open-source model, while eliminating many of its pitfalls.

Private, Verifiable, and Auditable AI Systems

Thu, 01 May 2025 00:00:00 GMT

Private, Verifiable, and Auditable AI Systems South, Tobin The growing societal reliance on artificial intelligence necessitates robust frameworks for ensuring its security, accountability, and trustworthiness. This thesis addresses the complex interplay between privacy, verifiability, and auditability in modern AI, particularly in foundation models. It argues that technical solutions that integrate these elements are critical for responsible AI innovation. Drawing from international policy contributions and technical research to identify key risks in the AI pipeline, this work introduces novel technical solutions for critical privacy and verifiability challenges. Specifically, the research introduces techniques for enabling verifiable and auditable claims about AI systems using zero-knowledge cryptography; utilizing secure multi-party computation and trusted execution environments for auditable, confidential deployment of large language models and information retrieval; and implementing enhanced delegation mechanisms, credentialing systems, and access controls to secure interactions with autonomous and multi-agent AI systems. Synthesizing these technical advancements, this dissertation presents a cohesive perspective on balancing privacy, verifiability, and auditability in foundation model-based AI systems, offering practical blueprints for system designers and informing policy discussions on AI safety and governance.

Language Models as Mirrors and Bridges for Intergroup Communication

Thu, 01 May 2025 00:00:00 GMT

Language Models as Mirrors and Bridges for Intergroup Communication Jiang, Hang This dissertation explores how large language models (LLMs) can serve dual roles in intergroup communication: as mirrors that reflect intergroup differences, and as bridges that facilitate communication across group boundaries. Intergroup communication refers to interactions between individuals from different social groups, such as political, cultural, or professional communities, where divergent perspectives often lead to misunderstandings, unequal access to information, and social fragmentation. The first part of the dissertation presents LLMs as mirrors that reveal intergroup differences. We first introduce CommunityLM, a novel framework for probing public opinion by fine-tuning LLMs on social media posts from specific communities. Our case study comparing Republican and Democratic groups reveals that model predictions align well with human survey responses, substantially outperforming established baselines. Building on this foundation, we develop PersonaLLM to investigate whether prompt-based LLM agents can generate content aligned with assigned personas, which has emerged as a popular approach for modeling the behaviors of social groups. Through automated and human evaluations, we demonstrate that these agents can complete personality tests and write stories that reflect the distinctive behavioral patterns of specific personality profiles. Together, these complementary projects illustrate how LLMs can effectively capture and simulate the unique perspectives and behaviors that characterize diverse social groups. The second part of the dissertation presents LLMs as bridges that facilitate communication across group boundaries. First, we introduce Bridging Dictionary, an interactive tool that uses retrieval-augmented generation (RAG) techniques with LLMs to identify polarized language and suggest more inclusive alternatives. In collaboration with PBS Frontline, we demonstrate the potential of LLMs to reduce misunderstanding in journalism and political communication. Second, we present Legal Storytelling, a human-LLM collaboration framework that generates accessible narratives to explain complex legal concepts to non-experts. Through randomized controlled trials (RCTs), we find that LLM-generated narratives can improve legal literacy and help bridge communication gaps between experts and laypeople, particularly among non-native English speakers. Third, we develop FaciliTrain, a voice-based, LLM-powered system that enables facilitators to learn and practice intergroup dialogue skills with multiple LLM agents representing diverse social backgrounds and personas in a small-group setting. User studies with campus participants show encouraging early results, suggesting that LLMs can effectively support the development of communication skills essential for constructive intergroup dialogue. Together, these projects illustrate how LLMs can actively foster mutual understanding across social divides by promoting more inclusive, accessible, and constructive communication.

Facilitating Creative Learning: Engaging in a Practice of Care

Thu, 01 May 2025 00:00:00 GMT

Facilitating Creative Learning: Engaging in a Practice of Care Presicce, Carmelo Creative learning is shaped not only by tools and activities, but by relationships. This dissertation explores facilitation in creative learning environments as a relational practice centered on care—not as a set of techniques, but as a deeply human way of being with others, a commitment to creating spaces where people feel supported enough to explore, connected enough to share, and valued enough to express themselves. Grounded in constructionist, socioconstructivist, and humanistic pedagogies, the research draws from my multi-year engagement with Learning Creative Learning (LCL)—an online course and global community for educators—and WeScratch, a series of hands-on, collaborative online workshops introducing educators to creative coding. Through qualitative analysis of small-group facilitation during WeScratch workshops, I explore how volunteer facilitators experience and reflect on their practice. Drawing from three case studies, I examine how care takes shape in the situated, relational work of creative learning facilitation. In particular, I identify three interrelated forms of care: epistemic care, which focuses on what and how people learn; affirming care, which supports what learners value and who they are; and convivial care, which attends to how learners feel and relate to one another in a group. After introducing these three forms of care through the work of individual facilitators, I show how epistemic, affirming, and convivial care are deeply interwoven in practice—at times reinforcing one another, at times pulling in different directions. Facilitators must navigate these tensions in the moment, making situated judgments about when to step in, when to hold back, and how to respond to the evolving needs of individuals and groups. By centering care, this research highlights facilitation as deeply human, relational work that sustains the conditions for creative learning, contributing to the broader and evolving discourse on constructionism. It also makes the case for seeing facilitation as an ethical and political practice. In a time when educational discourse is increasingly shaped by ideals of efficiency and optimization—and the world faces rising authoritarianism and dehumanization—choosing to care is not only pedagogically meaningful, but also politically urgent.

Novel Earth Abundant Catalytic Materials for Abatement of Atmospheric Methane Sources, and Evaluation of Agricultural Deployment Environments

Sun, 01 Sep 2024 00:00:00 GMT

Novel Earth Abundant Catalytic Materials for Abatement of Atmospheric Methane Sources, and Evaluation of Agricultural Deployment Environments Brenneis, Rebecca J. Annual global average temperatures in the past year have already exceeded the international target limit of 1.5°C, and the window to prevent that rise from extending is rapidly closing. The high global warming potential (GWP) and short atmospheric residence time (half-life of around 12 years) of methane make it a critical target for action to slow the pace of climate change in this decade. Yet technological solutions for methane abatement are challenged by methane’s inertness, dilute atmospheric concentrations, and diffuse, variable emissions sources. In this thesis, I propose the use of a bio-inspired, earth-abundant, heterogeneous catalysts as a novel tool for atmospheric and emissions-based methane abatement. Copper zeolites were characterized for their ability to convert low levels of methane, continuously, at low temperatures, for moderate durations, and in the presence of a variety of gaseous mixture influents, designed to mimic atmospheric air at standard temperatures and pressures. Catalytic performance was tested under conditions designed to mimic those found at two of the primary sources of low-level, anthropogenic emissions: ventilation air methane (VAM) and industrial dairy. Laboratory synthesized catalysts were shown to completely oxidize methane at concentrations ranging from atmospheric to 1%, covering the range of subflarable levels. Conversion was demonstrated at temperature as low a 270°C, with complete conversion achievable as low as 350°C, in the presence of 20% oxygen. While the presence of water vapor, nitric oxide, and hydrogen sulfide were shown to partially reduce catalytic efficiency, conversion efficiency was restored with increased temperature. The presence of carbon dioxide, alkanes, ammonia and hydrogen, at industrially relevant concentrations, had no effect on catalytic performance. Finally, atmospheric samples were collected at six industrial scale dairy barns across the Midwest and compared with the simulated laboratory conditions. Dairy samples fell within the ranges tested at the bench scale showing no evidence of any impediment to copper zeolite as a potential abatement tool. Methane concentrations at dairies were shown to be on the order of atmospheric to low 10s of ppmv making copper zeolites the only currently identified abatement strategy to address methane emissions at these locations. While it remains to be shown that these zeolites can provide net greenhouse gas benefit in the conditions required, copper zeolites are a strong option on a short list of technologies to address methane at any subflarable concentration, sources of which comprise 80% of global emissions sources, showing great promise as a climate technology breakthrough.

To Co- Is Human: Designing Technologies That Center Human Connection, Co-creativity, and Calm in the Era of AI

Thu, 01 May 2025 00:00:00 GMT

To Co- Is Human: Designing Technologies That Center Human Connection, Co-creativity, and Calm in the Era of AI Dhariwal, Shruti In an era where Artificial Intelligence (AI) systems are increasingly framed as our “companions” and “co-creators,” this dissertation reclaims “co-” as a fundamental marker of shared human experience—using it as a foundation to reimagine and build technologies that consciously center interhuman connection and co-creativity. Central to this work, we’ve developed CoCo (coco.build)—a general-purpose, real-time co-creative learning platform that empowers young people to engage in a wide variety of safe, shared creative experiences with their peers, spanning creative computing, AI education, digital art, writing, and more. Through the platform, we showcase how digital environments can move beyond isolated modes of learning and creating to support multiple ways of being creative together with others—introducing a new paradigm for real-time digital collaboration. We further illuminate how CoCo has been envisioned as a “self-less” social platform that de-emphasizes comparison-based, self-centric metrics (profiles, likes, followers) prevalent in most online systems for youth. We anchor these interconnected ideas in a unifying theme of “Being. Creative. Together.”—reflecting timeless values that have become especially timely in an era when AI tools can further accentuate individualized digital experiences for young people. We supplement the broader design, technical, practical, and pedagogical contributions of this work by sharing insights and feedback from pilots with over 2,000 young people and educators across diverse settings. Ultimately, we see this dissertation as both a contribution and a call—to preserve the human essence of co-, to distinguish it from the useful, powerful, but instrumental AI interactions, and to shape digital environments that nurture young people’s capacity to co-imagine, co-create, co-learn, co-exist, and co-evolve—with and through one another. Note: This work has been co-developed with Manuj Dhariwal. See https://coco.build/thesis for suggested citation and updates on this work.

Decentralized Machine Learning over Fragmented Data

Thu, 01 May 2025 00:00:00 GMT

Decentralized Machine Learning over Fragmented Data Singh, Abhishek The remarkable scaling of data and computation has unlocked unprecedented capabilities in text and image generation, raising the question: Why hasn’t healthcare seen similar breakthroughs? This disparity stems primarily from healthcare data being fragmented across thousands of institutions, each safeguarding patient records in regulatory-compliant silos. The problem is not limited to healthcare but extends to other industries with fragmented data across institutions and individuals. Instead of centralizing various datasets to solve the fragmentation problem, which raises regulatory and ethical concerns, this thesis proposes systems and algorithms to decentralize the machine learning pipeline. Current approaches in this area have centered around Federated Learning (FL), which enables model training over distributed data. However, FL’s dependence on central coordination and inflexibility with heterogeneous systems limit its applicability in healthcare settings. Motivated by these challenges, I explore the following three core themes: 1) Coordination – Today’s coordination algorithms typically rely on static rules or randomized communication, approaches that turn out to be sub-optimal when data heterogeneity is high. I present a new system and a benchmark framework that enables systematic assessment of different coordination algorithms. Next, I propose an adaptive coordination algorithm that leverages historical performance and learning dynamics to improve coordination. 2) Heterogeneity – Data owners can vary significantly in their data distributions, computational resources, and privacy requirements. To address this heterogeneity, I turn the focus from the traditionally protected training phase to securing the critical inference process. Next, I develop techniques for distributed training that adapt to heterogeneous computational capabilities across different agents. 3) Scalability – Enabling scaling in decentralized ML requires addressing three key challenges: parallelization, synchronization, and self-scaling. While parallelization has advanced significantly, the other two remain challenging. I present a framework for offline collaboration through sanitized, synthetic datasets that eliminates constant synchronization needs while preserving privacy. This thesis identifies and addresses some of the bottlenecks along these three core themes through a complementary set of solutions: adaptive coordination, heterogeneity-aware training, and scalable collaboration. Together, these building blocks can enable a practical framework for unlocking data silos across institutions.

Interplay between spatial structure and competition in ecological communities

Thu, 01 May 2025 00:00:00 GMT

Interplay between spatial structure and competition in ecological communities Swartz, Daniel W. Ecology, much like physics, has a long history of theoretical contribution. In this thesis, we take a physics approach to describing ecological communities, searching for simple, emergent features that can generalize beyond specific models of community dynamics. Unifying all of the models we study is an underlying spatial structure, leading to a richer set of possible behaviors than a typical well-mixed model. We first study the case of a metapopulation, a collection of smaller communities linked by dispersal. We find that when the environment is allowed to fluctuate stochastically, new growth laws emerge at the single species level, and high diversity is achieved in the case with many species. We then study the case of pathogen evolution, again in the metapopulation framework. We find that intermediate dispersal can act as a strong driver of pathogen evolution. We also study what happens as a population of microbes expands into unexplored territory, known as a range expansion. We find that a simple model can capture all morphological phases observed in experiments and predict invasion fitness as a function of local and global competitive ability. We also break a standard assumption in microbial ecology, the isotropy of space, and find that a new sector morphology emerges.

The Algorithmic Cookbook of Quantum Science: Quantum and Classical Recipes for Computation

Thu, 01 May 2025 00:00:00 GMT

The Algorithmic Cookbook of Quantum Science: Quantum and Classical Recipes for Computation Martyn, John Michael Since the dawn of science, computation and physics have evolved alongside each other, both driven by a shared quest to solve problems and calculate properties of the natural world. Today, this symbiotic relationship is epitomized in quantum information science, which proposes to use quantum mechanics to solve hard computational problems and develop new paradigms of communication and cryptography. Yet often absent from these developments is a clear, human-interpretable understanding, with many quantum protocols built from inherently quantum concepts (e.g., entanglement, superposition) that defy our classical line of thought and muddle the search for efficient quantum algorithms. Here we show that this search need not be so opaque: simple mathematical tools, namely polynomials and their fundamental theorems, in unison with concepts from classical computing, provide a powerful framework for the design of quantum algorithms. We develop this framework and use it to construct an assortment of quantum algorithms, including methods for quantum simulation, parallel computing, randomized algorithms, and continuousvariable quantum hardware. In illuminating this framework, we find a striking bidirectional flow: just as classical concepts inspire new quantum algorithms, so too can quantum mechanical insights bring about novel methods of classical computing. In this reverse direction, we adopt inherently quantum concepts, such as random compilation and bosonic symmetry, to develop new classical methods, with applications in simulating quantum systems and designing robust neural networks. In aggregate, this thesis provides a compendium of algorithmic techniques for probing quantum systems and solving hard problems, using both quantum and classical tools—an “algorithmic cookbook”—predicated on deep connections between these two domains. The recipes presented here aim to demystify black boxes of quantum information science, and provide a valuable resource for future developments.

The Death of Quasiparticles: Strongly Interacting Gapless Phases with Fermi Surfaces and Fractional Statistics

Thu, 01 May 2025 00:00:00 GMT

The Death of Quasiparticles: Strongly Interacting Gapless Phases with Fermi Surfaces and Fractional Statistics Shi, Zhengyan The emergence of quasiparticles at low temperature provides a powerful organizing principle for many quantum phases of matter, ranging from conventional magnets and superconductors to exotic insulators with topological order. In this thesis, I describe my research in gapless quantum phases in which the framework of quasiparticles breaks down. The main characters are two categories of gapless phases that feature the interplay between strong interactions and two additional ingredients – Fermi surfaces and fractional statistics. Chapter 2 through Chapter 5 focus on strongly interacting metals with Fermi surfaces. The most salient examples are a class of Hertz-Millis models describing the onset of spontaneous symmetry breaking in a metallic environment. At the quantum critical point, gapless order parameter fluctuations destroy quasiparticles living on the Fermi surface, giving rise to a strongly coupled non-Fermi liquid metal. A key result of these chapters is the identification of an infinite-dimensional symmetry that survives in these non-Fermi liquid metals despite the death of quasiparticles. This infinite-dimensional symmetry and its quantum anomaly lead to a series of non-perturbative results on thermodynamics and transport, which are confirmed by perturbative diagrammatic calculations in special examples. Chapter 6 through Chapter 8 explore quantum phases in which anyonic quasiparticles with fractional statistics play an essential role. When parameters in the system are tuned to close the anyon energy gaps, the original anyons lose their coherence and a variety of novel phases emerge. A highlight in this direction is a new mechanism for topological superconductivity in itinerant abelian and non-abelian anyon fluids, which could make contact with experiments on doped fractional quantum anomalous Hall states in the near future.

Particles Inside Particles: The Flow of Energy in Quarks, Gluons, and Jets

Thu, 01 May 2025 00:00:00 GMT

Particles Inside Particles: The Flow of Energy in Quarks, Gluons, and Jets Alipour-fard, Samuel This thesis presents the author’s work in developing probes of the inner structure of jets in high-energy particle collisions. We begin by introducing QCD and the scattering of partons (quarks and gluons), discussing jets as theoretical and experimental proxies for partonic physics, and presenting the partonic cascade model of jet formation and jet substructure. Noting the ubiquitous presence of low-energy pollution in particle collision events, in the forms of hadronization, detector effects, the underlying event (UE), and pileup (PU), we then move towards the modern research area of developing pollution-insensitive probes of jet substructure. Pollution-insensitive features of jet substructure are often accessed theoretically either through jet grooming or energyweighted correlation functions. We present the basics of the modern theory of jet grooming as well as the work of the author in developing the Piranha paradigm for continuous jet grooming, introduced by the author in Ref. [1], and explore the formal and phenomenological benefits of continuous grooming techniques as pollutioninsensitive probes of jet substructure. We introduce the basics of the simplest energy-weighted correlation function – the energy-energy correlator (EEC), which probes angular correlations between particle pairs – and discuss its multi-particle analogues. We focus on the efficient and visually intuitive projected and resolved energy correlators introduced by the author in Ref. [2], which provide computationally-realistic, pollution-insensitive probes of angular many-body correlations in QCD jets. Finally, we exposit the generic theory of energy-weighted observable correlations (EWOCs), introduced by the author in Ref. [3], which utilizes the energy weighting of the EEC to provide pollution-insensitive probes of non-angular correlations within jets.

Solid-state cavity quantum electrodynamics with spin ensembles

Thu, 01 May 2025 00:00:00 GMT

Solid-state cavity quantum electrodynamics with spin ensembles Wang, Hanfeng Quantum sensors have the potential to operate at fundamental physical performance limits. Among various quantum sensing platforms, solid-state spin emitters stand out due to advantageous characteristics such as room-temperature spin polarization and readout, atomic-scale spatial resolution, and extended coherence times. Despite these strengths, traditional optical detection methods exhibit low readout fidelity in solid-state ensembles, severely limiting their achievable sensitivity. This thesis addresses this limitation by coupling a solid-state emitter ensemble to a microwave cavity, forming a cavity quantum electrodynamics system. Our approach eliminates the need for photon collection required by conventional optical readout methods, and the resulting strongly coupled system allows efficient cavity-based probing of the solid-state spin ensemble. By exploiting the hybrid quantum system with cavity quantum electrodynamics, we achieve record-high sensitivity for solid-state quantum sensors, representing a substantial advancement toward achieving fundamental sensing limits.

Expanding the Phase Space of Photons in Matter: From High-Throughput Screening to Atom-by-Atom Engineering

Thu, 01 May 2025 00:00:00 GMT

Expanding the Phase Space of Photons in Matter: From High-Throughput Screening to Atom-by-Atom Engineering Ghorashi, Ali Focusing on the topological band properties of photonic crystals and the plasmonic properties of two-dimensional metals, we seek to answer the question: what is the phase space of photons in matter? For topology, what are the physical parameters that determine whether a given photonic crystal band hosts Dirac points, a non-zero Chern number, or topologically protected corner states? And for plasmons, what are the experimentally addressable ranges of plasmonic dispersions, phase velocities, confinements, and losses? In particular, is it possible to engineer the elusive lossless plasmon? Using high-throughput screening, artificial intelligence, and atom-by-atom engineering through density functional theory, we determine the topological prevalence of photonic bands, propose two systems that evade plasmonic losses through the electron-phonon interaction, and (re)discover general physical laws that govern the geometries of photonic eigenstates.

On the Sample Efficiency of Data-Driven Decision Making

Thu, 01 May 2025 00:00:00 GMT

On the Sample Efficiency of Data-Driven Decision Making Qian, Jian This thesis studies the fundamental problem of decision making under uncertainty through the lens of statistical decision theory. We characterize the minimax risk, which captures the sample efficiency required for effective decision making across three key settings: offline estimation with batch data, online estimation with sequential data, and interactive decision making as exemplified by multi-armed bandits and reinforcement learning. The first part of the thesis develops novel algorithmic and theoretical tools to enhance decision making in these regimes and to bridge the gaps between them. We revisit logistic regression in the offline setting and provide guarantees without restrictive boundedness assumptions. We then propose meta-algorithms that reduce online estimation to offline estimation, enabling any offline estimator to be used effectively in online scenarios. Furthermore, we present general-purpose algorithms for interactive decision making problems by leveraging offline or online estimation techniques. The second part of the thesis introduces a unified approach to understanding the fundamental complexity of interactive decision making. We propose the Decision Making with Structured Observation (DMSO) framework, which encompasses bandits, reinforcement learning, and more general settings. Within this framework, we develop a new complexity measure—the Decision-Estimation Coefficient (DEC)—which captures both upper and lower bounds for minimax regret. DEC extends classical notions such as the modulus of continuity to interactive scenarios by introducing an adaptive variant of Le Cam’s method. Finally, we unify the three classical lower bound techniques—Le Cam’s method, Assouad’s lemma, and Fano’s inequality—through a generalized formulation that also incorporates the DEC, offering a comprehensive understanding of the minimax risk in decision making tasks.

Towards achieving power autonomy in soft-actuated micro aerial robots

Thu, 01 May 2025 00:00:00 GMT

Towards achieving power autonomy in soft-actuated micro aerial robots Ren, Zhijian Micro aerial robots with insect-like flight capabilities hold immense promise for various applications, including environmental monitoring, precision agriculture, and infrastructure inspection in confined spaces. However, realizing power autonomy in these miniature robotic platforms presents significant challenges due to weight constraints, power density limitations, and inefficient actuation at small scales. This dissertation presents three essential improvements towards achieving power autonomy in soft-actuated micro aerial robots. Our robotic platform is driven by a dielectric elastomer actuator (DEA) and generates lift force through flapping wings, a similar mechanism found in flying insects. First, we implemented a dynamic model to optimize the robot components for pairing with an improved DEA to generate a higher lift force. The robot achieved a peak lift-to-weight ratio of 4.3 and demonstrated a 20-second hovering flight with position and attitude errors smaller than 2.5 cm and 2◦ . Second, we fabricated a lightweight high-voltage boost converter that transformed a 7 V DC input into an AC waveform of 600 V and 400 Hz to drive the actuator. This is the first onboard boost converter that can drive the soft-actuated micro aerial robot to take off, and it represents a substantial achievement in miniaturizing power electronics for microrobots. Third, we took inspiration from the natural autorotation of maple seeds in their slow descent. We implemented the first samara-inspired mechanism on micro aerial robots, enhancing lift generation while maintaining in-flight attitude stability without feedback control. The 1.22-gram vehicle can stably take off in 1 second with a total input thrust of 1 gram-force. These accomplishments provide a pathway towards achieving power autonomy and open opportunities for developing agile, robust, and autonomous micro aerial robots for diverse applications.

Low-Energy Electron-Photon Interactions in a Scanning Electron Microscope

Thu, 01 May 2025 00:00:00 GMT

Low-Energy Electron-Photon Interactions in a Scanning Electron Microscope Simonaitis, John The interaction of free-electrons with matter and light is among the most fundamental of processes in nature. From the use of free-electrons for atomic imaging, to their use in the generation of high-intensity, tunable light in synchrotrons, the physics of unconfined electrons has wide application. In recent years, there has been a new focus on the quantum nature of individual electrons in electron microscopes to enable further improvements in these technologies. This work takes advantage of developments in ultrafast optics, electron spectroscopy, quantum optics, and nanofabrication to explore various electron-electron, electron-photon, and electron-material interactions. In this thesis, we construct a low-energy, ultrafast scanning electron microscope, using it to explore quantum coherent interactions between electrons, light, and matter. In Chapter 1, we review the history of free electron experiments and how advances in nanofabrication, low-dimensional materials, and ultrafast optics have opened new opportunities for electron-light interactions to a degree not previously possible. In Chapter 2 we discuss experimental forms of quantum electron microscopy known as interaction-free measurement and electron multi-passing. Chapter 3 details a general theory of electron-photon interactions, including simulations with quantum two-level systems and extended optical nanostructures. In Chapter 4, we design and construct a second microscope with ultrafast triggering, an electron spectrometer with sub-eV resolution, nanostructured interaction regions, and active beam alignment. Chapter 5 explores various experimental results, demonstrating enhanced loss spectroscopy of 2D materials, energy resolution of gold nanoparticle plasmons, as well as spectroscopy of time-tagged cathodoluminescence from optical fibers. Finally, in chapter 6 we discuss future perspectives of this approach, analyzing the impact a heralded electron source would have on electron microscopy and lithography.

Studies of Jet Modification in Heavy Ion Collisions with the CMS Experiment

Thu, 01 May 2025 00:00:00 GMT

Studies of Jet Modification in Heavy Ion Collisions with the CMS Experiment Park, Mary Isabelle In the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC), lead ions are collided at ultra-relativistic velocities to produce Quark-Gluon Plasma (QGP), a state of matter where quarks and gluons are deconfined and move collectively. Jets are produced in high-momentum transfer parton scatterings prior to and independently of QGP formation, and serve as natural probes of its properties. As the high-energy partons pass through the QGP, they lose energy through medium-induced gluon radiation and elastic scattering, resulting in jets that are modified with respect to the vacuum baseline. In this thesis, jet modification is quantified by measuring the jet production cross section as a function of jet radius in inclusive jets and the jet axis decorrelation in jets recoiling from isolated photons in Lead-Lead (PbPb) and Proton-Proton (pp) collisions. Both measurements indicate that effects of medium-induced jet broadening may be balanced by survivor bias in PbPb collisions, potentially due to differences in the magnitude of quenching of wide versus narrow jets. The results underline the importance of constraining the initial jet kinematics with bosons, which are unmodified by the QGP.

Understanding Drivers of Stratospheric Ozone Change and Fingerprinting its Recovery

Thu, 01 May 2025 00:00:00 GMT

Understanding Drivers of Stratospheric Ozone Change and Fingerprinting its Recovery Wang, Peidong Stratospheric ozone serves as Earth’s natural protective layer, shielding the surface from harmful ultraviolet radiation. The discovery of the Antarctic ozone “hole” in the late 1980s raised significant societal and scientific concern, prompting the rapid regulation of ozonedepleting substances (ODSs) under international treaties. While the signs of ozone recovery have begun, new challenges continue to arise. This thesis investigates three critical factors driving stratospheric ozone changes and influencing the detection of ozone recovery: (1) ODS emissions, (2) chemical chlorine processes, and (3) internal climate variability. With ODS emissions being regulated under the Montreal Protocol and studies now focusing on illicit new production on the order of tens of gigagrams per year, the ocean’s role as both a natural source and sink of ODSs becomes increasingly important. However, these processes have often been overlooked or highly simplified in past ozone assessments. Using a hierarchy of models, from simple box models to global ocean general circulation models, I quantified the ocean’s uptake and release of various ODSs. Chapter 2 examines the ocean’s uptake of chlorofluorocarbons (CFCs), particularly emphasizing its influence on recent illicit CFC emissions estimation. Chapter 3 extends this analysis to include ocean uptake and potential microbial degradation processes, evaluating their effects on emission estimates for various hydrochlorofluorocarbons (HCFCs) and hydrofluorocarbons (HFCs), which are chemical constituents that have been used to replace CFCs. Once these man-made ODSs reach the stratosphere, they are photolyzed to chlorine reservoir species (e.g., HCl and ClONO2), which, through heterogeneous reactions, can transform into reactive chlorine that depletes ozone. While heterogeneous chlorine activation on volcanic ash is well understood, the unprecedented 2020 Australian wildfires raised new questions about chemical processes on smoke particles. This knowledge gap existed because only a few wildfires had injected significant amounts of smoke particles into the stratosphere during the satellite era. Leveraging over 30 years of satellite data, I separated chemical and dynamic processes affecting chlorine reservoir species to quantify chemical chlorine activation across different aerosol types. In Chapter 4, I developed a new approach to quantitatively estimate the onset temperature for chemical chlorine activation after the 2020 Australian wildfire using satellite observations. Chapter 5 applies this method to compare the impact of chemical chlorine activation from two independent wildfire events with that from a series of volcanic eruptions of varying magnitudes. Despite emerging challenges such as illicit emissions and recent wildfires and volcanic eruptions, advancements in observational records, our understanding of ozone chemistry, and computational power have significantly enhanced our ability to quantitatively detect and attribute stratospheric ozone changes. In Chapter 6, I applied a pattern-based “fingerprinting” technique to quantitatively separate the contributions of ODS forcing from other external forcings and internal variabilities in satellite observations. This analysis shows that Antarctic ozone increases cannot be explained by climate internal variability alone, providing strong confidence that ozone recovery is underway, primarily driven by human efforts to reduce ODS emissions.

Light-Induced Collective Interactions in Arrays of Quantum Emitters

Thu, 01 May 2025 00:00:00 GMT

Light-Induced Collective Interactions in Arrays of Quantum Emitters Rubies-Bigorda, Oriol The interaction between light and matter has captivated physicists for centuries, from early studies of vision and refraction in ancient Greece to the development of quantum mechanics and quantum electrodynamics in the past century. While the response of a single quantum emitter to light is well understood, the radiative properties of an ensemble of closely spaced emitters are far more intricate. Coupling to a shared electromagnetic environment induces coherent and dissipative interactions between emitters, giving rise to a collective response that cannot be captured by treating them independently. In the regime of few excitations, the system hosts delocalized subradiant states, that is, coherent superpositions that are largely decoupled from the electromagnetic field and thus decay at suppressed rates. While this weak coupling makes subradiant states attractive for quantum technologies, it also renders them difficult to manipulate. At higher excitation densities, the intrinsic nonlinearity of emitters and the exponential growth of the Hilbert space make theoretical and numerical descriptions of the system and its dynamics increasingly challenging. This thesis explores two fundamental questions: How can subradiant and dark states be selectively accessed and harnessed for practical applications in quantum technologies? And how can interacting ensembles of quantum emitters be efficiently simulated to uncover their many-body physics? The first part of the thesis develops protocols for controlling and addressing dark states in free-space and waveguide-coupled atomic arrays, demonstrating their utility in quantum storage and the deterministic generation of entangled photonic states. Incorporating atomic motion, we further show that collective subradiant states can enhance cooling in dense atomic arrays, offering new avenues for controlling motional dynamics. In the second part, we introduce cumulant expansions of the equations of motion as a powerful tool to analytically and numerically investigate collective decay in the many-body regime. We first examine the collective decay of fully excited atomic arrays in free space, characterizing the onset and scaling of the superradiant burst across different geometries. In collaboration with experiments on ultracold erbium atoms in optical lattices, we provide the first direct observations of many-body collective effects in free-space ordered arrays, including early-time superradiant bursts, late-time subradiant tails, and the emergence of atomic correlations throughout the dynamics. Finally, we theoretically and numerically explore the transient formation of multi-excitation subradiant states, and demonstrate how the existence of multiple dissipation channels suppresses steady-state superradiance in extended arrays.

Hybrid Core Inductors for High Saturation Capability

Thu, 01 May 2025 00:00:00 GMT

Hybrid Core Inductors for High Saturation Capability Yang, Rachel S. Power electronics are critical for any system requiring electricity and often impact the performance of these systems. In many cases, the performance of power electronics is limited by lossy and large inductors that are constrained by the saturation of their magnetic core material. Such saturation-limited inductors are typically found in power electronics applications where the inductor sees large dc current with relatively small ac ripple, such as EMI filters or converters operating in continuous conduction mode. This thesis investigates two types of inductor designs that can achieve higher saturation capability by combining multiple materials in a single core, enabling these designs to achieve greater energy storage or lower loss than conventional single-material cores. The first design combines a permanent magnet with a soft magnetic material (e.g. ferrite) to form a PM hybrid core. This core achieves higher saturation capability by directing PM flux to oppose winding flux in the ferrite. First-order models, design processes, and other theory for the PM hybrid core are developed in this thesis, and different geometries for this core are explored. Additionally, two PM hybrid core prototypes are presented, one using a pot core geometry and one using a modified E core geometry. The PM hybrid pot core prototype achieves 70% more energy storage or 50% of the dc loss versus comparable ferrite prototypes, while the PM hybrid E core prototype achieves 30% more energy storage or a minimum of 52% of the total loss versus comparable ferrite prototypes. The second design pairs a low-frequency, high-saturation material (e.g. steel) with a low-saturation, highfrequency material (e.g. ferrite) to form a steel hybrid core. This core achieves higher saturation capability by directing most of the dc flux to the steel and all of the ac flux to the ferrite, enabling the core to leverage both materials’ advantages while avoiding their detriments. First-order models and design processes for the steel hybrid core are developed in this thesis. An example steel hybrid core design using a pot core is also presented. This design can achieve 220% more energy storage versus a comparable ferrite prototype, and it may achieve lower loss. Its performance, though, is sensitive to manufacturing and assembly imperfections. In this thesis, both the PM hybrid and steel hybrid cores are demonstrated to have great potential in achieving high saturation capability. By leveraging these hybrid cores, inductor designs can achieve greater energy storage density or lower loss and thus enable higher performance power electronics.

Quantum Gas Microscopy of Bosonic Correlations in the Continuum

Thu, 01 May 2025 00:00:00 GMT

Quantum Gas Microscopy of Bosonic Correlations in the Continuum Xiang, Jinggang This thesis details the complete upgrade and renovation of an existing experimental platform into a high-resolution quantum gas microscope for ultracold 87Rb atoms. Quantum gas microscopes enable site-resolved imaging, providing unprecedented access to quantum statistical effects and many-body phenomena. While such instruments are often employed to study physics in optical lattices, we have innovatively adapted our apparatus to investigate bulk system behavior. A major part of this project involved upgrading the scientific apparatus and retrofitting the previous system. We introduced new optical components, including a high-NA objective, and improved the vacuum system for better optical access. Extensive lab renovations, from upgrading the optical table to reorganizing the laser and imaging setups, were carried out to enhance mechanical and thermal stability. Rigorous optical benchmarking confirmed that the objective achieves diffractionlimited imaging, which is critical for resolving single atoms. This capability allowed us to detect density fluctuations at the scale of the thermal de Broglie wavelength in a quasi-two-dimensional gas of 87Rb atoms. In an experiment resembling Hanbury Brown and Twiss interferometry, we measured a 30% enhancement in the second-order correlation function in situ, demonstrating strong bosonic bunching. This outcome underscores the microscope’s precision and the importance of high-resolution imaging in capturing subtle quantum statistical effects. The successful realization of this apparatus demonstrates the utility of quantum gas microscopes in probing bulk systems. With this new platform in place, future studies can explore critical phenomena, many-body correlations, matter-wave emission, and quantum simulations with cold atoms.

Measurement of Cosmic Ray Lithium Isotopes Using the Alpha Magnetic Spectrometer

Thu, 01 May 2025 00:00:00 GMT

Measurement of Cosmic Ray Lithium Isotopes Using the Alpha Magnetic Spectrometer LaVecchia, Gianni The study of cosmic rays and their properties provides insight into the origins of our universe and is a unique lens on the nuclear physics of the cosmos. The identification of cosmic ray isotopes poses a particular challenge, as it requires the measurement of multiple observables to a high degree of accuracy for the deduction of nuclear mass. Using the unique detection capabilities of the Alpha Magnetic Spectrometer (AMS), the isotope fluxes of cosmic ray lithium in the rigidity range of 1.92 to 25 GV are presented. This work is based on 0.97 million ⁶Li and 1.04 million ⁷Li nuclei collected by the AMS over a 12.5 year period, and improves the error and extent of existing measurements by a factor of 10. These results lead to the conclusion that there is no sizable primary component in cosmic ray ⁷Li. The improvements to the AMS velocity measurement establishes the groundwork for future cosmic ray isotope measurements.

Metrics, Muons, Moments, Models, Machine Learning, Measurements, and More: A Manifesto on Collider Physics

Thu, 01 May 2025 00:00:00 GMT

Metrics, Muons, Moments, Models, Machine Learning, Measurements, and More: A Manifesto on Collider Physics Gambhir, Rikab The interface between particle theory and particle experiments is essential to improving our understanding of the Standard Model and looking for new physics beyond it. At this interface lies a complicated web of complex and expensive simulations that cannot fully be trusted, experimental and theoretical uncertainties, overwhelmingly large amounts of data, all while we have yet to find any deviations from the Standard Model. In this thesis, we propose strategies for improving the theory ↔ experiment pipeline at all stages. We first show how modern Machine Learning and statistical techniques can be used to improve the calibration and resolution of particle detectors in a robust way, which can lead to improved measurement precision. We then develop brand new classes of measurable observables based on the principle of infrared-and-collinear-safety, geometry, and machine learning, which come with guarantees about their theoretical calculability and interpretability, in turn motivating measurements at collider experiments. Finally, we then present two complementary approaches to search for new physics: one, in the form of an experimental proposal for a muon beam dump experiment that is viable alongside a full future collider program; and the other, in the form of machine-learning based anomaly detection to search for subtle signals in already-published data.

Practical Algorithms for Modeling Causality to Accelerate Scientific Discovery

Thu, 01 May 2025 00:00:00 GMT

Practical Algorithms for Modeling Causality to Accelerate Scientific Discovery Wu, Menghua Scientific research revolves around the discovery and validation of causal relationships between variables. Machine learning has the potential to increase the efficiency of this process by proposing novel hypotheses from data observations, or by designing experiments that maximize success rate. This thesis addresses these problems through pragmatic approaches, designed to model large systems and incorporate rich domain knowledge. These algorithms are applied to use cases in molecular biology and drug discovery, which highlight their potential to inform efficient experiment design and to automate the analysis of experimental results.

Recycling and Regeneration of Spent Perfusion Media via Ion Concentration Polarization

Thu, 01 May 2025 00:00:00 GMT

Recycling and Regeneration of Spent Perfusion Media via Ion Concentration Polarization Wynne, Eric Michael The widespread adoption of monoclonal antibody therapies is often constrained by their high prices, which can limit accessibility, particularly for patients in low- and middle-income countries. Addressing this economic barrier is crucial to ensure that life-saving treatments can reach all who need them. We present a series of bioprocessing innovations designed to reduce the cost of monoclonal antibody manufacturing and improve global access to these critical therapeutics. The work focuses on developing technologies for media regeneration and recycling, with the goal of reducing the economic and environmental impact of cell culture media in perfusion mammalian cell culture. We demonstrate a microfluidic separation device engineered to selectively remove metabolic waste products—specifically ammonia and lactate—from spent media using ion concentration polarization. Building on this foundation, a scalable millifluidic system was developed to enable higher-throughput waste removal. We characterized the impact of media recycling upon batch and perfusion cell cultures. We devised a nutrient supplementation strategy to create ‘regenerated’ media that minimized any effect on cell growth and productivity compared to fresh media. To support continuous manufacturing, a perfusion culture system incorporating a microfluidic spiral cell retention device and continuous cell bleed was established, and stable performance was maintained over extended durations. A further innovation introduced a multi-stage waste recovery system that increased media regeneration yield to 87.5%. This recovery rate enabled a self-recycling perfusion bioreactor in which 75% of the media feed was regenerated, without significant impact on cell growth, productivity, or product quality. Together, these advances establish a novel biomanufacturing platform that combines electrokinetic waste removal with media regeneration and recycling. The approach is broadly adaptable to mammalian cell culture processes and offers a promising path toward more sustainable, cost-effective, and environmentally responsible production of monoclonal antibodies and other biologics.

Scaling Cooperative Intelligence via Inverse Planning and Probabilistic Programming

Thu, 01 May 2025 00:00:00 GMT

Scaling Cooperative Intelligence via Inverse Planning and Probabilistic Programming Zhi-Xuan, Tan How can we build cooperative machines that model and understand human minds — machines that assist us with our goals, coordinate on plans, infer the intentions behind our words, and even learn our norms and values? This thesis presents a scalable model-based approach to building such systems via inverse planning and probabilistic programming. First, we introduce a probabilistic programming architecture that implements a Bayesian theory of mind. This architecture, Sequential Inverse Plan Search (SIPS), performs online inference of human goals and plans by inverting a Bayesian model of incremental human planning. By combining high-performance symbolic planners with sequential Monte Carlo (SMC) inference, SIPS achieves faster-than-real-time speed, while scaling to hundreds of possible goals, and remaining robust to human mistakes due to boundedly-rational planning. Second, we present Cooperative Language-guided Inverse Plan Search (CLIPS), a system that integrates SIPS with large language models (LLMs) to model communicative cooperation. By using LLMs as likelihood functions within probabilistic programs, CLIPS can infer human goals from ambiguous instructions, then provide uncertainty-aware assistance with much higher levels of reliability than LLMs can on their own. In addition, CLIPS can be used to infer the shared intentions of communicating agents from their actions and words. Third, we show how inverse planning can model the acquisition of social normativity, formalizing norm-guided societal behavior as a norm-augmented stochastic game (NSG). In NSGs, agents assume that society follows a shared set of social norms, and infer these norms from the actions of other agents. By doing so, agents can rapidly learn cooperative social norms using orders of magnitude less data than model-free approaches. Finally, we present advances in probabilistic programming infrastructure that have enabled architectures such as SIPS and CLIPS. Through interfaces for programmable SMC and probabilistic programming with LLMs, developers can readily compose modeling and inference subroutines when designing probabilistically coherent intelligent systems. Together, these innovations demonstrate the feasibility and scalability of rational AI engineering for cooperatively intelligent machines, while illuminating the computational and algorithmic foundations of human cooperative intelligence.

Atomistic Study of Traveling Skyrmions in Multi-Sublattice Magnetic Materials

Thu, 01 May 2025 00:00:00 GMT

Atomistic Study of Traveling Skyrmions in Multi-Sublattice Magnetic Materials Tremsina, Elizaveta A. The development of novel energy-efficient computing hardware is imperative for the reduction of the carbon footprint and for the extension of computing, mobile and wearable device lifespan. Recent advances have been focused on turning to novel material systems, and one such avenue is magnetic thin films. Bits of information can be encoded by magnetic twisted textures called skyrmions, which can be efficiently driven by applying electrical current. Recently, emphasis has been placed on investigating antiferromagnetic and ferrimagnetic skyrmions, as opposed to the single-sublattice ferromagnetic ones studied earlier, due to their potential for more rapid dynamics and magnetic stability. However, there is a pressing need for a thorough and detailed understanding of the intricacies of skyrmion motion, in particular, limiting velocity, optimization of trajectory, controlled mobility and, notably, the observed dynamic distortions of skyrmion profiles. For this reason, experimental studies are simply not enough to provide a complete picture, since the material parameter space for systems hosting skyrmions is quite large. We perform a comprehensive study combining simulation-based as well as analytical approaches, of the spin-orbit torque motion of skyrmions in a wide host of magnetic materials, ranging from crystalline antiferromagnetic to ferrimagnetic, to ferromagnetic. We systematically analyze the relationship between physical distortions of the skyrmion profiles, based on the action of local Thiele forces, and internal elastic tension forces, providing a quantitative and nuanced explanation of these effects. These results expand the understanding of fundamental properties of magnetic skyrmions, as well as their potential use in spintronics applications.

Transformative Lenses: Empowering Learners with New Perspectives Using Generative AI and Augmented Reality

Thu, 01 May 2025 00:00:00 GMT

Transformative Lenses: Empowering Learners with New Perspectives Using Generative AI and Augmented Reality Leong, Joanne Sau Ling Learning is a fundamental human drive that has been shaped by technological advancements over the years. The emergence of generative AI marks a profound shift—its capacity to produce text, images, and video challenges long‐held beliefs about what only humans could create. This shift creates new opportunities for learning, including enabling the design of more customized and personalized learning experiences. Recognizing that learning is deeply influenced by our perceptions of ourselves, others, and our materials and environments, I propose creating transformative lenses powered by generative AI and augmented reality (AR) to adapt what learners perceive, as a means to empower them with new perspectives. I design and implement a set of novel interactive systems and experiences as case studies that address factors including creativity, communication, and motivation. Studying the use of these systems, I gather early evidence that such lenses can help people to overcome their own limiting thoughts and emotions to move towards realizing their full potential. Reflecting on these case studies, I distill key considerations for designing and applying transformative lenses. Finally, I discuss the broader implications of this work at the evolving intersection of generative AI and learning.

Language Models as Opinion Models: Techniques and Applications

Thu, 01 May 2025 00:00:00 GMT

Language Models as Opinion Models: Techniques and Applications Brannon, William Real-time social media platforms now host the news cycle and shape public opinion, while large language models (LLMs) give us new tools to observe and predict those shifts. This dissertation links the new affordances of social media with the predictive power of LLMs to explain -- and forecast -- opinion change. We first quantify the dynamics of news on an influential social platform, then develop LLM-based tools to forecast persuasion and predict heterogeneous treatment effects (HTEs). Study I — Media tempo and tone. Using 518,000 hours of U.S. talk-radio broadcasts and 26.6 million tweets from elite and mass users, we show that Twitter discourse (i) moves faster at both take-off and fade-out stages of a news event and (ii) sustains greater outrage than radio – despite radio’s often explicitly outrage-focused business model. To our knowledge, this is the first large-scale, data-driven comparison between Twitter and traditional media of both outrage levels and the rate of decay of attention to news. Study II — Zero-shot persuasion forecasting. Across a diverse set of 28 randomized experiments, LLM-based methods outperform an ensemble of strong baselines at predicting HTEs and deliver good performance at predicting average treatment effects (ATEs) — all without any experiment-specific fine-tuning. Study III — Transfer and scaling. Fine-tuning LLMs on contemporaneous news coverage boosts HTE (and ATE) prediction performance greatly, to more than 3x baseline performance. A new minibatch-moment-matching (M3) objective lets us train a 400M-parameter model to nearly match the HTE prediction performance of an 8B model at a fraction of the inference cost. Transfer, however, falters out of distribution on held-out experiments and demographic groups, lending support to contextual theories of persuasion. Overall, we (i) quantify how platform affordances shape the tone and tempo of public discourse, (ii) introduce LLM-based methods that make causal experiments more sample-efficient, and (iii) chart the limits of transfer learning for opinion prediction. Our findings provide practical tools for HTE prediction and help researchers anticipate persuasion dynamics in a media landscape shaped by both humans and machines.

Across the Scales of the Nucleus: Understanding Short Range Correlations from Medium Modification to Probe Independence

Thu, 01 May 2025 00:00:00 GMT

Across the Scales of the Nucleus: Understanding Short Range Correlations from Medium Modification to Probe Independence Denniston, Andrew W. The atomic nucleus presents an intricate system due to the non-linear forces described by Quantum Chromodynamics (QCD) that govern its structure. The range of scales involved is remarkable; the most massive nuclei weigh approximately five orders of magnitude more than the quarks that compose them. The nucleus can be analyzed at various levels, from quarks to hadrons to the nucleus as a whole. Short-Range Correlations (SRCs) within the nucleus play a significant role that spans these diverse scales. At the most fundamental level, SRCs influence the interaction between nucleons. The nucleon-nucleon (NN) interaction, arising from QCD, is crucial in determining nuclear properties. SRCs serve as valuable probes for measuring this NN interaction, as the nucleons within SRCs become effectively decoupled from the rest of the nucleus. Multiple experimental techniques, including electron scattering, have been employed to investigate the NN interaction through SRCs. However, our first project demonstrates that inclusive measurements alone are inadequate to constrain this interaction fully. Moving to the scale of the nucleus, SRCs contribute to the high-momentum tail of the nuclear spectral function. While the low-momentum region is characterized by nucleons exhibiting bulk properties, nucleons begin to pair into SRCs at higher momenta. Our research aims to bridge the understanding between the mean-field portion of the nucleus and its high-momentum SRC components. Additionally, SRCs affect the quark structure of protons, as evidenced by the EMC effect, which indicates that quarks behave differently when protons are embedded within a nucleus—an effect referred to as medium modification. This thesis explores the correlation between SRCs and medium modification across various experimental setups. Finally, we seek to establish an interpretation of the nuclear ground-state. Accomplishing this requires demonstrating that our SRC observables are independent of the probe’s scale and scheme. The concluding project of this thesis illustrates how we utilize triple coincidence quasi-elastic scattering across a range of (Q2 ) values to develop a model-dependent framework for understanding SRC distributions within the nucleus’s ground-state wavefunction.

Discovering and Engineering the Computation Underlying Large Intelligent Agents

Thu, 01 May 2025 00:00:00 GMT

Discovering and Engineering the Computation Underlying Large Intelligent Agents Sharma, Pratyusha The richness of language and intelligent behavior has often been attributed to latent compositional structure. Can we build tools for discovering how deep networks learn and represent this latent structure implicitly? And more importantly, can we use this knowledge to improve generalization in largely structure-less general purpose models or refine our understanding of the world they describe? In this dissertation, I present three perspectives to answer these questions. First, I present experimental methods to functionally characterize the space of learnt solutions in LLMs and demonstrate how this understanding can be used to improve their empirical generalization in a gradient free manner, sometimes by as much as 30% points on language understanding benchmarks. Following that, I show how to decipher the structure of another (black box) language-like system, the naturally arising communication system of sperm whales in the wild, discovering for the first time a unique combinatorial communication system. Finally, I apply insights from these results to equip embodied agents with a latent language of thought—hierarchical and compositional—and show how it can enable long-horizon reasoning and planning in these systems. This dissertation ultimately aims to bridge the gap between natural and artificial intelligence, offering new insights into both the fundamental nature of communication in complex biological organisms in the wild and the development of more powerful, and improved AI systems. A key pattern in the discoveries in this thesis has been how simple structures enable complex externalized behaviors in both biological organisms and AI systems.

Generative Diffusion Models Towards De Novo Protein Design

Thu, 01 May 2025 00:00:00 GMT

Generative Diffusion Models Towards De Novo Protein Design Yim, Jason De novo protein design aims to generate proteins with desired functions by rationally engineering novel protein structures and sequences. The structure requires modeling continuous 3D coordinates of atoms with rigid biochemical constraints of the polymer chain while the sequence is a series of discrete amino acids that should fold into a plausible structure. Understanding the protein function-structure-sequence relationship necessary for protein design is complex, but deep learning has proven promising to learn the relationship from large protein datasets. This thesis aims to develop deep learning models that generate novel structures and sequences that can be guided towards desired functions. We first describe novel generative models that learn to generate protein structures and sequences by developing diffusion models over general state spaces including Riemannian manifolds and discrete tokens. The resulting methods – FrameDiff, FrameFlow, and MultiFlow – demonstrate the ability of diffusion models to extrapolate beyond the training data to generate novel and diverse protein structures and sequences that pass in silico protein design filters. Next, we apply diffusion models to practical protein design challenges by collaborating with experimental and computational biologists to develop RoseTTAFold Diffusion (RFdiffusion). By combining the structure prediction capabilities of RoseTTAFold and diffusion modeling principles, RFdiffusion can generate functional proteins with in vitro validated properties such as high-affinity binders and symmetric protein assemblies. De novo protein design aims to generate proteins with desired functions by rationally engineering novel protein structures and sequences. The structure requires modeling continuous 3D coordinates of atoms with rigid biochemical constraints of the polymer chain while the sequence is a series of discrete amino acids that should fold into a plausible structure. Understanding the protein function-structure-sequence relationship necessary for protein design is complex, but deep learning has proven promising to learn the relationship from large protein datasets. This thesis aims to develop deep learning models that generate novel structures and sequences that can be guided towards desired functions. We first describe novel generative models that learn to generate protein structures and sequences by developing diffusion models over general state spaces including Riemannian manifolds and discrete tokens. The resulting methods – FrameDiff, FrameFlow, and MultiFlow – demonstrate the ability of diffusion models to extrapolate beyond the training data to generate novel and diverse protein structures and sequences that pass in silico protein design filters. Next, we apply diffusion models to practical protein design challenges by collaborating with experimental and computational biologists to develop RoseTTAFold Diffusion (RFdiffusion). By combining the structure prediction capabilities of RoseTTAFold and diffusion modeling principles, RFdiffusion can generate functional proteins with in vitro validated properties such as high-affinity binders and symmetric protein assemblies.

Learning to Solve Long-Horizon Robot Manipulation Problems

Thu, 01 May 2025 00:00:00 GMT

Learning to Solve Long-Horizon Robot Manipulation Problems Yang, Zhutian If we want mobile robots that perform multi-step tasks in visually diverse and geometrically complex environments, we need them to quickly decide what to do and how to do it. Manipulating multiple objects in environments with movable and articulated obstacles over time requires the robot to satisfy constraints like collision-freeness, reachability, and action feasibility. For problems with large state spaces, continuous action spaces, and long decision horizons, the hybrid constraint satisfaction problems induced by planners become combinatorially difficult to solve. In this thesis, I will discuss strategies for using offline learning to speed up deploymenttime planning, i.e., using a plan feasibility predictor, a subgoal generator, or a compositional joint continuous constraint solver. I will also present strategies for chaining policies learned from demonstrations using conditional inputs, such as key poses and natural language, for generalization in real-world environments. With the resulting efficient long-horizon manipulation planning system, we can solve complex robotic manipulation problems faster at deployment time. It can also be used to generate diverse large-scale whole-body trajectories as part of the data mixture for training robot foundation models in embodied reasoning, planning, and acting.

Building small domain-specific masked language models vs. large generative models for clinical decision support and their effects on users.

Thu, 01 May 2025 00:00:00 GMT

Building small domain-specific masked language models vs. large generative models for clinical decision support and their effects on users. Sergeeva, Elena The frequently adopted definition of knowledge defines it as “justified true belief”. As one may notice this definition presents some issues when applied to AI: it is unclear to which degree it is justified to use “humanizing” vocabulary like “belief” or “justification” when describing the performance of an AI system. Traditional explicit knowledge-representation based AI involves reasoning over symbolic representation of statements standing for such “justified true beliefs” [1], the modern connectionist methodology however replaces explicit reasoning with making a prediction based on a set of computations done over weighted continuous representations of the inputs. The continuous representations learned by such systems remain “black box-like”, where the only elements directly understandable by the human user are the model inputs and outputs. In the first part of this thesis I introduce a set of Masked-Language model transformer based models for a diverse set of medical natural language processing tasks including Named Entity Recognition, Negation Extraction and Relation extraction that perform as well or better than bigger prompt-and-generate transformer-based causal language models. In the second part of the thesis, I discuss the modern “prompt-and-generate” approach to natural language processing where both the inputs and the outputs of the model are word-like elements commonly referred to as “tokens”. I explore the nature of token based representation of the input and look at the way token “meaning” is refined at each layer of the successive transformer computation. With respect to the outputs, I explore how people engage with AI generated sequences of tokens that people perceive as “explained” predictions.

Language-Centric Medical Image Understanding

Thu, 01 May 2025 00:00:00 GMT

Language-Centric Medical Image Understanding Wang, Peiqi This thesis advances medical image understanding by leveraging the multifaceted roles of language: as supervision, prior knowledge, and a medium for communication. We introduce three main contributions: (1) a weakly supervised framework that uses language in clinical reports to guide fine-grained alignment between image regions and textual descriptions, (2) an adaptive debiasing method that uses language prior to improve the robustness of learning algorithms under noisy supervision, and (3) a novel approach for calibrating linguistic expressions of diagnostic certainty, enabling more reliable communication of clinical findings. Together, these methods lead to more accurate, robust, and reliable machine learning systems, ultimately streamlining clinical workflows and improving patient care.

Exploring spin physics with ultracold atoms

Thu, 01 May 2025 00:00:00 GMT

Exploring spin physics with ultracold atoms Lee, Yoo Kyung The dynamics of many interacting spins is an active frontier of research; not only can they explain magnetic phenomena, but they also provide paradigmatic models with deep connections to high-T_c superconductivity, optimization problems, neural networks, and more. Experiments with ultracold alkali atoms in optical lattices have realized spin models with great success. In particular, the isotropic Heisenberg model---the XXX model---was realized more than a decade ago. The ⁷Li apparatus described here was the first to realize a tunable, anisotropic Heisenberg model, also known as the XXZ model. In this thesis, I will describe how the capabilities of this apparatus were harnessed to characterize the spin models we realize, employ them to observe new resonances, and to contribute to studies in spin squeezing and fundamental physics. First, I will discuss how we prepared and observed phantom helix states: eigenstates of the XXZ Hamiltonian. Our understanding of the contact interactions and the phantom helix states enabled us to observe long-predicted lattice-induced resonances, whose effects can be leveraged as another knob to tune the XXZ Hamiltonian. Furthermore, our control over the spin system allowed us to generate spin-squeezed states, a paradigmatic form of entanglement for spin ensembles. This is the first time squeezed states were realized with nearest-neighbor contact interactions in a lattice. Finally, our control over the spin degree of freedom and defects in our state preparation allowed us to create pristine periodic lattices with which to study gedankenexperiments in light scattering.

Probing the Diversity of Fast Radio Bursts with CHIME/FRB

Thu, 01 May 2025 00:00:00 GMT

Probing the Diversity of Fast Radio Bursts with CHIME/FRB Shin, Kaitlyn Fast radio bursts (FRBs) are extremely bright extragalactic radio transients that flash for microseconds to milliseconds at a time, most never to repeat again. Encoded in every observed FRB is information from burst propagation effects, giving us clues about their mysterious origins as well as the environments they traveled through. With inferred all-sky rates of hundreds per day, FRBs have held great interest for those interested in extreme astrophysical processes as well as those interested in cosmological properties of the Universe. The Canadian Hydrogen Intensity Mapping Experiment (CHIME)/FRB project has revolutionized the FRB field with its field-leading discovery rate. With CHIME/FRB, we can start to carry out population-level studies of FRBs to constrain their origins and inform their use as cosmological probes. I present the first population-level studies of CHIME/FRB-observed FRBs using the CHIME/FRB Catalog 1 data release and the injections system to account for observational biases. I discover that CHIME/FRB is likely observationally biased against bursts originating from turbulent local environments, and constrain the energy and distance distributions of FRBs. I also present the Catalog 1 dataset updated with channelized raw voltage (“baseband”) data (“BaseCat1”), for which I played a pivotal role. The CHIME/FRB baseband localization pipeline can localize FRBs to arcminute-precision as long as the signal is bright enough to trigger the saving of offline baseband data. I then discuss two single source-studies enabled by the baseband localization pipeline — one discovering repeaters during phases of unusually heightened burst activity, and one using the burst properties of an unusual FRB to probe the properties of its sightline. In the latter study, I constrain the electron density content of a diffuse filamentary structure on the outskirts of the Virgo Cluster, demonstrating the power of FRBs as probes of diffuse media.

DePUDS: Decentralized Prosocial Urban Development System

Thu, 01 May 2025 00:00:00 GMT

DePUDS: Decentralized Prosocial Urban Development System Zhang, Yan Urban areas face severe socio-economic and environmental challenges like housing crises, inequity, and environmental degradation, often worsened by traditional zoning practices. These are typically rigid, inefficient, outdated, and susceptible to obstruction by narrow special interests (NIMBYism), failing to engage the broader community or adapt to evolving needs. This dissertation proposes the Decentralized Prosocial Urban Development System (DePUDS), a novel governance framework designed to overcome these shortcomings by empowering informed collective consensus and including the often-silent majority. DePUDS integrates decentralized technologies like blockchain and smart contracts with structured economic incentives, facilitated through an accessible user-friendly Decentralized Application (DApp) to encourage broad participation. This system fosters transparent, inclusive, and equitable urban development. Its core mechanism, adaptive incentive-based zoning, dynamically aligns developer profitability with community-endorsed priorities—such as affordable housing, public amenities, and sustainability—providing flexibility absent in traditional zoning. Employing advanced agent-based simulations enhanced by large language models (LLMs), this research rigorously assesses DePUDS's effectiveness across two distinct case studies: Kendall Square in Cambridge, MA (a dynamic innovation hub) and the Inner Richmond District in San Francisco, CA (a culturally rich but housing-constrained neighborhood). Simulation results demonstrate DePUDS significantly aligns development outcomes with community preferences. In Kendall Square, targeted incentives substantially increased affordable housing and public amenities without hindering private investment. In the Inner Richmond, substantial community-driven incentives successfully unlocked constrained development, markedly reducing displacement risks, boosting affordable housing, enhancing amenity access, lowering carbon emissions via density, and preserving local cultural assets. The comparative analysis underscores DePUDS's versatility, showing its potential to enhance growth in active markets and stimulate development in constrained ones. Key policy implications point towards structured DApp-based community participation, adaptive incentive zoning, and dedicated funding. While acknowledging practical implementation hurdles (legal, economic, technological), the findings affirm the feasibility, effectiveness, and transformative potential of decentralized, incentive-driven urban governance. This dissertation offers significant theoretical contributions, practical policy guidelines, and future research pathways to foster more inclusive, sustainable, and resilient urban communities.

Biophysical specializations supporting efficiency in neural networks

Thu, 01 May 2025 00:00:00 GMT

Biophysical specializations supporting efficiency in neural networks Toloza, Enrique H.S. Neuroscience and artificial intelligence (AI) research have long enjoyed a synergistic relationship. AI has drawn key inspiration from the organization and function of the brain, while our understanding of the biological processes underlying computation has been profoundly enriched by studying the behavior of artificial systems. As breakthroughs in generative AI continue to transform our world, and as the need for more sustainable artificial neural systems becomes more urgent, the neuro-AI feedback loop has never been more important. AI needs ever more powerful and efficient systems, and neuroscience needs further insight into how our brains work. The development of more brain-like AI promises solutions to both of these problems. Unfortunately, this has thus far been stymied by two critical challenges: 1) how do we identify the features that make a system brain-like and 2) how do we incorporate these features into artificial networks in a useful and interpretable way? To address the first of these challenges, I will use the remarkable structural and biophysical diversity of the brain as an introduction into what it means for a system to be “brain-like.” This will lead us to a discussion of dendrites, the tree-like structures implicated at virtually every length scale of neural computation. Dendrites will thereafter act as the focal point for our study of brain-like computation. Specifically, I will trace how relatively simple biophysical features defined at the subcellular level can transform the computational landscape of large networks of neurons. To address the second of these challenges, it is necessary to discuss several enduring problems in computational neuroscience, broken down as chapters in this thesis. In Chapter 2, I will present the development of a new model of single-neuron dynamics that is realistic enough to capture the rich dynamics of dendritic spiking but efficient enough for use in simulations of thousands of neurons, thereby filling a long unmet need in the field. In Chapter 3, I will describe a solution to the general problem of training neural networks with arbitrary differentiable dynamics, thus opening the door for the study of countless biophysical phenomena in the context of networks that can learn to perform computations. In Chapter 4, I will use these tools to test several longstanding hypotheses regarding the utility of different biophysical features in neurons, performing first-of-their-kind fair comparisons of the computational performance of spiking networks, rate-based networks, and networks with nonlinear and linear dendrites. Finally, in Chapter 5, I will use insights gained from studying dendrites at the network level to provide a new perspective as to how the structural and biophysical diversity of the brain could emerge from a complex interplay of functional pressures (e.g., task demands) and physical constraints (e.g., space and energy). Together, the chapters of this thesis outline a general quantitative framework for building more brain-like AI for use in both AI research and neuroscience. This framework illustrates how biophysical specializations arising at the level of single neurons shape the emergent dynamics of the brain.

On the Learnability of General Reinforcement-Learning Objectives

Thu, 01 May 2025 00:00:00 GMT

On the Learnability of General Reinforcement-Learning Objectives Yang, Cambridge Reinforcement learning enables agents to learn decision-making policies in unknown environments to achieve specified objectives. Traditionally, these objectives are expressed through reward functions, enabling well-established guarantees on learning near-optimal policies with a high probability — a property known as probably approximately correct (PAC) -learnability. However, reward functions often serve as imperfect surrogates for true objectives, leading to reward hacking and undermining these guarantees. This thesis formalizes the specification and learnability of general reinforcement-learning objectives beyond rewards, addressing fundamental questions of expressivity and policy learnability. I examine three increasingly expressive classes of objectives: (1) Linear Temporal Logic (LTL) objectives, which extend conventional scalar rewards to temporal specifications of behavior and have garnered recent attention, (2) Computable objectives, encompassing a broad class of structured, algorithmically definable objectives and (3) Non-computable objectives, representing general objectives beyond the computable class. For LTL objectives, I prove that only finitary LTL objectives are PAC-learnable, while infinite-horizon LTL objectives are inherently intractable under the PAC-MDP framework. Extending this result, I establish a general criterion: an objective is PAC-learnable if it is continuous and computable. This criterion facilitates the establishment of PAC-learnability for various existing classes of objectives with unknown PAC-learnability and informs the design of new, learnable objective specifications. Finally, for non-computable objectives, I introduce limit PAC-learnability, a practical relaxation where a sequence of computable, PAC-learnable objectives approximates a non-computable objective. I formalize a universal representation of non-computable objectives using nested limits of computable functions and provide sufficient conditions under which limit PAC-learnability holds. By establishing a theoretical foundation for general RL objectives, this thesis advances our understanding of which objectives are learnable, how they can be specified, and how agents can effectively learn policies to optimize them. These results contribute to the broader goal of designing intelligent agents that align with expressive, formally defined objectives—moving beyond the limitations of reward-based surrogates.

Solid-State Quantum Memories for Near-Term Quantum Repeaters

Thu, 01 May 2025 00:00:00 GMT

Solid-State Quantum Memories for Near-Term Quantum Repeaters Sutula, Madison M. Over the past decade, quantum computers have emerged as a promising technology to enable transformational advances in information processing and communication and solve problems that are intractable to classical computers. While there is great promise in linking quantum computers together over long distances via quantum channels, these technologies are still under development. Solid-state emitters with coherent spin-photon interfaces, long spin lifetimes, and narrow optical transitions are a leading platform for use as quantum memories in networked quantum repeaters. However, while such emitters have already enabled advanced quantum networking demonstrations in laboratory settings, applying these devices as useful memory devices at scale is a key outstanding challenge. In this thesis, we experimentally investigate solid-state quantum memories for quantum information applications. First, we develop experimental techniques to characterize solid-state emitters with high throughput, enabling both better understanding of the distribution of emitter properties and improved feedback on material preparation and device fabrication. Next, we implement quantum frequency conversion to create a coherent spin-photon interface between silicon-vacancy centers in diamond and optical photons in the low-loss telecom band. Finally, we investigate color centers in other engineering materials, including silicon and silicon carbide, to better understand the fundamental trade space of requirements for solid-state hosts. Together, these efforts represent a significant advance in creating, controlling, and deploying telecom-compatible spin interfaces, paving the way for memory-enabled quantum repeaters.

Principled Approaches for Latency Reduction in Networking Systems

Thu, 01 May 2025 00:00:00 GMT

Principled Approaches for Latency Reduction in Networking Systems Pit-Claudel, Benoit Modern networks face unprecedented challenges due to exponential growth in traffic demands, driven by AI workloads in datacenters and the ubiquitous adoption of cloud services across the internet. This dissertation addresses three critical challenges in network systems: efficient scheduling of inference tasks, performance optimization in hybrid networks, and memory-efficient load balancing in datacenters. First, we introduce Nona, a stochastic scheduling framework that leverages queueing theory to optimize task placement in datacenter environments. By employing randomized algorithms and considering both network and compute constraints, Nona demonstrates multiple orders of magnitude improvements in job completion times while maintaining implementation simplicity. Nona proposes stochastic scheduling, in which the complexity of the scheduling problem is moved to an offline phase. When handling jobs online, stochastic schedulers are oblivious to the instantaneous state of the network and only rely on predetermined allocation probabilities to make lightning-fast decisions. Second, we present LINC, an in-network coding solution designed for hybrid backbone networks. Through comprehensive mathematical analysis and simulation, we highlight the benefits of network coding in cases where no modifications of the end-hosts are possible. Finally, we develop Sirona, a memory-efficient version of a reactive subflow spraying mechanism suited for hardware deployment. We show that Sirona can achieve competitive performance in homogeneous and heterogeneous datacenter networks while keeping a low memory footprint.

Forward Modeling for Bolometry and Disruption Mitigation in Tokamaks or How to Kill Your Plasma With Confidence, Style, and Pizzazz

Thu, 01 May 2025 00:00:00 GMT

Forward Modeling for Bolometry and Disruption Mitigation in Tokamaks or How to Kill Your Plasma With Confidence, Style, and Pizzazz Stein-Lubrano, Benjamin The tokamak is a promising approach to magnetic confinement fusion. Tokamak functionality is threatened by plasma disruption events, which can damage critical machine components. Disruption damage can be mitigated by high-Z impurities, delivered by Massive Gas Injection (MGI) or Shattered Pellet Injection (SPI). Impurities radiate energy out of the plasma and onto the first wall. Evenly distributed radiation causes less damage than unmitigated disruption pathways, which deliver concentrated heat loads. In order to successfully develop and deploy mitigation systems, it is important to accurately measure and characterize disruption radiation. Accurate measurement is challenged by fast disruption timescales and highly asymmetric radiation patterns, which push the time and spatial resolution limits of radiant heat sensors. Previous radiation analysis approaches are typically limited to two dimensions or less by the highly under-determined nature of tomographic reconstruction and limited spatial resolution of sensors. Two dimensional analysis is often inaccurate for disruption radiation, which can be highly three dimensional as a result of localized impurity sources and fast 3D MHD events. In this thesis, I present a new algorithm for 3D radiation analysis in tokamak disruptions, called Emis3D. When Emis3D is applied to mitigated disruptions on the JET tokamak, a significant injection plume radiation effect in mitigated disruptions is revealed. When this effect is included in radiated energy calculations, the mitigated radiation fraction of plasmas with high thermal energy content is significantly improved, indicating that thermal mitigation is more effective than previously thought. Emis3D can also be used as a design tool to evaluate potential radiant heat sensor layouts. When applied to the SPARC tokamak, Emis3D demonstrates that toroidally skewed sensor sightlines improve spatial resolution and reduce blind spots, allowing more accurate measurement.

Understanding the Milky Way with Stars

Thu, 01 May 2025 00:00:00 GMT

Understanding the Milky Way with Stars Ou, Xiaowei "How do galaxies form?" is one of the most important questions in modern astrophysics. Hierarchical growth, the most plausible theory behind galaxy formation, suggests that galaxies, including the Milky Way, assemble through the accretion of smaller systems, over a scaffolding of the invisible Dark Matter. Such growth is evidenced by the differences in stellar structures found in the Galaxy over the last few decades, accelerated most recently by the Gaia space mission. Yet, we still lack a full picture of the formation of the Milky Way and its stellar components, and we are even further in understanding its underlying Dark Matter distribution. For the latter, discrepancies between observations and predictions from CDM model at galactic scales have sparked debate about how well this model accounts for the evolution of the Milky Way. Stellar tracers provide a powerful tool for examining these discrepancies, helping us explore the hierarchical assembly of galaxies in the Local Group and test different models for dark matter. At the same time, cosmological simulations and machine learning techniques offer a bridge between the theory and observations. In this thesis, I combine observation of stellar kinematics and chemistry with cosmological simulations to understand the formation and evolution of the Milky Way and its satellite dwarf galaxies. I map the dark matter distributions in the Milky Way and one of its ultra-faint dwarf galaxies using stellar dynamics, combining simulations of tidal disruption with observational data to study ongoing merger events and how hierarchical assembly shaped the Milky Way today. I conduct robust machine learning searches of kinematic substructures from disrupted dwarf galaxy debris in the Milky Way and utilize stellar heavy element abundances to probe the galaxies that merged with the Milky Way in the past. Lastly, I develop synthetic surveys from simulations to bridge gaps between theory and observation, testing the robustness of current and future methodologies in understanding how the Milky Way came to be.

Coating Thermal Noise in Gravitational-Wave Detectors

Thu, 01 May 2025 00:00:00 GMT

Coating Thermal Noise in Gravitational-Wave Detectors Demos, Nicholas The direct detection of gravitational waves, originating from cataclysmic events such as black hole and neutron star mergers, has ushered in a new era of observational astronomy. These signals offer unique insights into astrophysical phenomena and fundamental physics, but fully realizing their potential requires continued improvements in detector sensitivity. A primary factor limiting the performance of current ground-based interferometers like Advanced LIGO and Advanced Virgo is thermal noise arising from the highly reflective multilayer coatings on the test mass mirrors. Reducing this coating thermal noise, particularly its Brownian component, while simultaneously maintaining exceptionally low optical absorption and scatter is necessary to advance detector capabilities. This thesis addresses this challenge through the characterization and development of alternative coating materials and designs. Central to this work is a dedicated experimental apparatus employing a high-finesse folded optical cavity and a multimode co-resonance technique. This system enables direct, high-precision measurements of coating thermal noise in the frequency band relevant to gravitational-wave detectors and allows for relatively rapid evaluation of candidate coatings, providing timely feedback for materials development. Coating materials such as niobia-based oxides, hafnia-tantala mixtures, and substoichiometric silica, were explored employing strategies like compositional optimization, post-deposition annealing, and multimaterial designs with buried layers. Progress toward lower-noise coatings is demonstrated. Highly reflective coatings based on optimized titania-silica, titania-germania, and ternary silicon nitride structures achieved thermal noise levels approximately 75% that of current detector coatings. These coatings also exhibited exceptionally low optical absorption, reaching levels near 1 part-per-million following appropriate heat treatment. While challenges related to defect formation during annealing and discrepancies between different noise measurement methodologies were identified, ongoing research, particularly on defect mitigation in materials like titania-germania, continues to advance the field. The findings presented here contribute to the materials science foundation for improving current gravitational-wave detectors and guiding the design of future observatories.

Time-Domain Astrophysics with the Transiting Exoplanet Survey Satellite

Thu, 01 May 2025 00:00:00 GMT

Time-Domain Astrophysics with the Transiting Exoplanet Survey Satellite Jayaraman, Rahul The Transiting Exoplanet Survey Satellite (TESS) is conducting an all-sky survey with the primary aim of detecting planets orbiting nearby stars. However, its large field of view and 200 s imaging cadence are useful for other science cases, ranging from stellar astrophysics to transient science. This thesis focuses on using TESS to study both the circumstellar environment and stellar interiors, as well as using the satellite to detect and characterize optical emission from gamma-ray bursts (GRBs). Chapter 2 focuses on the discovery of HD 135348, a "rigidly rotating magnetospheric" star–wherein the stellar magnetic field traps dust in a co-rotating orbit and leads to complex periodic photometric modulations–using solely photometric data. Chapter 3 focuses on the discovery of a long-period subdwarf B (sdB) star using 20 s cadence TESS data and proposes a novel formation mechanism for long-period sdB stars that relies upon stable, nonconservative mass transfer. Chapters 4 and 5 focus on pulsating stars in close binaries, and the evolutionary insights that these "tidally tilted" pulsations enable. In particular, we focus on developing models to track the amplitude and phase of these pulsations as a function of orbital phase, as well as tools to perform physically-motivated modeling of the binary components. Chapters 6-7 focus on the optical signatures of gamma-ray bursts in TESS, and analyze the prompt optical flash that is often observed contemporaneously with the high-energy emission from these bursts. Chapter 7, in particular, aims to connect the prompt optical flash to the high-energy spectral energy distribution (SED), and explains the suppression of the optical flash (compared to the extrapolation of the high-energy SED) by invoking dust extinction in the host galaxy. This thesis represents a significant step forward in both stellar and transient astrophysics; throughout this work, we emphasize the use of an unconventional tool–TESS–to pursue timely scientific questions.

Building Intelligence that can Interact with the Physical World

Thu, 01 May 2025 00:00:00 GMT

Building Intelligence that can Interact with the Physical World Wang, Tsun-Hsuan (Johnson) Recent advances in Artificial Intelligence (AI) have demonstrated remarkable success in parsing, reasoning, and generating digital content across modalities such as natural language, speech, images, videos, and 3D data. However, these breakthroughs have yet to extend meaningfully beyond the digital realm into the physical world. Developing AI for physical interaction poses challenges such as limited grounding, scarce physical data, and high reliability demands in safety-critical settings. This thesis takes a holistic approach to building intelligence that can interact with the physical world – through the lenses of data, brain, and body. Data is the fuel powering highly capable AI systems. We present methods for data-driven simulation that synthesize sensor measurements from physical processes, and knowledge-driven simulation that leverages large language models to generate actor behaviors and scenarios. By reverse engineering the generative processes behind physical data, we address data scarcity while enabling scalable and effective evaluation. The brain, driven by data, demands a deep understanding of the physical world and reliable interaction with it. We introduce methods to bridge the internet-scale knowledge of digital AI with the physical world to improve generalization and interpretability. For greater reliability, we integrate control-theoretic modules into AI models to enable certifiability. Beyond the behavioral intelligence, the body plays a crucial role in physical interaction. We demonstrate how morphological intelligence can emerge from computation and show how pre-trained generative AI models (brain), when augmented with physics-based simulation that provides feedback on generated data, can be applied to robot design. To this end, this thesis explores how digital AI can be extended into the physical world through a comprehensive investigation of data, brain, and body – laying the groundwork for building physical AI.

Biomolecular Modeling at Scale

Thu, 01 May 2025 00:00:00 GMT

Biomolecular Modeling at Scale Wohlwend, Jeremy Predicting the structure and interactions of biomolecules is a fundamental problem in computational biology, with broad implications for disease understanding and drug discovery. Advances in deep learning have enabled remarkable progress, but scaling these approaches to the varied and complex realities of biology is a persistent challenge. This work introduces deep learning methods for biomolecular modeling at scale, designed for efficiency, adaptability, and accessibility. The early chapters present models developed in the general molecular domain, including prediction of structure and interactions for proteins, nucleic acids, and small molecules. To demonstrate how these methods extend to specific biological problems, the latter portion of this work focuses on modeling T cell receptor recognition. As a key immunological mechanism, it highlights the promise of scalable models, but also their present limitations in capturing fine-grained molecular selectivity. Together, these contributions define a framework for bridging foundational models and domain-specific applications, with the potential to scale, and meet the demands of increasingly complex biological systems.

Multimessenger signatures of compact binaries

Thu, 01 May 2025 00:00:00 GMT

Multimessenger signatures of compact binaries Mo, Geoffrey Kwan Lok Gravitational waves and electromagnetic observations provide complementary views into some of the most extreme objects in the Universe. In this thesis, I present studies of multimessenger compact binaries from two angles: electromagnetic follow-up of gravitational-waves, and gravitational-wave follow-up of electromagnetic sources. I first describe technical and computational efforts to enable the distribution of alerts of kHz gravitational-wave sources as a member of the LIGO--Virgo--KAGRA collaboration, and to improve localizations of these events by folding in galaxy catalog information. I then detail work to enable electromagnetic follow-up observations of binary neutron star and neutron star--black hole mergers with two telescopes, the Transiting Exoplanet Survey Satellite (TESS) and the Wide-field Infrared Transient Explorer (WINTER). Approaching multimessenger observations from the opposite direction, I describe a search for gravitational waves coincident with fast radio bursts from the only Galactic fast radio burst source. Lastly, I perform an electromagnetic study of Type Ia supernovae in the mid-infrared, whose white dwarf binary progenitors will be mHz gravitational-wave sources for the future LISA space mission.

Efficient Network Systems Design for Machine Learning

Thu, 01 May 2025 00:00:00 GMT

Efficient Network Systems Design for Machine Learning Yang, Mingran Machine learning (ML) is transforming modern life by powering a diverse range of groundbreaking applications. As ML models and datasets expand, the scale of training and inference workloads in modern datacenters is increasing at an unprecedented pace. As the demand for computing resources grows, the need for low-latency and energy-efficient network systems becomes increasingly urgent. This thesis introduces efficient network systems designed to support machine learning workloads. It presents three key systems: Trio-ML, which accelerates ML training; Lightning, which enhances ML inference efficiency; and on-fiber photonic computing, a forward-looking vision for next-generation computing systems. The first system, Trio-ML, accelerates data-parallel distributed ML training by leveraging in-network computing on Juniper Networks' programmable chipset Trio. Trio-ML features two key designs: in-network aggregation, which utilizes Trio packet processing threads to aggregate gradients directly inside the network, and in-network straggler mitigation, which utilizes Trio timer threads to detect and address stragglers. We prototype Trio-ML on a testbed with three real DNN models (ResNet50, DenseNet161, and VGG11) to demonstrate its effectiveness in mitigating stragglers while performing in-network aggregation. Our evaluations show that when stragglers occur in the cluster, Trio-ML outperforms today's state-of-the-art in-network aggregation solutions by up to 1.8x. The second system, Lightning, is the first reconfigurable photonic-electronic smartNIC to serve real-time ML inference requests. Lightning uses a fast datapath to feed traffic from the NIC into the photonic domain without creating digital packet processing and data movement bottlenecks. To do so, Lightning leverages a novel reconfigurable count-action abstraction that keeps track of the required computation operations of each inference packet. Our count-action abstraction decouples the compute control plane from the data plane by counting the number of operations in each task and triggers the execution of the next task(s) without interrupting the dataflow. We evaluate Lightning's performance using four platforms: prototype, chip synthesis, emulations, and simulations. Our simulations with large DNN models show that compared to Nvidia A100 GPU, A100X DPU, and Brainwave smartNIC, Lightning accelerates the average inference serve time by 337x, 329x, and 42x, while consuming 352x, 419x, and 54x less energy, respectively. Building on the in-network computing and photonic computing concepts discussed in Trio-ML and Lightning, we present a forward-looking vision for future computing systems. We argue that pluggable transponders are a prime platform for performing photonic computing inside the network without having to replace networking switches and routers. Optical transponders are ubiquitous in today's wide-area and datacenter networks, giving us a unique opportunity to re-purpose them for photonic computing. To this end, we introduce on-fiber photonic computing, explore key research challenges in bringing this vision to reality, and discuss real-world applications.

Wireless, Battery-Free, High-Sensitivity 5G RF Energy Harvesters for Next Generation IoT Sensor Tags

Thu, 01 May 2025 00:00:00 GMT

Wireless, Battery-Free, High-Sensitivity 5G RF Energy Harvesters for Next Generation IoT Sensor Tags Yildirim, Deniz Umut The Internet of Things (IoT) is revolutionizing various industries, enabling a new wave of smart applications such as automated asset tracking in warehouses, substation monitoring in smart grids, and precision agriculture. However, as IoT devices proliferate, powering these devices in a sustainable and maintenance-free manner has become a critical challenge. Traditional IoT systems rely on batteries, which present issues of limited lifespan, environmental impact, and maintenance costs, especially in large-scale deployments. As a result, the development of battery-free IoT devices powered by ambient energy harvesting has gained significant attention. Among various energy-harvesting technologies, radio frequency (RF) energy harvesting has emerged as a promising solution for powering IoT devices. By harvesting energy from ambient RF signals in licensed frequency bands, RF energy-harvesting systems eliminate the need for batteries and allow for continuous, maintenance-free operation. This is especially crucial in environments where battery replacement is impractical or impossible, such as in large industrial warehouses, remote infrastructure, and hazardous environments. However, achieving high sensitivity and reliable operation in RF energy-harvesting systems poses several challenges. High-sensitivity rectifiers are required to capture and convert weak RF signals into usable energy, but integrating these rectifiers with ultra-low power baseband data processing circuits remains a significant hurdle. Moreover, antenna-rectifier matching calibration must be compatible with the duty-cycled operation of these tags, where brief communication periods are followed by long charging intervals. Additionally, the antenna system must be robust to detuning when placed on various objects, ensuring that the system can operate effectively in diverse environments. This thesis presents two integrated circuits to work towards these goals. The first chip is designed with the goal of minimizing the charging time as much as possible, which is critical in scenarios such as inventory management in warehouses, and tamper detection. The goal was to achieve < 1-minute charging time while maintaining sensitivity competitive with the state-of-the-art. Unlike previous harvesters that either focused solely on sensitivity without integrating baseband processing and communication, or included those features but considered continuous communication at low sensitivity, the IC developed in this work achieves a sensitivity of −31 dBm and is capable of backscattering data approximately 18 seconds after a cold start. It also provides a detailed description of the difficulty of achieving higher sensitivities at higher 5G frequencies. The second chip in this thesis builds upon the first one and integrates an analog front-end to convert sensor data for environmental monitoring. We implemented an antenna-rectifier calibration method that is maintained as long as there if RF power, even though the tag goes into long charging periods. Even though the charging time, or the data readout interval, for these tags is more relaxed compared to the inventory management applications, we have also developed a design methodology to minimize the energy required to generate a data packet for backscattering, through which we were able to keep the charging time at 4 minutes while having additional functionalities and backscattering at a higher data rate compared to the first chip. Finally, a simple shielding method was implemented to enable the tags to be placed on any objects without resonance frequency detuning. All of these were achieved while still obtaining a sensitivity of −30 dBm, competitive with the state of the art. In addition, the third project investigates the use of heterogeneously integrated “beyondCMOS” devices to enhance overall rectifier performance. These emerging devices, fabricated by Palacios Group at MIT, show promise in overcoming sensitivity limitations commonly found in rectifiers, thereby extending the range and coverage of energy-harvesting IoT systems. We conduct a detailed characterization of these devices, highlighting their unique physical behaviors not present in standard CMOS technology, and provide system-level design guidelines for building improved rectifiers. Preliminary simulation results show that rectifiers using negative-capacitance field-effect transistors (NCFETs) can harvest up to four times as much power than their CMOS-based counterparts, while maintaining the same sensitivity. This thesis outlines the design, implementation, and evaluation of all three systems. The two aforementioned ICs are tested both in simulation and in real-world scenarios such as a typical office environment. Meanwhile, the novel device technologies are explored through simulation, demonstrating their significant potential for next-generation rectifier design.

Additive Manufacturing of Electrical Machines and Electronic Devices

Thu, 01 May 2025 00:00:00 GMT

Additive Manufacturing of Electrical Machines and Electronic Devices Cañada Pérez-Sala, Jorge Recent advancements in the additive manufacture of electronics and electrical machines have led to successful demonstrations of 3D-printed passive (e.g., resistors, capacitors, inductors) and active (e.g., transistors) electronic components, as well as magnetic cores and power transfer devices. However, each new demonstration of 3D-printed functional devices has typically required increasingly specialized and expensive manufacturing hardware. This work opposes that trend by developing a technology capable of fabricating all such devices on a single, affordable machine: a material extrusion 3D printer. Material extrusion stands out among additive manufacturing technologies for its widespread availability and its compatibility with monolithic multi-material manufacturing, essential for the fabrication of functional electromagnetic devices. These attributes, together with its well-established ability to fabricate mechanically functional parts, make material extrusion a promising technology for the single-step fabrication of electronics and electrical machines, and for their monolithic integration into complex devices, such as custom functionalized prostheses, robots, and space exploration hardware. In this research, a desktop 3D printer was transformed into an almost-universal manufacturing machine capable of fabricating a myriad of electrically, magnetically, and mechanically functional devices, using various feedstock formats (e.g., filament, pellets, ink). With this machine, milestones such as the fabrication of the first semiconductorfree, fully 3D-printed logic gates, and that of the first fully 3D-printed motor, have been achieved. Built for under $4000 in parts, the modified 3D printer opens the door to the democratization of electronics and electrical machine manufacturing, empowering institutions and individuals alike, and serving as an educational tool to introduce advanced manufacturing to new generations. Additionally, this work investigates optimization strategies for planar inductors and alternative techniques for the creation of miniaturized, three-dimensional, electrically functional components via two-photon polymerization. By demonstrating novel methods and applications, this thesis advances the state of the art in the additive manufacture of electromagnetic devices and paves the way toward the decentralized fabrication of electrical machines and electronic devices.

A Unified Theory of Representation Learning: How Hidden Relationships Power Algorithms that can Learn without Labels

Thu, 01 May 2025 00:00:00 GMT

A Unified Theory of Representation Learning: How Hidden Relationships Power Algorithms that can Learn without Labels Hamilton, Mark T. How does the human mind make sense of raw information without being taught how to see or hear? This thesis presents a unifying theory that describes how algorithms can learn and discover structure in complex systems, like natural images, audio, language, and video - without human input. This class of algorithms has the possibility to extend our own understanding of the world by helping us to see previously unseen patterns in nature and science. At the core of this thesis’ unified theory is the notion that relationships between deep network representations hold the key discover the structure of the world without human input. This work will begin with a few examples of this principle in action; discovering hidden connections that span cultures and millennia in the visual arts, discovering visual objects in large image corpora, classifying every pixel of our visual world, and rediscovering the meaning of words from raw audio, all without human labels. In the latter half of this thesis, we will present two unifying mathematical theories of unsupervised learning. The first will explain why relationships between deep features can rediscover the semantic structure of the natural world by connecting model explainability, cooperative game theory, and deep feature relationships. The second mathematical theory will show that relationships between representations can be used to unify over 20 common machine learning algorithms spanning 100 years of progress in the field of machine learning. In particular, we introduce a single equation that unifies classification, regression, large language modeling, dimensionality reduction, clustering, contrastive learning, and spectral methods. This thesis uses this unified equation as the basis for a “periodic table of representation learning” that predicts the existence of new types of algorithms. We show that one of these predicted algorithms is a state-of-the-art unsupervised image classification technique. Finally, this work will summarize the key findings and share ongoing and future directions.

Score Estimation for Generative Modeling

Thu, 01 May 2025 00:00:00 GMT

Score Estimation for Generative Modeling Jayashankar, Tejas Kumar Recent advances in score-based (diffusion) generative models have achieved state-of-the-art sample quality across standard benchmarks. Building on the remarkable property of these models in estimating scores, this thesis presents three core contributions: 1) new objectives to reduce score estimation error, 2) a novel Bayesian-inspired optimization framework for solving inverse problems, and 3) a fast one-step generative modeling framework that is based on a novel amortized score estimation framework. In the first part of this thesis, we introduce two new score estimation objectives with applications to both implicit and diffusion-based generative models. To improve spectralbased non-parametric estimators, we propose a theoretically optimal parametric framework that learns the score by projecting it onto its top-L principal directions. Additionally, inspired by matrix-valued kernel methods, we present a second approach that lifts the score into the space of outer products, and minimizes the distance between the estimated and true scores in this higher-order space. In the second part, we shift focus from score estimation to leveraging diffusion models as data-driven priors for solving inverse problems. Centering our development around the problem of source separation, we introduce a novel algorithm inspired by maximum a posteriori estimation. This approach combines multiple levels of Gaussian smoothing with an α-posterior, enabling effective signal separation using only independent priors for the sources. We demonstrate the effectiveness of this method through its application to interference mitigation in digital communication signals. Finally, we outline how this framework can be naturally extended to tackle a broader class of inverse problems. In the final part, we return to the fundamental challenge of efficient sampling, which is critical for enabling practical data-driven engineering systems. We propose a novel generative modeling framework that enables training a one-step neural sampler from scratch. At the core of this method is a new objective based on multi-divergence minimization, guided by a novel approach for score estimation of mixture distributions. Our framework is simple to implement, stable during training, unifies several existing approaches, and achieves state-of-the-art performance in image generation tasks. Furthermore, we discuss how this framework can be naturally extended to multi-step neural sampling and adapted for fast posterior sampling—an essential component in simulation-based inverse problem solvers.

Superconducting Nanowire Integrated Circuits for Scalable Cryogenic Memory

Thu, 01 May 2025 00:00:00 GMT

Superconducting Nanowire Integrated Circuits for Scalable Cryogenic Memory Medeiros, Owen A. Superconducting nanowire integrated circuits (SNICs) are a promising class of cryogenic electronics that harness the zero resistance, high kinetic inductance, and nanoscale geometry of ultrathin superconducting wires to implement logic, memory, amplification, and sensing with minimal energy dissipation. Unlike Josephson-junction-based circuits, SNICs support compact, planar layouts compatible with single-layer fabrication and operation in unshielded cryogenic environments. This thesis develops superconducting nanowire memory (SNM) as a scalable implementation of SNICs. A modular cell architecture is introduced, exploiting hysteretic switching and inductive asymmetry to enable nonvolatile digital state storage with zero static power consumption. A hierarchical design framework is established, combining automated layout generation, electrothermal simulation in LTspice, and microscopic modeling using the time-dependent Ginzburg–Landau (TDGL) formalism. To enable scalable integration, this work implements a row–column SNM array layout and demonstrates fabrication across full 4-inch wafers using a planar, singlelayer process. Cryogenic measurements validate reliable operation in both single cells and multi-cell arrays, confirming the predictive accuracy of the design and modeling framework. Tradeoffs in bias current levels, pulse timing, and read/write conditions are systematically evaluated through cryogenic measurements, revealing their impact on bit error rate, operational margins, and energy efficiency across single cells and arrays. Together, these contributions establish SNICs as a viable and extensible platform for cryogenic memory, providing the tools, models, and infrastructure needed to enable broader adoption in quantum computing, neuromorphic systems, and other energy-constrained cryogenic applications.

Next Generation Operating Systems for the Datacenter

Thu, 01 May 2025 00:00:00 GMT

Next Generation Operating Systems for the Datacenter Fried, Joshua Modern datacenters face a fundamental challenge: handling demanding real-time and dataintensive workloads that require both microsecond-scale low latency and high throughput, while simultaneously achieving high resource utilization and efficient multi-tenancy. Traditional operating systems, designed for an era of slower hardware, introduce significant overheads to microsecond-scale I/O that prevent applications from exploiting the full performance of the underlying hardware. Furthermore, their millisecond-scale resource management is ill-equipped to handle the microsecond-level burstiness of modern workloads, leading to costly overprovisioning and idle resources. Recognizing the performance limitations imposed by traditional OSes, a common workaround has emerged: letting applications communicate directly with hardware, bypassing the OS entirely. While this approach offers performance gains by removing the OS from the critical path, existing kernel-bypass solutions require dedicated resources, extensive application rewrites, and provide weak isolation, making them unsuitable for general-purpose, shared environments. This thesis presents a new datacenter operating system, composed of three integrated systems: Shenango, Caladan, and Junction. Together, they preserve the high-performance, low-overhead I/O benefits of kernel bypass, while providing efficient resource management, strong isolation for multi-tenant workloads, and compatibility with unmodified software. First, Shenango enables applications to bypass traditional OS-mediated I/O without dedicating CPU cores solely to polling. Next, Caladan ensures that idle resources can be used productively by other applications by actively managing competition for microarchitectural resources, thereby preserving each application’s high I/O performance and responsiveness. Finally, Junction overcomes several common limitations of kernel-bypass solutions, bringing these benefits to all applications by preserving compatibility with existing software and reducing memory and polling overheads. Collectively, these systems provide the advantages of direct hardware access without sacrificing the flexibility or efficiency of a general-purpose operating system. This work demonstrates that high I/O performance, efficient resource utilization, and broad application compatibility can indeed coexist, paving the way for a new generation of datacenter operating systems.

Systematic Development of Healthcare AI: From Data Curation, Algorithm Optimization, Benchmark Design and Clinical Applications

Thu, 01 May 2025 00:00:00 GMT

Systematic Development of Healthcare AI: From Data Curation, Algorithm Optimization, Benchmark Design and Clinical Applications Gao, Mingye Artificial intelligence (AI) has brought transformative changes to healthcare industry in the recent years from various aspects, such as patient care, disease diagnosis and medical research. As healthcare systems worldwide face increasing pressure from aging populations and rising chronic disease rates, there is an urgent need for systematic approaches to develop reliable and safe AI solutions. This thesis advances the systematic development of healthcare AI through four interconnected components: data curation, algorithm optimization, benchmark design, and clinical applications. The primary contribution of this thesis focuses on establishing a comprehensive pipeline for healthcare large language models (LLMs), spanning from data curation to clinical deployment. At the data level, a rule-based filtering framework was developed to select high-quality subsets from the large pre-training corpora, significantly improving both continue pre-training and fine-tuning performance of LLMs. For safety alignment, an automated pipeline was developed for preference learning that includes preference dataset synthesis, rule-based and data-adaptive annotation, and reward model training. Additionally, two novel benchmarks were created to ensure reliability and safety of LLMs in healthcare tasks: one assessing demographic biases of LLMs across common diseases, while another assessing models’ ability to reject illogical requests from users in drug-related scenarios. Finally, LLMs were used to generating patient-friendly educational content for clinical trials, demonstrating their role in improving patient education and engagement in clinical trials. This systematic progression from data to deployment establishes a blueprint for developing safe and effective language models in healthcare settings. Beyond language models, machine learning techniques were applied on an additional healthcare task. In this project, a novel approach combining normalized cross-correlation and attention graph convolutional recurrent networks was developed to realize contactless, continuous and reliable radar-based vital signs monitoring in dynamic home environments. Through systematic data collection and algorithm optimization, the accurate heart rate can be obtained across varying radar-subject distances (2-2.5m) and subject orientations, demonstrating robust performance in real-world conditions through extensive validation in four test houses with six subjects. Collectively, these contributions advance healthcare AI development across 2 fronts: establishing frameworks for safe and effective deployment of language models in healthcare settings and enabling reliable and continuous health monitoring at-home without wearable devices.

Modeling Biomolecular Interactions with Generative Models

Thu, 01 May 2025 00:00:00 GMT

Modeling Biomolecular Interactions with Generative Models Corso, Gabriele In 2021, DeepMind’s AlphaFold2 revolutionized single-chain protein structure prediction achieving atomic accuracy, solving a longstanding challenge in biology. However, understanding biomolecular interactions, a critical problem for advancing drug discovery and biological research, remained unsolved. This thesis presents our research to redefine the machine learning approach to this problem, modeling structures with a new generative paradigm and tailoring the neural architectures and learning tasks to the specific challenges that arose. These ideas combined with significant engineering efforts led us to develop a class of open-source models from DiffDock to the recent Boltz-1. These have significantly pushed our ability to understand biomolecular interactions, they have been widely adopted in industry and academia to help with drug development and protein design and they have opened the door to new research paradigms to push biological research further.

Designing Hardware Accelerators for Solving Sparse Linear Systems

Thu, 01 May 2025 00:00:00 GMT

Designing Hardware Accelerators for Solving Sparse Linear Systems Feldmann, Axel Solving sparse linear systems is a key primitive that sits at the heart of many important numeric algorithms. Because of this primitive’s importance, algorithm designers have spent many decades optimizing linear solvers for high performance hardware. However, despite their efforts, existing hardware has let them down. State-of-the-art linear solvers often utilize < 1% of available compute throughput on existing architectures such as CPUs and GPUs. There are many different algorithms used to solve sparse linear systems. These algorithms are diverse and often have very different computational bottlenecks. These include low arithmetic intensity, fine-grained parallellism, tight dependences, and sparsity-induced load imbalance. This thesis studies the problem of designing hardware accelerators for sparse linear solvers. We propose three novel architectures that explore different parts of the design space. The accelerators exploit static sparsity as the basis of novel hardware-software co-designed scheduling approaches. First, we introduce Spatula, an architecture designed to accelerate direct solvers. Then, we propose Azul, a hardware accelerator targeted at iterative solvers. Taken together, Spatula and Azul demonstrate significant speedups on both of the main classes of sparse linear solver algorithms. Finally, to show that our techniques are useful for end-to-end applications, we present Ōmeteōtl, an accelerator targeted at applications that use iterative solvers in their inner loop. Ōmeteōtl also shows that the techniques in this thesis generalize to sparse matrix computations beyond linear solvers. These accelerators deliver order-of-magnitude speedups over state-of-the-art GPU baselines, achieving > 100× speedups on many inputs.

Theoretical Foundations for Learning in Games and Dynamic Environments

Thu, 01 May 2025 00:00:00 GMT

Theoretical Foundations for Learning in Games and Dynamic Environments Golowich, Noah Decision-making problems lie at the heart of numerous aspects of human and algorithmic behavior across our society, ranging from healthcare systems to financial systems to interactions with the physical world. A central challenge that arises across many decision-making problems is the presence of multiple agents, often with competing incentives. To understand how agents will act in such situations, it is often productive to compute equilibria, which have the property that no agent can deviate from them and improve their utility. An additional challenge is that decisions made by agents often change the state of the environment, which is modeled as dynamic. Thus, we need efficient algorithms for learning good policies, which tell the agent what to do as a function of the environment’s state. Extensive work spanning multiple domains such as economics, computer science, and statistics has been developed to model these decision-making problems. This has led to many celebrated results, which include, for instance, a considerable body of work studying the computational properties of Nash equilibria in normal-form games, and a long line of papers on reinforcement learning. However, many of these classical works suffer from a few shortcomings: first, they often do not account for the enormous state or action spaces available to agents in realistic decision-making settings, and second, many of them do not derive computationally efficient algorithms for the desired solution concepts. These shortcomings are brought to the forefront by the remarkable recent progress in artificial intelligence, which holds promise for solving decision-making problems with enormous state or action spaces but which is often bottlenecked by computation. The objective of this thesis is to develop theoretical foundations for the computational aspects of such decision-making problems: e.g., How do we efficiently compute equilibria in large games?, and: How can we efficiently learn near-optimal policies in complex environments? Some highlights of our results are listed below—first, we study problems in which there are multiple agents and the goal is to compute some notion of equilibrium: • We show the first near-optimal rate of convergence to equilibrium for a no-regret learning algorithm in normal-form games, resolving a decade-long line of work which had aimed to establish increasingly better rates. • We establish the first algorithm with sublinear swap regret against arbitrary adversaries enjoying only polylogarithmic dependence on the number of actions, resolving a question of Blum and Mansour from 2007. • As a corollary of the preceding result, we obtain the first polynomial-time algorithm for approximating a correlated equilibrium in extensive-form games (to constant approximation error), addressing a question of von Stengel & Forges from 2008. Additionally we obtain near-optimal bounds on the communication and query complexity of approximating correlated equilibria in normal-form games (to constant approximation error), addressing several open problems in the literature. • We give the first algorithm for the sequential calibration problem with calibration error beating that of the seminal work of Foster & Vohra from 1998. Moving on to decision-making problems where the environment is modeled as dynamic (typically studied in the framework of reinforcement learning (RL)), our results include the following: • We give the first end-to-end computationally efficient algorithms for learning a nearoptimal policy in many fundamental reinforcement learning problems, such as those of (constant-action) Linear Bellman Complete MDPs and sparse linear MDPs. • We give the first quasi-polynomial time algorithm for finding a near-optimal policy in a general and well-motivated class of partially observable RL environments, and show that our bound is tight. • We prove some (perhaps surprising) hardness results that arise in multi-agent RL problems. For instance, we show that it is computationally hard to implement noregret learning algorithms in multi-agent RL environments even when the agents can coordinate on their choice of algorithm, which creates a stark contrast with simpler multi-agent learning settings (e.g., in normal-form games) where no-regret learning has formed the bedrock for a wide array of developments over the last several decades. • Nevertheless, we show that by adjusting the type of equilibrium appropriately, we can circumvent the above hardness results and derive computationally efficient decentralized algorithms for computing equilibria in multi-agent RL environments. Many of the above results have inspired follow-up work which includes applications of our results to various problems in game theory, reinforcement learning, online learning, and related domains, as well as the formulation of new problems which are inspired by the above results.

Characterizing human vision through large-scale brain imaging and computational models

Thu, 01 May 2025 00:00:00 GMT

Characterizing human vision through large-scale brain imaging and computational models Lahner, Benjamin Efforts to understand the neural underpinnings of human visual processing require sufficient experimental data and robust models. This thesis significantly contributes to both these fronts while simultaneously elucidating some of the most intriguing aspects of the human visual system. In the first chapter, I use a combination of classical machine learning, artificial neural networks, and a joint MEG/fMRI neuroimaging dataset to reveal that the human visual system extensively processes highly memorable images in regions distributed throughout visual cortex late in time. In the second chapter, I present the BOLD Moments Dataset, a large-scale fMRI dataset using short video stimuli to extend computational models of visual processing into the video domain to better understand how humans process visual content unfolding over time. The last chapter introduces a fMRI dataset aggregation framework titled MOSAIC to achieve the scale and stimulus diversity needed for training modern neural networks directly on brain responses. This body of work exemplifies how large-scale experimental data and artificial neural networks can contribute towards a robust and generalizable understanding of human visual processing.

Wireless Systems for a Sustainable Future: From Battery-Free Subsea IoT to THz-Based Agriculture Monitoring

Thu, 01 May 2025 00:00:00 GMT

Wireless Systems for a Sustainable Future: From Battery-Free Subsea IoT to THz-Based Agriculture Monitoring Afzal, Sayed Saad This thesis describes how wireless sensing can drive significant advancements in climate and sustainability. Specifically, it shows how we can leverage diverse signals—acoustics, ultrasound, THz, and optics— in unconventional ways to unlock new capabilities in underwater climate monitoring, food safety, and disaster response. The thesis introduces two novel technologies. The first technology enables long-term, ultra-low power ocean sensor networks for use in climate modeling, marine monitoring, and sustainable aquaculture. Unlike existing IoT technologies – like Bluetooth, WiFi, and GPS – which cannot work underwater, we design and implement an ultra-low power subsea backscatter communication system, enabling battery-free underwater imaging, sensing and localization. Second, the thesis describes a new technology that can support sustainability in agriculture through real-time food quality assessment that reduces food waste. In contrast to existing food quality technologies that require direct contact with produce, we introduce a new wireless system for accurate, non-invasive sensing using sub-THz signals. We describe the design, implementation, and evaluation of multiple systems that leverage these technologies to monitor the ocean and food waste: First, we present a ultra-wideband metamaterial sensor design that facilitates scalable, and long-range battery-free underwater communication. Next, we describe a system that can push the throughput of this technology using higher order modulation. Beyond building sensor networks, we demonstrate their real-world potential through two systems: one for underwater localization that uses rich spatio-temporal-spectral features for accurate positioning, and another for battery-free imaging that fuses acoustic and optical signals to capture color images in the dark. Finally, we present a novel solution for accurate fruit ripeness sensing using sub-terahertz wireless signals. These systems unlock new IoT applications in climate modeling, aquaculture, robotics, and agriculture.

Automatic Integration and Differentiation of Probabilistic Programs

Thu, 01 May 2025 00:00:00 GMT

Automatic Integration and Differentiation of Probabilistic Programs Lew, Alex K. This thesis addresses the challenge of automating fundamental operations from probability theory and calculus on probability distributions defined by higher-order probabilistic programs. It does this by developing a suite of composable program transformations for an expressive core calculus for probabilistic programming: • Integration: Compiling a probabilistic program into a deterministic representation of its expectation operator, handling potentially intractable integrals symbolically. • Unbiased estimation: Transforming programs involving intractable operations (like integration) into runnable probabilistic programs that yield provably unbiased estimates of the original value, with flexible levers for users to navigate cost-variance trade-offs. • Radon-Nikodym differentiation: Compiling probabilistic programs into implementations of a novel interface for the unbiased estimation of density ratios, of the sort that arise in Monte Carlo and variational inference. • Differentiation: Extending automatic differentiation (AD) to compose with the above transformations, enabling the optimization of expected values and density ratios of probabilistic programs. These transformations operate on an expressive higher-order probabilistic programming language and are proven correct using denotational semantics and logical relations. The resulting framework enables the sound and automated implementation of a wide range of algorithms for probabilistic inference and learning. To demonstrate the practical value of these techniques, we use them to implement three systems for scalable probabilistic inference in different domains: (1) extensions to the Gen probabilistic programming system that accelerate and automate a broad range of Monte Carlo and variational inference algorithms, (2) the PClean system for automated Bayesian reasoning about relational data, and (3) the GenLM system for controllable generation from language models. We find that our techniques enable these systems to scale to a variety of complex, real-world problems, and to achieve state-of-the-art performance on a range of benchmarks.

Minimizer-space computation

Thu, 01 May 2025 00:00:00 GMT

Minimizer-space computation Ekim, Barış C. As the volume of DNA sequencing data increases, the need for algorithmic advances to efficiently handle the data arises. One such concept is minimizers, which are genomic substrings that allow for reduced representations of larger DNA sequences. In this thesis, we introduce minimizer-space computation as a new algorithmic paradigm for DNA sequence analysis. Instead of DNA nucleotides, we consider minimizers as the letters of an extended alphabet in which algorithms operate. We present several techniques on how to efficiently construct these extended alphabets, demonstrate how to develop approaches that use these alphabets and consequently use only a fraction of sequence data, and show how fundamental biological tasks, such as genome assembly and read mapping, can be significantly accelerated over state-of-the-art methods.

Performant and Resilient Service Composition for Modern Cloud Applications

Thu, 01 May 2025 00:00:00 GMT

Performant and Resilient Service Composition for Modern Cloud Applications Li, Tianyu Modern cloud applications are often distributed systems composed from vendor-provided building blocks (e.g., object storage services, container orchestration services). Consequently, distributed fault-tolerance is a central concern for application correctness. Although each building block may offer individual fault-tolerance, the end-to-end application is still susceptible to failures, because the composition logic that orchestrates them may still fail. This thesis explores resilient composition, a systematic way to assemble fault-tolerant components into resilient end-to-end distributed applications. We begin by presenting the fail-restart system model, which captures the unique fault-tolerance challenges that arise when composing services. Based on this model, we define Composable Resilient Steps (CReSt), an atomic programming abstraction that guarantees fault-tolerance across the assembled application. We then detail efficient methods for implementing CReSt using a range of database techniques, and a novel distributed protocol that allow optimistic, speculative execution ahead of slower fault-tolerance safeguards. Together, these pieces allow developers to assemble fault-tolerant distributed systems that are correct by construction and often more performant than existing solutions.

Succinct Cryptography via Propositional Proofs

Thu, 01 May 2025 00:00:00 GMT

Succinct Cryptography via Propositional Proofs Mathialagan, Surya The goal in modern cryptography is to obtain security while minimizing the use of computational resources. In recent years, we have been incredibly successful in our pursuit for efficiency, even for cryptographic tasks that were thought to be “science fiction”. For example, we have constructions of fully homomorphic encryption and private information retrieval from standard, cryptographic assumptions which achieve the ideal levels of succinctness. However, there are still some tasks in cryptography where achieving the “ideal” efficiency from standard assumptions has evaded us. In this thesis, we study the problem of achieving succinctness in two such settings: • Can we construct succinct indistinguishability obfuscation (IO) for Turing machines? In particular, can we construct an obfuscated program whose size is independent of the input length? • Can we construct succinct non-interactive arguments (SNARGs) for all of NP? While the problems seem unrelated at first glance, the root difficulty seems to stem from a similar place: both primitives have non-falsifiable security definitions. In fact, this type of barrier exists for many other cryptographic primitives, including witness encryption. This leads to a central question which we refer to as the “non-falsifiability barrier”: how can we construct non-falsifiable primitives from falsifiable assumptions? In this thesis, we show how to leverage propositional proofs to overcome the non-falsifiability barrier, and make substantial progress in the goal of achieving succinctness in both settings. Our main result is universal construction of both SNARGs and succinct IO for Turing machines from standard assumptions using propositional proofs. We then show several applications, including rate-1 IO for many programs, the first succinct secret sharin schemes for monotone circuits, and many more. Our results establish propositional proofs as a foundational tool for achieving succinctness across a broad range of cryptographic settings.

Statistical and Algorithmic Thresholds in Spin Glasses

Thu, 01 May 2025 00:00:00 GMT

Statistical and Algorithmic Thresholds in Spin Glasses Huang, Brice This thesis studies spin glasses, disordered complex systems originating in statistical physics. Such systems model optimization, sampling, and inference problems from probability and statistics, which are of fundamental importance to modern data science. In particular, spin glasses provide natural examples of random, high-dimensional, and often highly non-convex cost or log-likelihood functions, making them an excellent testing ground for such questions. Part I of this thesis studies statistical properties of these models. Chapter 2 identifies the storage capacity of the Ising perceptron, a simple model of a neural network, subject to a numerical condition. This gives a conditional proof of a 1989 conjecture of Krauth and M´ezard. Chapter 3 gives a new proof of the celebrated Parisi formula for the free energy of the spherical mean-field spin glass, which was first proved by Talagrand and in more generality by Panchenko. Our proof takes a simpler modular approach, drawing on recent advances in spin glass free energy landscapes due to Subag. Chapter 4 characterizes the topology trivialization phase transition of multi-species spherical spin glasses and shows that lowtemperature Langevin dynamics finds the ground state in the topologically trivial regime; the latter result is new even in the single-species setting. Part II of this thesis concerns algorithms for optimization and sampling problems on spin glasses. Chapter 5 studies the problem of optimizing the Hamiltonian of a multi-species spherical spin glass. Our main result exactly characterizes the maximum value attainable by a class of algorithms that are suitably Lipschitz in the disorder. This class includes gradient-based algorithms and Langevin dynamics on constant time scales, and in particular includes the best algorithm known for this problem. This chapter is part of a series of works where we establish exact algorithmic thresholds using the branching overlap gap property (OGP), a landscape property introduced in our earlier work (which appears in our S.M. thesis). In this chapter, we develop a more robust way to establish the branching OGP that does not require Guerra’s interpolation; this allows our method to be applied to models well beyond the (single-species) mean-field spin glass we previously considered. Chapters 6 and 7 study sampling from the Gibbs measure of a spherical mean-field spin glass. Chapter 6 develops a sampling algorithm based on simulating Eldan’s stochastic localization scheme, while Chapter 7 analyzes simulated annealing of Langevin dynamics. We prove both algorithms succeed for inverse temperatures up to a stochastic localization threshold. Chapter 6 gives the first stochastic localization-based sampler with a guarantee of vanishing total variation error, improving on earlier algorithms with vanishing Wasserstein error. Chapter 7 provides the first provable guarantees for a Markov chain in this model beyond the uniqueness threshold, where mixing from worst-case initialization is provably slow.

A Cavity-Coupled Rydberg Atom Array for Quantum Science and Quantum Computing

Thu, 01 May 2025 00:00:00 GMT

A Cavity-Coupled Rydberg Atom Array for Quantum Science and Quantum Computing Hu, Beili Neutral atom arrays have rapidly emerged as a leading platform for quantum computing, boasting scalable, configurable arrays of single atoms trapped in optical tweezers, fast, high-fidelity entangling gates through Rydberg interactions, and programmable, parallelized control of qubit operations. Coupling an atom array to an optical cavity opens a new frontier. Leveraging enhanced light-atom interactions in cavity quantum electrodynamics, cavity- coupled atom arrays acquire capabilities that can further expand the neutral atom toolbox, including cavity-enhanced atom readouts, atom-photon entanglement, and photon-mediated interactions between distant atoms. This thesis presents a quantum hardware platform that integrates an array of neutral atoms with a high-finesse optical cavity. After describing the design and development of the experimental apparatus, I demonstrate high-fidelity atom state readout through the cavity, achieving improved speed and atom survival compared to conventional free-space imaging methods. I then introduce a new technique for selectively controlling atom-cavity coupling on arbitrary subsets of the array, using local AC Stark shifts on the excited states of the atoms. Building on these tools, I demonstrate fast, non-destructive cavity-based readout of atom arrays, a crucial bottleneck of atom array platforms. I also showcase real-time measurement and feedback capabilities with a demonstration of classical error correction, using a register of atomic bits. Finally, I describe progress toward implementing single- and two-qubit gates within the cavity-coupled system. By combining coherent control, tunable interactions, and high-fidelity, non-destructive readout integrated and real-time feedback, the cavity-coupled Rydberg atom array offers a promising path toward fault-tolerant quantum computing.

Estimation, Prediction and Counterfactual Inference with Dependent Observations

Thu, 01 May 2025 00:00:00 GMT

Estimation, Prediction and Counterfactual Inference with Dependent Observations Kandiros, Anthimos Vardis The success of modern data science is largely driven by access to large-scale, high dimensional data. Much of classical machine learning has been developed under the assumption that this data is generated independently from some distribution. However, this assumption is often violated when data exhibit complex dependencies across a spatial or temporal domain, or due to social interactions. In this thesis, our goal is to design and analyze methods that address these dependencies for performing three fundamental estimation tasks: unsupervised learning, supervised learning and counterfactual inference. In supervised learning, we observe a sequence of unlabeled examples and our goal is to infer some structural property from the distribution they came from. The presence of dependencies could severely complicate this question. Our results in this direction encompass both fully observable as well as latent variable models. For fully observable models, we use the celebrated Ising model to describe the dependencies. Assuming we have access to a single sample from some Ising model, which captures a variety of real-world scenarios, we design and analyze polynomial time algorithms for recovering the matrix corresponding to the network structure of the model. We then leverage these techniques to obtain improved guarantees for estimating Ising models in Total Variation (TV) distance from multiple samples. For latent variable models, we focus on the case where the structure is a tree and we get samples from the leaves, which is a common scenario in phylogenetics. Assuming the model is Gaussian, we analyze the behavior of the Expectation-Maximization (EM) algorithm, a popular heuristic for latent variable models. We show that for trees with a single latent node, EM converges to the true model and for general tree topologies, the only stationary point in the interior of the domain is the true model. We then shift our focus to discrete models and study latent tree Ising models, for which we provide polynomial time algorithms for learning the distribution of leaves in TV distance. In supervised learning, we observe a sequence of feature-label pairs and our task is to learn the predictive relationship between the features and the labels. Here, this relationship could be confounded by the presence of dependencies among labels. We formulate this question as a regression problem, where the labels of the units follow the joint distribution of an Ising model with an unknown strength parameter and external fields that are determined by the regression function. We characterize the minimax optimal rate of estimation for the various parameters and provide an efficient algorithm that achieves it. Interestingly, it might not be possible to estimate all the parameters in some cases. In counterfactual inference, we focus on the design of network experiments, where the treatment of a unit could affect the outcome of a neighboring unit in an underlying graph. Our goal is to estimate a general causal effect that can be defined as the average difference in outcomes for a unit under two different interventions. For an arbitrary such effect, we propose an experimental design, called the conflict graph design. For an unbiased estimator of that effect, we prove bounds on its variance that yield the best known rates of estimation for various effects studied in the literature, such as the average direct effect and the total effect, but also provide estimation rates for effects that have received less attention from the perspective of experimental design.

Hardening Trusted Execution Environments Against Microarchitectural Side-Channel Attacks: A Constructive Approach

Thu, 01 May 2025 00:00:00 GMT

Hardening Trusted Execution Environments Against Microarchitectural Side-Channel Attacks: A Constructive Approach Dréan, Jules Guillaume Jacques Bénony D Trusted Execution Environments (TEEs) [1–5] promised to enable secure computation even in the presence of privileged adversaries by providing hardware-enforced isolation. However, the discovery of microarchitectural side-channel and transient execution attacks [6–10] has severely undermined these security guarantees. These attacks exploit shared hardware resources and speculative execution to leak sensitive information across security boundaries, effectively bypassing the architectural isolation enforced by TEEs. The widespread impact of these vulnerabilities is evidenced by more than 43 published attacks [11] targeting commercial TEE platforms including Intel SGX, AMD-SEV, and ARM TrustZone. Existing approaches to defend against these attacks face significant limitations. Hardware-based solutions [12–14] often require complex processor modifications with significant hardware overhead. Replacing trusted hardware with cryptographic approaches incurs prohibitive performance overheads [15]. Meanwhile, formal verification methods struggle to scale to realistic code base sizes and often fail to capture subtle microarchitectural behaviors [16–18]. This thesis proposes a constructive approach to TEE security and demonstrates that practical defenses against microarchitectural attacks are achievable through careful system design. Rather than relying only on models and simulations, we focus on constructing systems that are secure by design. Our work is concretely realized through the design, implementation, and evaluation of two novel platforms: First, we present Citadel, a TEE platform that enables secure shared memory while providing precise guarantees against microarchitectural side-channel attacks. Citadel introduces relaxed microarchitectural isolation (RMI), a novel security property that allows programs to share memory while restricting information leakage to that of a non-speculative execution. To achieve RMI, Citadel combines hardware-enforced microarchitectural isolation with two simple mechanisms for controlled speculation: SpecSafe, which prevents speculative shared-memory accesses entirely, and Burst mode, which enables better performance through constrained speculation on small code snippets. Through a fully functional FPGA prototype, we demonstrate that Citadel can run real-world applications including cryptographic libraries and private ML inference with less than 5% overhead while maintaining strong security guarantees. Second, we develop Argos, an “integrity-only” TEE specifically designed for verifiable fully homomorphic encryption, that enables the deployment of FHE schemes in real-world settings where malicious security is required. We show that by carefully constraining the attack surface and employing simple hardware mechanisms, we can achieve complete security against microarchitectural attacks. Argos introduces a simplified transcript-based attestation scheme that only requires one signature per FHE computation, amortizing the cost of relying on a physical TPM to microarchitecturally isolate secrets. Argos can be used to not only enforce circuit-level integrity of FHE schemes but can also be extended to support more complex FHE-based applications that take (potentially poisoned) input from the (malicious) circuit evaluator. Argos is compatible with commodity hardware and only incurs minimal performance overhead with an average of 3% overhead for FHE evaluation and 8% overhead for complex protocols. Through these systems, we show that effective defenses can be built against microarchitectural side channel and transient execution attacks. Our constructive approach yields practical systems that are secure by design while maintaining efficiency and usability. This thesis opens new possibilities for the deployment of trusted hardware by demonstrating concrete paths toward robust microarchitectural security.

Steering Robots with Inference-Time Interactions

Thu, 01 May 2025 00:00:00 GMT

Steering Robots with Inference-Time Interactions Wang, Yanwei Imitation learning has driven the development of generalist policies capable of autonomously solving multiple tasks. However, when a pretrained policy makes errors during deployment, there are limited mechanisms for users to correct its behavior. While collecting additional data for finetuning can address such issues, doing so for each downstream use case is inefficient at deployment. My research proposes an alternative: keeping pretrained policies frozen as a fixed skill repertoire while allowing user interactions to guide behavior generation toward user preferences at inference time. By making pretrained policies steerable, users can help correct policy errors when the model struggles to generalize—without needing to finetune the policy. Specifically, I propose (1) inference-time steering, which leverages user interactions to switch between discrete skills, and (2) task and motion imitation, which enables user interactions to edit continuous motions while satisfying task constraints defined by discrete symbolic plans. These frameworks correct misaligned policy predictions without requiring additional training, maximizing the utility of pretrained models while achieving inference-time user objectives.

Advancing Deep Learning Efficiency: From Specialized Co-Design to Automated Generation

Thu, 01 May 2025 00:00:00 GMT

Advancing Deep Learning Efficiency: From Specialized Co-Design to Automated Generation Lin, Yujun The explosive growth of artificial intelligence (AI) technologies, particularly large-scale deep learning models such as large language models and diffusion models, has intensified the demand for efficient full-stack inference solutions that effectively balance performance and costs. This work will present a comprehensive exploration into the algorithm-system co-optimization, hardware design specialization and automation for scalable AI deployment. First, we begin with algorithmic optimization for large-scale models, including large language models and diffusion models, developing inference libraries that leverage quantization to boost the performance of generative AIs on existing GPU platforms. Next, we design specialized hardware accelerators for domain-specific applications, specifically point cloud understanding, emphasizing efficiency improvements through the exploitation of data sparsity. Finally, we open up the hardware design space beyond template-based sizing, and progress into the automated learning-based co-design of neural network and hardware architectures, maximizing their synergy with a full-stack joint optimization. We then introduce an automated framework for spatial accelerator generation, transforming high-level mappings into custom hardware designs that support scalable deployment. Together, these contributions advance AI inference efficiency by bridging the gap between advanced computational requirements and hardware capabilities, between theoretical potential and practical solutions, and between design cost and effectiveness.

Learning Theoretic Foundations for Understanding Quantum Systems

Thu, 01 May 2025 00:00:00 GMT

Learning Theoretic Foundations for Understanding Quantum Systems Liu, Allen Understanding and harnessing the power of quantum systems has the potential to transform many domains in science and technology. However, before we can achieve these aspirations, we must first build a better understanding of how quantum systems fundamentally behave. In this thesis, we approach this question through the lens of learning theory to develop new paradigms for learning about quantum systems and understanding their structural properties. We deliver several surprising results, upending previous beliefs about even fundamental laws and giving provably efficient algorithms for learning about quantum systems in settings previously conjectured to be intractable. Typically in quantum many-body systems, the particles in the system interact locally with respect to some geometry as described by a local Hamiltonian. Two key questions are first, understanding equilibrium properties of a system with a given Hamiltonian and second, recovering the Hamiltonian from measurements of the properties of the system. For the first, we prove a universal law that there is a sudden death of entanglement, at a critical temperature depending only on the geometry but not on the system size. For the second, we give the first efficient algorithm for recovering the Hamiltonian at any temperature, breaking a conjectured barrier at low temperatures. Beyond systems with local interactions, we also consider learning and testing properties of general quantum states, focusing on the interplay between statistical complexity and near-term quantum device constraints, only allowing for entangled measurements over a limited number of copies of the state. We characterize the optimal rates for learning and testing with single-copy measurements and for multi-copy measurements in many relevant near-term regimes.

Domain Wall Based Magnonics in Iron Garnet

Thu, 01 May 2025 00:00:00 GMT

Domain Wall Based Magnonics in Iron Garnet Gross, Miela J. Magnonic devices leverage magnons, quantized spin waves, as the mechanism to process and transfer information. In materials with low Gilbert damping, these spin wave-based systems enable ultra-fast operation while eliminating thermal heating and leakage currents inherent to conventional electron-based microelectronics. To maximize energy efficiency and processing speed, materials like iron garnets, ferrimagnetic insulators with tunable magnetic properties, are essential. Key magnetic parameters, including saturation magnetization, perpendicular magnetic anisotropy, coercivity, and Gilbert damping, can be tailored through elemental substitution or strain engineering in thin films. Furthermore, relativistic domain wall velocities reported in yttrium iron garnet (YIG), bismuth substituted YIG, and thulium iron garnet lay the foundations for high-speed operation. These unique attributes position garnets as ideal materials for the development of magnonic devices that integrate efficiency, speed, and versatility. This thesis presents my research on integrating thin film garnets into a domain wall based magnonic devices. It begins by exploring the magnetic characterization of thin film iron garnets, including the growth process, temperature dependent magnetic behavior, and tunable magnetic anisotropy. Next, we report on magnonics within the garnet, focusing on the interactions between spin waves and domain walls. Finally, we demonstrate a write mechanism for a magnonic device driven by spin wave-induced domain wall motion, providing detailed characterization of the device behavior and performance. These results underscore the potential of iron garnets for magnonic-based device applications and offer insights into the efficiency of write mechanism, paving the way for energy-efficient high-speed spintronic technologies.

Mitigating Inhomogeneity in High-Field MRI Excitations: Arbitrary Waveform Optimization and Multiphoton Parallel Transmission (MP-pTx)

Thu, 01 May 2025 00:00:00 GMT

Mitigating Inhomogeneity in High-Field MRI Excitations: Arbitrary Waveform Optimization and Multiphoton Parallel Transmission (MP-pTx) Drago, John M. High-field magnetic resonance imaging (MRI) using a standard volume coil results in a spatially varying flip angle across the body, which renders images difficult to clinically interpret. This arises from the complex interactions of electromagnetic fields from current-carrying elements surrounding the imaging region. Parallel transmission (pTx) mitigates this issue by employing multiple high-power, independently controlled transmit elements for more precise excitation control. However, since the wavelength of the applied radio waves is shortened in tissue, the effect becomes highly dependent on the patient’s anatomy. As a result, optimization must be performed on a patient-by-patient basis, and methods that attempt full control of these independent waveforms are too computationally intensive to execute during the limited examination time. Additionally, the high-field excitations create complex electric field distributions that require control and careful monitoring to avoid excessive tissue power deposition (and ultimately heating), quantified as the specific absorption rate (SAR). To address these challenges, we introduce a method for optimizing patient-specific pulses using a global waveform (Ritz) approach, enabling rapid, in-scanner optimization. While pTx effectively addresses flip angle inhomogeneity, it remains costly and introduces challenges in SAR management. We address the SAR management and cost problems of pTx by introducing and characterizing the MP-pTx method, which leverages the multiphoton phenomenon to improve homogeneity using a standard volume coil supplemented with low-frequency (kilohertz) parallel channels. MP-pTx reduces costs and simplifies SAR management by shifting the parallel irradiation to low-cost, lowSAR shim array channels. These channels supplement an off-resonant excitation from a conventional birdcage coil with an oscillating, z-directed field that satisfies the resonance condition for spin state transitions.

Generative Latent Motion Planning and Reinforcement Learning for Legged Locomotion

Thu, 01 May 2025 00:00:00 GMT

Generative Latent Motion Planning and Reinforcement Learning for Legged Locomotion Miller, Adam Joseph In recent years, reinforcement learning has demonstrated its promise as a powerful tool for developing innovative and advanced control systems for legged robots. The method’s robustness, versatility, and generality have made it a prime candidate for future robotic systems deployed in the real world. Through the development of more advanced machine learning algorithms and more reliable and efficient physics simulators, reinforcement learning continues to improve and enable new, dynamic, and agile capabilities. While the results are often impressive and the tools relatively beginner-friendly, there remain impediments to scalable and reliable progress. Poor reward function scaling, challenges balancing exploration versus exploitation, and misalignment from the engineer’s intent are roadblocks to better performance. To get beyond these limitations, new tools and frameworks are necessary. In this work, I present novel methods to address these challenges and extend the capabilities of reinforcement learning on robot hardware. Through the quantification of the distributional sim-to-real gap, simulation model optimization for hardware matching, latent space motion sequence planning, and latent style training, I demonstrate never-before-seen performance on legged hardware.

Learning from Weak Supervision: Theory, Methods, and Applications

Thu, 01 May 2025 00:00:00 GMT

Learning from Weak Supervision: Theory, Methods, and Applications Lang, Hunter The growing demand for high-quality labeled data to train machine learning models has driven widespread adoption of weak supervision and synthetic data methods, which use automated models instead of humans for annotation. Large language models (LLMs) have further accelerated this trend because their zero- and few-shot classification performance enables them to serve as effective “synthetic annotators” for various tasks. In practice, the data generated by these weak annotators is imperfect, but it enables the training of strong models. However, theoretical understanding of why training one model on the outputs of another leads to strong performance remains limited, especially when the annotator model exhibits suboptimal performance on the target task. In this thesis, I develop a theoretical framework for learning from weak supervision that captures the key aspects of the problem better than existing approaches in the crowdsourcing and learning-with-noisy-label literature. This framework establishes structural conditions that explain when and why weak supervision can reliably train strong models. Building on these theoretical results, the second part of the thesis introduces methods to improve how models learn from weak supervision and applies these methods to low-labeled-data settings.

Multi-fidelity Optimal Trajectory Generation: Optimal Experiment Design for Robot Learning

Sun, 01 Sep 2024 00:00:00 GMT

Multi-fidelity Optimal Trajectory Generation: Optimal Experiment Design for Robot Learning Ryou, Gilhyun Data-driven methods have significantly advanced robot learning, yet their direct application to real-world robots remains challenging, particularly under extreme conditions. This challenge is especially pronounced for highly maneuverable vehicles like quadrotor aircraft, which often operate in scenarios requiring rapid maneuvering, such as racing, defense systems, or safety-critical obstacle avoidance. In such extreme conditions, real-world constraints like control delays, state estimation errors, and battery voltage fluctuations often compromise trajectory reliability, even when conforming to ideal dynamics. However, the typical data-driven methods are usually developed in simulated environments. Consequently, the transition to real-world dynamics requires extensive fine-tuning, which can be risky, as perfect training in simulations does not guarantee safe transitions to real-world dynamics. This thesis employs methods from optimal experiment design to address these challenges. By quantifying uncertainty and maximizing information gain, the approach aims to safely and efficiently design the real-world experiments required for accurate constraint modeling. In the first chapter, we present a multi-fidelity Bayesian optimization method that searches for time-optimal speed profiles for quadrotor aircraft, effectively balancing numerical simulations with real-world flight experiments. The second chapter extends the optimal experiment design method to a high-dimensional online planning problem through integration with reinforcement learning. The proposed algorithms, trained and validated through real-world flight experiments, significantly outperform baseline methods in trajectory time and computational efficiency. Additionally, these algorithms have been adapted to various planning problems, including fixed-wing aircraft planning, cooperative multi-drone systems, and energy-efficient trajectory generation.

Efficient Systems for Large-Scale Graph Representation Learning

Thu, 01 May 2025 00:00:00 GMT

Efficient Systems for Large-Scale Graph Representation Learning Huang, Tianhao Graph representation learning has gained significant traction in critical domains including finance, social networks, and transportation systems due to its successful application to graphstructured data. Graph neural networks (GNNs), which integrate the power of deep learning with graph structures, have emerged as the leading methods in this field, delivering superior performance across diverse graph related tasks. However, training graph neural networks on large-scale datasets encounters scalability challenges on current system architectures. First, the sparse, non-localized structures of real-world graphs lead to inefficiencies in data sampling and movement. This characteristic heavily stresses system input/output (I/O), particularly burdening the peripheral buses during the sampling phase of GNN training. Second, the suboptimal mapping of training procedure to GPU kernels leads to compute inefficiencies, including substantial kernel orchestration overhead and redundant computations. Addressing these challenges requires a comprehensive, full-stack optimization approach that fully leverages hardware capabilities. This thesis presents two complementary works to achieve the goal. The first work, Hanoi, unblocks the data loading bottleneck in out-of-core GNN training by co-designing the sampling algorithms to align with the hierarchical memory organization of commodity hardware. Hanoi drastically reduces I/O traffic to external storage, delivering up to 4.2× speedup over strong baselines with negligible impacts on the model quality. Notably, Hanoi is able to obtain competitive performance close to in-memory training with only a fraction of memory requirements. Building on this foundation, the second work, Joestar, introduces a unified framework for optimized GNN training on GPUs. Joestar adapts the multistage sampling approach from Hanoi to in-memory training which frees CPUs from heavy data loading workloads. Joestar also identifies novel kernel fusion opportunities and formulates better execution schedules by jointly considering the sampling and compute stages. Combined with compiler infrastructure in PyTorch, Joestar achieves state-of-the-art GNN training throughputs for billion-edge graph datasets on a single GPU.

Generalizable Long-Horizon Robotic Manipulation under Uncertainty and Partial Observability

Thu, 01 May 2025 00:00:00 GMT

Generalizable Long-Horizon Robotic Manipulation under Uncertainty and Partial Observability Curtis, Aidan A central goal in embodied artificial intelligence is to enable autonomous agents to accomplish complex, long-horizon tasks in novel, partially observable environments. In these scenarios, agents must effectively reason about uncertainty, generalize from limited experiences, and proactively plan actions to acquire missing information. This thesis tackles these core challenges by developing and evaluating novel methods specifically designed for partially observable contexts. The first part of this thesis introduces an enhanced heuristicguided planning technique that increases search efficiency in sparse-reward domains with significant uncertainty. Next, we investigate how symbolic reasoning can be integrated into the decision-making framework, accelerating search through the use of temporal and belief-space abstractions. Next, we propose a method for sequencing low-level reinforcement learning skills alongside information gathering actions, enabling increased task complexity and robustness in real-world tasks. Lastly, we show how large language models may be leveraged for few-shot model learning, allowing agents to rapidly adapt and generalize to new scenarios. The methods presented in this thesis advance the state-of-the-art in embodied AI by enabling robots to better handle uncertainty and incomplete information, ultimately paving the way for more capable, exploratory, and risk-aware autonomous systems.

Adaptive Abstractions for Robust Hierarchical Manipulation Planning

Thu, 01 May 2025 00:00:00 GMT

Adaptive Abstractions for Robust Hierarchical Manipulation Planning Noseworthy, Michael S. In this thesis, we address the problem of long-horizon robotic manipulation under partial observability. Tasks such as gearbox assembly or tidying a workstation involve many objects and necessitate a variety of manipulation capabilities. These long-horizon tasks are commonly addressed by hierarchical approaches, which introduce state and action abstractions to make planning tractable. However, our abstractions often rely on imperfect models of the world, which can lead to brittle execution. Furthermore, these abstractions depend on having accurate state information, which is often only noisily sensed, if sensed at all. For example, in the assembly domain, the pose of each part may only be known within a few millimeters, and a box’s mass distribution may be completely unsensed. To deploy robots outside of structured environments like the factory, they will need to be robust to model misspecification and partial observability. The central idea of this thesis is that we can develop adaptive abstractions to improve the robustness of hierarchical planning once the robot is deployed. Adaptive abstractions incorporate observations from the real world that are informative about misspecifications and partial observability, essentially allowing the planner to adapt to its deployment environment. We explore this idea by developing three types of models that enable this adaptivity at different levels of the abstraction hierarchy: plan feasibility models, adaptive samplers, and reactive control policies. In our first contribution, we consider adding adaptivity to a task and motion planning system at the task-planning level. We focus on a setup where the robot has access to a set of parameterized skills, but these skills are derived from imperfect models. To enable robust planning, we propose to autonomously learn skill feasibility models once the robot is deployed through a curious exploration phase. Critically, we propose a novel active learning framework to enable efficient learning without human intervention. We show that the resulting feasibility model leads to robust task performance on multiple downstream tasks in a stacking domain. Our second contribution looks at developing adaptive samplers that can incorporate information about object state that is typically unobserved (e.g., inertial and frictional properties). General-purpose belief representations can handle this partial observability, but online inference is computationally expensive. Instead, we propose to use an offline phase to learn an inference network that directly predicts a distribution over object properties that is consistent with the interaction history. We show that inference networks enable efficient adaptation in a grasping domain with heavy objects. Our final contribution focuses on learning adaptive controllers such that robustness is handled at the lowest level of the abstraction. We consider precise contact-rich manipulation tasks that are sensitive to pose estimation errors. To overcome noisy poses at the control level, explorative contact is necessary, but unintended forces can lead to catastrophic outcomes such as part slippage or damage. We propose to use simulation in an offline phase to train reactive force-aware policies. The policies are trained to overcome pose uncertainty while using force-sensing to adaptively limit excessive forces. The result is robust real-world performance on the multistage assembly of a planetary gearbox system, which includes insertion, gear-meshing, and nut-threading tasks. In summary, adaptive abstractions can be used to increase the robustness of hierarchical manipulation planning, an important step in deploying robots outside of the lab or factory. Throughout the thesis, we validate the proposed approaches on the real robot in stacking and assembly domains.

Methods for Generalization Under Distribution Shift

Thu, 01 May 2025 00:00:00 GMT

Methods for Generalization Under Distribution Shift Netanyahu, Aviv Machine learning systems have achieved remarkable performance in tasks where test data closely resembles the training distribution. However, real-world applications often require systems capable of handling more challenging situations -- specifically, adapting to new tasks and extrapolating to data points outside the distribution of the training set. The current paradigm for handling distribution shifts is collecting and training models on large datasets. This work offers two more principled frameworks that enable machine learning models to generalize effectively to out-of-distribution scenarios without sacrificing the power of modern overparameterized models. The first framework converts an out-of-support zero-shot generalization problem into an out-of-combination problem via a transductive reparameterization, which is possible under low-rank style conditions. We explore how this idea can be applied to domains like robotics, where the environment is changing, and materials and molecular design, where predicting properties of materials or molecules outside of known ranges is crucial to driving more efficient materials discovery. The second framework focuses on few-shot task learning, which involves agents learning new tasks from minimal data and applying them to new environments. We formulate the problem of few-shot task learning as Few-Shot Task Learning through Inverse Generative Modeling, which allows us to leverage the power of neural generative models pretrained on a set of base tasks. We adapt a method for efficient concept learning to few-shot task learning based on our formulation and rapidly learn new tasks with only a few examples, enabling task execution from autonomous driving to real-world robotic manipulation tasks in novel settings without the need for extensive retraining.

Scaling 3D Scene Perception via Probabilistic Programming

Thu, 01 May 2025 00:00:00 GMT

Scaling 3D Scene Perception via Probabilistic Programming Gothoskar, Nishad Understanding and interpreting the 3D structure of the world is a central challenge in artificial intelligence. Our physical world is 3D, yet our AI systems often “see” that world through pixels and images. In order to build truly intelligent AI systems, we must go beyond pixels and images and build 3D vision systems that can build meaningful and useful 3D representations of the world. This is the problem of 3D scene perception. How do we transform raw visual input into 3D representations of the world? 3D scene perception has numerous applications from robotics to augmented reality. Despite the advances over the last decade, 3D perception remains a major bottleneck in real-world robotics applications. The challenge stems from the immense variability in real-world conditions, e.g. lighting, color, viewpoint, camera properties, object appearance, the incompleteness of visual data due to limited resolution, noise, and occlusions, and the approximations in our models of visual data. Developing more robust and generalizable 3D perception systems would be an important step towards more general-purpose robotics. In this thesis, we explore a probabilistic architecture for 3D perception based on structured generative models and probabilistic programs. We begin with 3DP3, the first iteration of our approach, which infers 3D scene graphs from real-world depth image data. 3DP3 demonstrates that our method could work on real-world benchmarks and correct commonsense errors from deep learning systems. Building on this foundation, we develop Bayes3D, which scaled up these ideas using a GPU-accelerated image likelihood and generative model alongside a parallel coarse-to-fine inference algorithm. Next, we explore two approaches for incorporating RGB image data into generative 3D graphics programs, expanding their applicability. We then introduce DurableVS, which extends inverse-graphics techniques to model scenes involving a robot and multiple cameras, enabling precise control of a robot. Finally, we present Gen3D, which integrates all the key ideas from this thesis into a real-time 3D perception system that uses multi-resolution probabilistic models of 3D matter to enable real-time tracking that is competitive with vision transformers and 3D Gaussian splatting, state-of-the-art methods in computer vision and computer graphics.

Efficient Generative Models for Visual Synthesis

Thu, 01 May 2025 00:00:00 GMT

Efficient Generative Models for Visual Synthesis Yin, Tianwei While current visual generative models produce high-quality outputs, they suffer from significant computational costs and latency, limiting their applicability in interactive settings. In this dissertation, we introduce a suite of techniques designed to enhance the efficiency of generative models for image and video synthesis. First, we propose distribution matching distillation, a method that enables the training of one- or few-step visual generators by distilling knowledge from computationally expensive yet highly capable diffusion models. Next, we develop improved distillation techniques that enhance robustness and scalability, culminating in a production-grade few-step image generator. This system is now deployed in widely used software, generating hundreds of millions of images annually. Finally, we extend our approach to video generation by adopting an autoregressive paradigm, significantly reducing latency and enabling fast interactive video generation and world simulation.

Contactless Sleep and Physiological Monitoring Using Artificial Intelligence and Radio Waves

Thu, 01 May 2025 00:00:00 GMT

Contactless Sleep and Physiological Monitoring Using Artificial Intelligence and Radio Waves He, Hao Remote monitoring of sleep and physiological signals is critical for tracking human health, managing diseases, and enabling early intervention. However, existing monitoring solutions face two major limitations: (1) they are often unsuitable for vulnerable populations—such as infants and seniors—and (2) most of them raise concerns about measurement accuracy. We propose a novel, contactless approach that addresses both challenges by combining advances in artificial intelligence (AI) and radio-frequency (RF) sensing. Our solution makes monitoring more comfortable, accessible, and affordable, while still delivering clinically meaningful insights. This thesis makes four fundamental contributions: First, we introduce a system that can extract high-fidelity breathing signals from ambient RF reflections, even in complex scenarios where multiple individuals are present, such as couples sharing a bed. Second, we develop an AI-based sleep monitoring framework that generates sleep hypnograms and detects respiratory events entirely without the need for on-body sensors. Third we develop AI models that infer critical biomarkers—such as blood oxygen saturation (SpO₂) and inflammation (C-reactive protein levels)—in a fully passive and non-intrusive manner. Finally, inspired by the success of large language models, we show that physiological signals can be represented and interpreted analogously to language. This insight enables effective translation between modalities (e.g., from respiration to EEG) and unlocks robust representation learning for downstream clinical tasks. Together, these contributions establish a new paradigm for remote sleep and physiological monitoring—one that is contactless, continuous, and passive. We validate our system on real world datasets and demonstrate its potential to fundamentally transform clinical care and home health monitoring.

The resonant-frequency shift of a microwave cavity caused by the high-density plasma in semiconductors, as a function of magnetic field

Thu, 01 Jan 1959 00:00:00 GMT

The resonant-frequency shift of a microwave cavity caused by the high-density plasma in semiconductors, as a function of magnetic field Weber, Robert. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Physics, 1959; Includes bibliographical references (leaves 46-47).

Analysis of braced excavations.

Fri, 01 Jan 1971 00:00:00 GMT

Analysis of braced excavations. Wong, Ing Hieng. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Civil Engineering, 1971; Three leaves on transparent sheets. Vita.; Bibliography: leaves 95-99.

Modelling rail freight management.

Sun, 01 Jan 1978 00:00:00 GMT

Modelling rail freight management. Assad, A. (Arjang) Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 1978; Vita.; Bibliography: leaves 277-292.

Metabolism in vivo of 1, 3-butanediol in the rat

Fri, 01 Jan 1971 00:00:00 GMT

Metabolism in vivo of 1, 3-butanediol in the rat Nahapetian, Aratoonnaz, author. The metabolism of 1, 3-butanediol (BD) was investigated in vitamin B 12 -deficient and normal rats and in liver slice and diaphragm systems. Body weight gain and feed efficiency were determined in rats fed ad libitum for five weeks on a basal 5% BD or 5% sodium propionate diet with and without vitamin B12. The rats were train-fed for ten months on the same diets. The presence of sodium prop i onate in vitamin B12-deficient basal diets resulted in reduced food intake while BD had the opposite effect. As a result, vitamin B12-deficient rats fed a 5% sodium propionate diet grew less than those fed a 5% BD diet. The metabolism in vivo of BD labeled in carbon-1 (BD-l-cl4) and carbon-4 (BD-4-cl4) were compared to the metabolism of propionate-l-cl4 (PRP-l-cl4) in vitamin B12-deficient and normal rats. Vitamin B12 deficiency reduced the oxidation of sodium propionate but not that of BD, and had no effect on glycogen labeling from BD-l-cl4 and BD-4-cl4. For PRP-l-cl4 however, vitamin B12 deficiency resulted in not only no incorporation of label but liver glycogen levels were very small. On the other hand , when vitamin B12 was present in the diet, the labeling of glycogen from propionate was higher than that from either of the BD-labeled test compounds. Methylmalonic aciduria and urinary loss of ingested activity was higher in vitamin B12-deficient rats fed PRP-l-cl4 than in those fed l abe l ed BD. Nearly all of the urinary activity of vitamin B 1 2-deficient rats fed PRP-l-cl4 was in the form of me t hy l malonic acid (MMA), while little, if any, of the activity was found in the MMA fraction of urine of vita m in B12-deficient rats fed labeled BD. The metabolism in vivo of BD-c14 and BD-3-c14 was investigated in normal rats. About eighty percent of BD was oxidized to carbon dioxide within 32 hours. Its oxidation in the first eight hours was higher when BD was administered intraperitoneally than when it was fed by stomach tube. The loss of ingested activity in the urine expressed as a percentage of total intake and 1,3-BD was higher at the higher doses of BD. However, the activity in urinary BD could not account for all the activity in the urine. A considerable amount of ketone bodies was detected in urine of rats after feeding BD while no detectable ketone bodies were found in the urine of control rats. In addition, relative specific activities of urinary BD and S -hydroxybutyrate were 0.91 and 0.50 respectively. Polarimetry of both purified urinary BD and S -hydroxybutyrate showed that the percentages of (+)- and (-)-isomers of both compounds were 40 and 60% respectively. The metabolism in vitro of BD-3-c14 and DL-S - hydroxybutyrate-4-cl4 were investigated in systems which contained liver slices alone, diaphragm alone or both liver slices plus diaphragm. The oxidation rate of S -hydroxybut y rate was lower in liver slices than in either the diaphragm or the liver slices plus diaphragm systems. Moreover, the rate of oxidation of S -hydrox y- butyrate was highest in the system which included both liver slices and diaphragm. On the other hand, the oxidation rate of BD was lower in the system which had only diaphragm than in the other two systems. However, the rate of BD oxidation was highest in the system - wh ich included both liver slices and diaphragm. Presence of BD gave rise to increased D-(-)- S -hydroxybutyrate and acetoacetate in systems which contained liver slices or liver slices plus diaphragm. In addition, the production rate of D-(-)- S - hydroxybutyric acid was higher than that of acetoacetate in the pre sence of BD, while the opposite was true in its absence. Finally, all the radioactivity in the control incubation media was accounted for by BD-3-cl4, while about 1.5 and 98.5 percent of incubation media activity were recovered in S -hydroxybutyrate and BD peaks, respectively, in incub at ion systems containing liver. The results of this study indicate that 1,3-BD and sodium propionate do not share a common metabolic pathway in the rat. The data suggest, however, that nahapetian-4-1, 3-BD is most probably oxidized to S-hydroxybutyric acid using a "1,3-butanediol dehydrogenase" that is higher in activity in the liver than in the diaphragm. M oreover the (+)-isomer of BD is oxidized at a faster rate than the (-)-isomer, suggesting that the two isomers are oxidized by two different pathways. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Nutrition and Food Science, 1971; Thesis supervised by Sanford A. Miller Vita: page 196; Includes bibliographical references (pages 182-196)

New approaches to diagnostic imaging: Magnetic particle imaging for human functional neuroimaging and short mid-field MRI magnet design

Thu, 01 May 2025 00:00:00 GMT

New approaches to diagnostic imaging: Magnetic particle imaging for human functional neuroimaging and short mid-field MRI magnet design Barksdale, Alex Christopher Part I: Magnetic Particle Imaging for Human Functional Neuroimaging While Magnetic Resonance Imaging (MRI) has revolutionized diagnostic imaging since its clinical introduction in the 1980s — primarily focusing on hydrogen nuclei — it remains fundamentally limited by the weak nature of nuclear spin magnetism. For example, functional MRI (fMRI) provides valuable insights into brain activity through BOLD signaling, but its limited sensitivity and reliance on indirect physiological measures often necessitate large subject pools for meaningful analysis. In contrast, Magnetic Particle Imaging (MPI) utilizes the much stronger magnetism associated with superparamagnetic iron oxide nanoparticles (SPIONs), and by minimizing background signal levels which are not modulated by functional activity, it offers a promising alternative. However, there are no approved SPION tracers for human use that are well-suited to MPI, and we have little experience scaling this technology up to human-sized imagers. This thesis therefore demonstrates a human-scale MPI scanner using functional MPI (fMPI) in non-human primates and assesses its potential for future human studies. Additionally, we investigate safety aspects of MPI, specifically focusing on peripheral nerve stimulation (PNS) induced by the 25 kHz magnetic excitation fields used in MPI. Because this is a higher frequency than those used by MRI gradients, threshold data at this frequency are lacking. This thesis measures the PNS stimulation threshold in human subjects to better understand high-frequency magnetic PNS and ensure the safe implementation of human-scale MPI for future neuroimaging applications. Part II: Short Mid-Field MRI Magnet Designs Anxiety induced by the long, narrow tube of conventional 1.5T and 3T scanners is a common cause of incomplete patient examinations, leading to delays in diagnosis and reduced facility throughput. In contrast, the short aspect ratio of CT scanner bores is known to alleviate this anxiety, eliminating this problem. This thesis also addresses the need for a more patient-friendly MRI scanning option by introducing a new “hybrid” superconducting and permanent magnet concept applicable to mid-field (0.5T) superconducting solenoid magnets. While mid-field scanners offer lower sensitivity than high-field alternatives, recent advances in image reconstruction and denoising have significantly enhanced their utility, allowing them to deliver diagnostic information comparable to that of the previous generation of 1.5T scanners. Additionally, they increase the range of compatible metallic implants and offer hospitals a lower-cost, easier-to-site alternative to 1.5T and 3T scanners. They can also enhance patient comfort through shorter bore lengths and larger diameters, but their optimized winding designs still reach a limit in how short they can be made for a given homogeneity and diameter specification. This thesis introduces the use of rare-earth permanent magnets to enable further reductions in scanner length, aiming to match the aspect ratio of CT scanners.

Inference-Time Learning Algorithms of Language Models

Thu, 01 May 2025 00:00:00 GMT

Inference-Time Learning Algorithms of Language Models Akyurek, Ekin Modern language models (LMs) can perform complex tasks through in-context learning (ICL)—they can adapt to a task via examples provided in their input without any parameter updates. However, fundamental questions remain about when this adaptation works, what algorithms underlie it, and how to improve it. This thesis studies the mechanisms and limitations of ICL and develops better methods for test time adaptation of LMs on diverse benchmarks of language modeling and reasoning. I begin by evaluating the ICL capabilities of pre-trained LMs. I demonstrate that LMs can achieve strong compositional generalization when provided with few-shot examples. In a separate analysis, I show that their performance deteriorates significantly when faced with counterfactual variants of tasks they normally performed well on. Later, I develop "model problems" of ICL test the ability of LMs to learn novel mathematical structures in-context like linear functions and probabilistic formal languages. I interpret the algorithmic foundations of ICL. First, I prove that Transformer models with sufficient capacity can execute both iterative and closed-form solutions to linear regression problems, and demonstrate that these theoretical solutions manifest as interpretable intermediate variables. Then, I reveal how LMs develop specialized circuits that implement approximate n-gram learning algorithms for probabilistic languages. Building on these insights, I develop two approaches to enhance LMs. First, I demonstrate that explicitly incorporating n-gram computation into model architectures improves performance across multiple domains. Second, I introduce a test-time training that enables rapid adaptation through gradient updates on input data, achieving significant improvements over standard few-shot learning on abstract reasoning tasks. Together, these results advance our understanding of how LMs adapt to novel tasks and provide practical techniques for enhancing their test-time learning capabilities.

Machine Learning Methods for Single Cell RNA-Sequencing Data to Improve Clinical Oncology

Thu, 01 May 2025 00:00:00 GMT

Machine Learning Methods for Single Cell RNA-Sequencing Data to Improve Clinical Oncology Boiarsky, Rebecca Single-cell RNA sequencing (scRNA-seq) offers a detailed view of the cellular and phenotypic composition of healthy and diseased tissues. While machine learning (ML) methods are well-suited for the high-dimensional nature of scRNA-seq data, current computational tools face limitations, particularly when confronted with data from clinical oncology. This thesis presents the development and application of ML techniques for scRNA-seq data to address key computational challenges, with a focus on challenges in clinical oncology. It covers four key areas: identifying gene signatures and biomarkers in multiple myeloma, developing methods to account for somatic copy number variations in tumor samples, benchmarking large, pre-trained scRNA-seq foundation models, and creating a framework for predicting clinical outcomes using patient-level representations of single-cell data. Together, these studies aim to develop and evaluate novel ML algorithms for scRNA-seq data which can unlock actionable insights for personalized medicine.

Engineering TEV Protease Specificity: An Exploration of Machine Learning and High-Throughput Experimentation for Protein Design

Thu, 01 May 2025 00:00:00 GMT

Engineering TEV Protease Specificity: An Exploration of Machine Learning and High-Throughput Experimentation for Protein Design Sundar, Vikram Engineering sequence-specific proteases would enable a wide variety of therapeutic applications in diseases ranging from cancer to Parkinson’s disease. However, many previous experimental and physics-based attempts at protease engineering have failed to engineer specificity in cleaving alternative substrates, rendering them useless. In this thesis, we aim to engineer TEV (tobacco etch virus) protease, a highly sequence-specific protease, to cleave alternative substrates. We incorporate novel high-throughput assays and powerful machine learning (ML) methods for highly effective protein engineering. The first portion of this thesis focuses on generating fitness landscapes from high-throughput experiments. Most machine learning models do not account for experimental noise, harming model performance and changing model rankings in benchmarking studies. Here we develop FLIGHTED, a Bayesian method of accounting for uncertainty by generating probabilistic fitness landscapes from noisy high-throughput experiments. We demonstrate how FLIGHTED can improve model performance on two categories of experiments: single-step selection assays, such as phage display, and a novel high-throughput assay called DHARMA that ties activity to base editing. FLIGHTED can be used to generate robust, well-calibrated fitness landscapes, and when combined with DHARMA, our methods enable us to generate fitness landscapes of millions of variants. We then evaluate how to model protein fitness given a fitness dataset of millions of variants. Accounting for noise via FLIGHTED significantly improves model performance, especially of high-performing models. Data size, not model scale, is the most important factor in improving model performance. Furthermore, the choice of top model architecture matters more than the protein language model embedding. The best way to generate sufficient data scale is via error-prone PCR libraries; models trained on these landscapes achieve high accuracy. Using these methods, we successfully engineer both activity on an alternative substrate and specificity when compared to the wild-type. The ML-designed variants outperform anything found in the training set, demonstrating the value of machine learning even with experimental libraries of millions of variants. However, our results are limited to relatively close substrates. How best to improve model performance on distant substrates remains an open question.

Learning-Guided Optimization for Intelligent Mobility Systems

Thu, 01 May 2025 00:00:00 GMT

Learning-Guided Optimization for Intelligent Mobility Systems Li, Sirui Efficient and reliable mobility systems are essential to modern-day society, with broad impacts ranging from day-to-day commuting, public transportation, emergency response to last-mile package delivery and freight logistics. Autonomous vehicles have the potential to improve mobility efficiency and convenience but also raise questions about reliability and feasibility of deployment. The first contribution of this thesis is a set of novel, principled control-theoretical analyses that provide strong stability and reliability guarantees for autonomous vehicles and human-compatible driving, and they further covers emergent traffic behaviors in mixed-autonomy systems. While these theoretical guarantees offer valuable insights, mobility systems are inherently complex, and their overall performance often relies on solving difficult optimization problems, many of which are combinatorial, thus presenting significant scalability challenges. Overcoming these challenges requires innovative approaches that extend beyond traditional control techniques. This thesis further contributes a set of machine learning-guided optimization algorithms that significantly enhance the efficiency and scalability of solving combinatorial optimization problems. These algorithms have proven effective across a wide range of mobility-related applications. Compared to state-of-the-art solvers, they achieve 10× to 100× speed-up in large-scale vehicle routing problems, 35% to 70% solve-time improvement in various mixed-integer linear programming problems, and up to 54% acceleration in long-horizon scheduling problems. These advancements open new possibilities for efficient decision-making in large-scale transportation systems, enabling smarter, faster, and more adaptive mobility solutions. Combining learning, optimization, and control, this thesis demonstrates the potential of learning-guided optimization and principled control-theoretical analysis to address the increasing complexity of modern mobility systems.

Top-down and bottom-up interactions for cortical bursting

Thu, 01 May 2025 00:00:00 GMT

Top-down and bottom-up interactions for cortical bursting Tang, Vincent D. High-frequency burst firing occurs throughout the mammalian cortex in vivo, yet both the underlying mechanisms and functional roles of bursts are unclear. Burst firing in brain slices is strongly modulated by the activity of apical dendrites, which branch extensively in layer 1 (L1) and receive long-range inputs from higher-order cortical and thalamic areas. These properties suggest a powerful subcellular substrate by which single pyramidal neurons could multiplex bottom-up and top-down information via L1-independent tonic spikes and L1-dependent bursts, respectively, and have provided a basis for emerging theoretical models of cortical computation and learning. However, our understanding of burst firing and subcellular processing remains critically limited by a lack of evidence in awake animals. It is unclear whether burst firing a) is preferentially recruited by bottom-up versus top-down inputs, and b) requires apical dendritic engagement. To answer these questions, we performed high-density extracellular recordings in primary visual cortex of awake mice while presenting a battery of Gabor (bottom-up) and inverse (top-down) visual stimuli. We report widespread high-frequency bursts in L2/3 and L5 pyramidal neurons. Contrary to expectation, bursts exhibited extremely short response latencies, and were most strongly recruited by Gabor stimuli. We further tested the causal contribution(s) of apical dendrites to burst firing and top-down visual tuning via two optogenetic manipulations: direct L5 apical tuft inhibition and NDNF interneuron activation. Strikingly, L1 inhibition only modestly reduced the burst fraction, and did not differentially affect Gabor vs inverse responses. Taken together, these results challenge prevailing theories of apical dendritic involvement in burst spike generation and feedback visual tuning, and provide new biological constraints for future theoretical and experimental work.

Creating space for HVAC systems: A new, intuition-building approach to HVAC system integration in architectural education and practice

Thu, 01 May 2025 00:00:00 GMT

Creating space for HVAC systems: A new, intuition-building approach to HVAC system integration in architectural education and practice Irani, Ali Heating, Ventilation, and Air Conditioning (HVAC) systems are vital to ensuring a healthy indoor environment in buildings. They are essential to the global shift toward a decarbonized, all-electric future. While integrated design practice has promised cost, energy, and space savings due to earlier and more frequent collaboration between design disciplines, remaining missed opportunities in the HVAC system design and coordination process often lead to spatial conflicts, performance tradeoffs, and uncomfortable spaces. This dissertation aims to understand current coordination practices to identify the root causes of existing problems, timeline issues, and knowledge gaps. Then, it proposes a series of enhancements to address these shortcomings, focusing on National Architectural Accrediting Board (NAAB) accredited architectural education programs that train the next generation of practicing architects. The proposed research hypotheses are validated in a three-part research approach: (1) releasing architecture industry surveys and conducting interviews, (2) designing and testing an early-stage design tool, and (3) developing, implementing, and evaluating a comprehensive HVAC curriculum for architecture students. The dissertation demonstrates that with the right tools and educational resources, architecture students can make informed, intuition-based HVAC system selections and integrate them into their building design, with students who studied the comprehensive curriculum demonstrating a 13% improvement in understanding and application of HVAC concepts compared to a control group of students. This work helps bridge the knowledge gap regarding HVAC systems, empowering designers to coordinate more effectively and prioritizing the role of HVAC systems in building performance simulation education.

Mechanisms of Multi-Object Working Memory and Motion Prediction in the Primate Brain

Thu, 01 May 2025 00:00:00 GMT

Mechanisms of Multi-Object Working Memory and Motion Prediction in the Primate Brain Watters, Nick Sample-efficient learning and flexible generalization are hallmarks of intelligent behavior. Both sample-efficient learning and flexible generalization rely on re-using a mental model of the world in new contexts. For many decades, researchers in cognitive science, neuroscience, and machine learning have studied competing theories about the structure of our mental model of the world. One set of theories concerns the structure of multi-object representations in the brain. Some studies claim the brain represents multiple objects by allocating them to disjoint “slots” in working memory, others claim that the brain flexibly distributes a common pool of resources across objects, and yet others claim the brain represents multiple objects by rapidly switching between them through time. Another set of theories concerns the nature of predicting object motion. Some claim that the mind has an internal model of physics in the world that it uses to simulate the motion of objects through time, whereas others claim the mind relies on priors and heuristics to predict object motion without explicit simulation. Both of these sets of competing theories are long-standing and unresolved. In this work, we tackle these two open questions using primate neurophysiology and computational modeling. We trained monkeys to perform multi-object memory and motion prediction tasks, recorded large-scale single-unit activity from frontal cortex brain areas, and rigorously compared different hypotheses for the neural mechanisms of multi-object working memory and motion prediction. In the case of multi-object working memory, we found that the neural activity we recorded is more consistent with a model that flexibly distributes attentional resources across objects than with models that use object slots or temporal switching representations. In the case of motion prediction, we found that the neural activity is not consistent with the monkeys simulating an occluded moving object in real-time. Instead, the monkeys’ neural activity is driven largely by an anticipation of the position of the object at a future point in time. Both of these findings call into question long-standing cognitive theories and imply that the brain’s model of the world incorporates attentional mechanisms, priors, and heuristics. Lastly, we introduce a neural data preprocessing method for stabilizing electrophysiology recordings. This method improves spike-sorting results, helped us recover more neurons from our data, and we hope may help others make the most of their electrophysiology data as well.

Application of Revenue Management to Satellite Communications

Sun, 01 Sep 2024 00:00:00 GMT

Application of Revenue Management to Satellite Communications Eiskowitz, Skylar As the demand for satellite Internet continues to grow, satellite communication (SatCom) operators are faced with the challenge of effectively managing their capacity sales. While Revenue Management (RM) techniques have been widely used in other industries such as airline, hotel, and car rental services, the application of these methods in the context of SatCom is still scarce. This Thesis aims to bridge this gap by developing RM concepts, techniques, and optimization algorithms specifically tailored to the unique operational characteristics of SatCom capacity management and sales. The proposed SatCom RM method guides operators with quantitative recommendations of the amount of capacity to sell to different products in time and in different regions to maximize revenues. Though SatCom has characteristics that favor the use of RM concepts (perishable inventory, fixed capacity with a low variable cost, the possibility to segment demand), there are unique structural characteristics that complicate the development of SatCom RM models. The primary challenge is that different products consume varying amounts of capacity, with larger terminal size products utilizing less power on a satellite than smaller terminal size products. Moreover, the selling practices in SatCom are complex because products may be sold in one period and consumed across multiple periods in which additional sales are made. This requires rolling both the selling and consumption periods. Lastly, the SatCom RM problem poses a multidimensional network problem, as products can consume bundles of resources in both space and time. We extend two commonly used airline RM algorithms, Expected Marginal Seat Revenue (EMSRb) heuristic and Displacement Adjusted Virtual Nesting (DAVN) to the SatCom problem to create booking limits. The booking limits recommend a threshold amount of capacity an operator should sell of each product. The contribution of this Thesis is the modification of established airline RM algorithms to handle products with variable capacity uptakes. Further, these algorithms typically account for displacement costs of products, but only in one dimension of space or time (e.g., selling an airline flight that uses multiple spatial legs may displace capacity away from flights that only use one leg). Our modifications allow for the consideration of displacement costs in both dimensions of space and time. In order to evaluate the effectiveness of our inventory control approach, we conduct simulations of various demand scenarios and compare the revenue gains to a baseline scenario with no controls, as well as a simpler method that does not consider product duration. In a large-scale simulation spanning three years and encompassing thousands of product requests, we observe revenue gains ranging from 15%-30% depending on the demand scenario. Then, we extend the model to multiple zones and achieve 2%-10% revenue improvement using our Multi-Zone DAVN method compared to the DAVN method applied to each zone separately.

Non-invasive tuning of experience-dependent plasticity in the primary visual cortex

Thu, 01 May 2025 00:00:00 GMT

Non-invasive tuning of experience-dependent plasticity in the primary visual cortex Reilly-Andújar, Francis The cerebral cortex exhibits a remarkable capacity for experience-dependent plasticity, a feature that is predominantly confined to critical periods (CPs) during early postnatal development. In the mouse primary visual cortex (V1), ocular dominance plasticity (ODP) has served as a premier model for investigating the cellular and molecular mechanisms that underlie the formation and stabilization of cortical circuits. During the CP, short-term monocular deprivation (MD) induces both functional and anatomical changes in binocular V1, characterized by a weakening of deprived-eye responsiveness via mechanisms of synaptic long-term depression. As the critical period closes, increased inhibitory drive and the emergence of perineuronal nets (PNNs) stabilize neural circuits and restrict further experience-dependent plasticity. In Chapter 1, I review the key literature on ODP and provide a survey of interventions that have been shown to enhance ODP in adulthood. In Chapter 2, I present our findings that repeated anesthetic ketamine treatment can reinstate ‘juvenile-like’ plasticity in the adult mouse V1. Importantly, I demonstrate that this effect relies on the microglia-mediated depletion of PNNs, and that interfering with microglial purinergic P2Y12 receptor activation blocks the ketamine-induced enhancement of ODP. Building on these insights, Chapter 3 investigates the use of non-invasive light-flicker stimulation at different temporal frequencies as a means to unlock different forms of ODP in the adult mouse V1. Our results reveal that 60 Hz light-flicker stimulation reduces PNN levels and promotes a depression of deprived-eye responses following short-term MD, whereas 40 Hz stimulation – without altering PNN levels – enhances an adult form of ODP characterized by the strengthening of non-deprived eye responses following short-term MD. Furthermore, we show that in mice subjected to long-term MD initiated early in life, 40 Hz light-flicker treatment promotes recovery of visual function, as evidenced through physiological and behavioral assays. Finally, Chapter 4, outlines a series of future experiments designed to further elucidate the mechanisms by which light-flicker stimulation promotes enhanced ODP in adult V1. Together, the findings presented in this thesis introduce novel, minimally invasive (ketamine) and non-invasive (light-flicker) interventions that show promise as therapeutic strategies for ameliorating deficits arising from early life sensory deprivation.

The role of texture in auditory scene analysis

Thu, 01 May 2025 00:00:00 GMT

The role of texture in auditory scene analysis Hicks, Jarrod M. Everyday auditory scenes contain sounds from many sources. For example, when crossing the street, you might hear sounds produced from the rumble of passing cars, the chatter of pedestrians, and the rapid tick of crosswalk signals. To make sense of this complex mixture of sounds, the auditory system must separate the mixture into coherent perceptual representations that are likely to correspond to the underlying sources in the world. This process is known as auditory scene analysis. Although a rich body of work has probed auditory scene analysis with simple synthetic stimuli and revealed principles of perceptual organization, the extent to which these principles apply to real-world scenes with natural sounds remains unclear. This thesis empirically examines auditory scene analysis with realistic sounds. In particular, we study the perception of scenes containing a common class of environmental sounds known as “textures”, investigating how the auditory system makes use of statistical structure to separate textures from other sources and how the underlying statistical representation both constrains and enables scene analysis. We first investigated the mechanisms of hearing in noise using real-world background “noise” textures. The results show that the auditory system estimates the properties of “noise” textures and stores them over time, using the resulting internal model to estimate other concurrent sounds. We then considered how concurrent sound texture sources are separated from each other. We found that auditory scene analysis with textures involves some principles identified in classical scene analysis work with simple sounds, but that these principles apply to the higher-order statistical representations that define natural textures. Together, the results reveal new aspects of auditory scene analysis with real-world sounds and clarify the role texture plays in everyday hearing. Our findings provide a bridge between the simple, synthetic stimuli studied historically and the rich complexity of real-world sounds.

Driving Temporally Precise Learning in Individual Premotor Neurons using Closed-Loop Neurofeedback

Thu, 01 May 2025 00:00:00 GMT

Driving Temporally Precise Learning in Individual Premotor Neurons using Closed-Loop Neurofeedback Scherrer, Josefa R. Much of human existence is based on our ability to learn complex sequences of motor movements. Speech, writing, and tool use all require activating a series of different muscles in a precisely timed pattern, and these patterns are learned through a long process of trial and error. How does the neural circuitry in our motor system learn to generate the activity patterns that drive these sequences? This question can be explored by studying a similarly precise learned motor pattern in a different organism, the learned song of the songbird zebra finch. Zebra finches learn to sing a stereotyped song through a process of vocal experimentation and comparison to an internal template. Every time a bird sings, it varies the acoustic parameters of its song and determines whether each variation brings the song closer to its internal template. Variations that result in a better match are then repeated in subsequent renditions of the song, in a trial and error process suggestive of reinforcement learning. The learning process requires a basal ganglia-thalamocortical loop called the anterior forebrain pathway (AFP) that is similar to basal ganglia-thalamocortical circuitry in mammals. Existing evidence suggests that the AFP learns a time-dependent bias signal that steers the motor pathway to avoid vocal errors. This bias signal is known to be dependent on the cortical output of the AFP known as LMAN (lateral magnocellular nucleus of the anterior nidopallium). However, little is known about the neural code in LMAN that underlies this bias signal, or how this neural code is learned and generated. We address these questions by building a neural feedback system that allows us to impose correlations between the activity of individual LMAN neurons and a dopaminergic reward signal. We designed a low-latency feedback system that records neural activity from a chronic Neuropixels 2.0 implant, extracts the activity of specific neurons, and plays noise bursts to the bird contingent on the activity of those neurons. We used this system to perform feedback based on the activity of an arbitrarily chosen neuron in LMAN within a given 10 ms window in songs. All birds responded to the feedback by learning to bias the activity of the chosen LMAN neuron up or down within the chosen time window, transiently driving firing rates up by as much as 200 Hz. We observed a remarkable degree of timing precision in the learned bias, with birds able to control the activity of the chosen neuron at single millisecond levels of rise time and jitter. This high degree of precision informs models of the basal ganglia circuit architecture thought to drive learning. We also found the learned bias to be specific to the LMAN neurons correlated with reward, with neighboring uncorrelated neurons exhibiting no change in firing rate during learning. This single-neuron specificity strongly constrains the spatial precision of axonal targeting from thalamic regions that are thought to propagate the learned bias signal from the basal ganglia to LMAN. Finally, we demonstrated that fluctuations in neural activity of a given LMAN neuron drive transient and predictable changes in vocal output approximately 25 milliseconds later, consistent with what is known about signal propagation speeds in the song system. This fact, together with the results of our feedback experiments, combine to confirm our central hypothesis that LMAN drives song learning by independently activating LMAN neurons at precise points in time in order to bias vocal output and avoid vocal errors.

The Spiritual Curation of American Modernism

Thu, 01 May 2025 00:00:00 GMT

The Spiritual Curation of American Modernism Saha, Indrani Where do the spiritual go? In this study of late-nineteenth- and early-twentieth-century seekers, they join seances in Vermont farmhouses, attend Theosophical lectures on Karma, get lost in copies of Jnana-Y oga, journey to Buddhist temples in China, and consume spiritual manuals on Mentalphysics. But where do they go after those encounters? And, more importantly, what do they do? In this dissertation, they build modern art institutions. A cadre of artist-writers, museum curators, and public intellectuals found their power in early 20th century America by building institutions to introduce a new, spiritually grounded modern art to a mercantile nation. In the US, beyond European sources for "the spiritual" were flirtations with vaguely "Eastern" ones by way of Theosophy. Those who sought to institutionally manifest Wassily Kandinsky's "spiritual" in art believed themselves to provide the assistance necessary to cultivate and preserve these spiritual impulses in modern art. Alfred Stieglitz's Intimate Gallery (1925-1929), Katherine Sophie Dreier's Societe Anonyme (1920-1950), and Hilla Rebay's Museum of Non-Objective Painting (1939-1952)-all in New York City-served as intermediaries in translating predominantly Eastern spiritual ideas into productive ways of being. It would be needed, each curator believed, to cultivate these spiritual protocols just to survive in a material world they held to be detrimentally bankrupt of spirit. In other words, the American institutionalization of modernism built its canon around spiritual systems of national aesthetic welfare. Crucial to these spiritual curators' respective operations would be the promotion of not just any abstraction but a radically non-objective art thought to use the inner expressions of the artist to elevate the spectator. This dissertation takes the turn-of-the-century claims of spirituality by the founders of key art institutions seriously. In doing so, I argue that esoteric forms of Eastern spirituality infused formerly Protestant centers of culture to propel a twentieth-century embrace of radically abstract modern art.

Programmable Mud: 3D Printing earth to achieve low-carbon, low-cost construction automation

Thu, 01 May 2025 00:00:00 GMT

Programmable Mud: 3D Printing earth to achieve low-carbon, low-cost construction automation Curth, Alexander (Sandy) McCormick Large-scale additive manufacturing (LSAM) with locally sourced materials, such as earth, presents a promising approach to addressing the urgent challenges of rapid urbanization and construction-related carbon emissions. This dissertation establishes a comprehensive framework for integrating low-carbon materials, particularly minimally processed earth, with computational design methodologies and robotic fabrication processes for architectural-scale applications. Through systematic material characterization, novel testing protocols, and case studies across multiple building systems, the research demonstrates that minimally processed earthen materials can be transformed into high-performance building elements uniquely suited to local environmental conditions and design considerations. The developed computational framework employs multi-objective optimization and material-aware toolpath generation to balance structural performance, thermal comfort, embodied carbon, and construction time. Four case studies validate this approach: (1) toolpath optimization for shell structures, (2) a hybrid floor system combining shape-optimized concrete beams with 3D-printed ceramic blocks, (3) zero-waste earthen formwork for reinforced concrete, and (4) thermally optimized wall systems for passive climate control. Life cycle assessment reveals that 3D-printed earth structures have approximately one-fifth the embodied carbon of conventional concrete and one-fiftieth that of industry-standard 3D-printed mortar. This research bridges the gap between additive computational design and material circularity, offering scalable approaches to sustainable construction that can be implemented across diverse environmental and economic contexts.

The Limits of Longevity

Thu, 01 May 2025 00:00:00 GMT

The Limits of Longevity Rodriguez, Christopher W. Do all animals age? Although aging seems to be a widespread phenomenon, some demographic studies have failed to find evidence of aging in certain species, including some highly regenerative species of planarians and Hydra that reproduce through asexual fission. However, all demographic studies have limits on observation times and sample sizes, so it is unknown if these failures were because of an actual absence of aging or these inherent study limitations. Some argue that these species must be ageless. Because of pressures that result from the lack of a clean division between the germ line and the soma in fissiparous organisms, agelessness becomes necessary as a prerequisite of this kind of reproductive strategy. Others argue that fundamental theories of the evolutionary biology of aging absolutely preclude agelessness. Even putting evolutionary arguments aside, some mathematical models of cellular competition and senescence argue that agelessness is impossible mechanistically in multicellular organisms. In this work, I address evolutionary and mechanistic arguments for and against agelessness. I develop mathematical models of the Disposable Soma Theory that incorporate facets of the arguments for agelessness in asexual fissioning organisms. I construct models of mutation accumulation and drift within an individual and explore how this genetic decay could manifest in the mortality rates. I use these models to understand if aging is inevitable generally and apply them to planarians and Hydra to seek to estimate the likelihood of aging more narrowly in those specific cases. Contrary to other work, I find that agelessness (defined as non-increasing mortality rates in a population) is indeed possible as the optimal evolutionary strategy for multicellular organisms. However, the evolution and mechanistic realization of agelessness requires conditions that are unlikely to be met in any existing species. In the case of planarians and Hydra, they likely do not face the right kind of evolutionary pressure to completely avoid aging. Even if they do face necessary evolutionary pressure, intraindividual genetic decay will almost certainly induce increasing mortality on the population with little recourse. Therefore, these species likely do age, although they could have median lifespans on the order of hundreds or perhaps even thousands of years, which would make detecting aging in any given population study quite difficult indeed.

Fundamental representations of regions and interactions in spatial transcriptomics

Thu, 01 May 2025 00:00:00 GMT

Fundamental representations of regions and interactions in spatial transcriptomics Maher, Kamal M. While cells are often considered the fundamental unit of biology, it is their spatial coordination that gives rise to the tissue architectures underlying both health and disease. Spatial transcriptomics technologies offer a unique window into this coordination by simultaneously capturing the spatial and molecular identities of individual cells, providing unprecedented insight into tissue organization. However, the computational landscape for analyzing tissue structure remains fragmented, with a wide array of disparate methods. In this work, we aim to distill these approaches into a unified quantitative framework for analyzing tissue architecture. Tissue structure can be represented in terms of anatomical regions as well as the cellcell interactions that occur within them. For regional tissue organization, many existing methods—including those based on probabilistic models and graph neural networks—ultimately perform a form of smoothing, or local averaging of gene expression across neighboring cells. This process emphasizes large-scale spatial variation and enables standard single-cell analysis workflows, such as clustering and trajectory inference, to be applied in spatial contexts. However, we find that naive smoothing introduces artifacts that obscure meaningful spatial features. To address this, we introduce a minimal but powerful modification: subsampling within each neighborhood prior to averaging. This approach enhances spatial feature resolution and generalizes conventional analyses to spatial features: clustering identifies multicellular regions; data integration aligns spatial regions across samples and technologies; and trajectory inference captures spatial gradients. We also show that this subsampling strategy improves the performance of more complex downstream methods. To further generalize our framework, we formalize the joint analysis of tissue regions and multiscale cell-cell interactions using signal processing over graphs: low-frequency components represent regional gene expression patterns across a tissue mesh; high-frequency components capture fine-scale, cell-cell interactions; and mid-frequency signals correspond to boundaries between regions and diffusive signaling. By interpreting spatial gene expression in this spectral framework, we provide a principled way to bridge conceptual and computational perspectives on tissue structure. Ultimately, this work serves as both a theoretical foundation to understand existing methods and a roadmap for developing future approaches to quantitatively describe molecular tissue architecture.

Deciphering Features of Protective or Maladaptive Cellular Immunity in the Airways Following Primary and Repeated Pathogen Exposure

Thu, 01 May 2025 00:00:00 GMT

Deciphering Features of Protective or Maladaptive Cellular Immunity in the Airways Following Primary and Repeated Pathogen Exposure Bromley, Joshua David The human respiratory tract is constantly subject to environmental stressors and perturbations that cause deviations from homeostatic conditions. The airway’s cellular constituents – epithelial, stromal, and immune cells – maintain local and global homeostasis by facilitating gas exchange and providing a barrier against noxious environmental agents (e.g., xenobiotics, allergens, toxins, and microbes). Infection with viral, microbial, and eukaryotic pathogens can disrupt airway homeostasis, leading to local and systemic inflammation, which can either contribute to the clearance or persistence of the pathogen. Prior antigenic exposure - prophylactically or from a previous infection - can promote transient and long-lived changes in cellular epigenetics, gene expression networks, and cell type composition that may contribute to protective (or maladaptive) immunity; however, we lack a complete understanding of the pathogen and cellular determinants that modulate immunity upon reinfection. In this thesis, we employed single-cell RNA-seq (scRNA-seq), computational methods, and microbial assays to discover the host and pathogen determinants governing airway homeostasis during primary infection and reinfection at barrier sites where the infection begins and may persist: the nasopharynx, airways, and lung parenchyma. First, we leveraged scRNA-seq to identify the cellular and molecular features of mild, moderate, and severe COVID-19, revealing that persons with severe COVID-19 have blunted anti-viral immunity in the nasopharynx. We further extended these findings by profiling nasopharyngeal swabs from vaccinated and unvaccinated individuals across three waves of SARS-CoV-2 variants, revealing shifts in viral tropism and that intramuscular COVID-19 vaccines promote the recruitment of putative antigen presenting macrophages to the nasal mucosa. Next, we used rhesus macaques to interrogate temporal host-pathogen interactions during SARS-CoV-2 infection and reinfection in the lower respiratory tract. This work identified innate training-like gene programs among myeloid populations that provided enhanced protection against SARS-CoV-2 reinfection. Finally, we used cynomolgus macaques as a model to study Mtb infection and reinfection, demonstrating that CD4+ T cells are required to restrict bacterial growth and induce protective immunomodulatory gene programming and cell-cell interaction networks in pulmonary granulomas formed following Mtb reinfection. These findings extend beyond long-held paradigms of protective TB immunity, revealing that CD4+ T cells regulate pro- and anti-inflammatory granuloma equilibria. Collectively, the work presented in this thesis highlights the utility of single-cell genomics for studying respiratory infection- and immuno-biology and provides a framework for contextualizing pathogen-induced deviations from biological homeostasis in the airways, which has implications for the development of prophylactics and therapeutics.

Expansion Microscopy of Extracellular Space for Light Microscopy-Based Connectomic Analysis

Thu, 01 May 2025 00:00:00 GMT

Expansion Microscopy of Extracellular Space for Light Microscopy-Based Connectomic Analysis Emenari, Amauche In this dissertation, we present an exploratory methodology, termed expansion microscopy of extracellular space (ExECS), designed to enhance the visualization of the extracellular space (ECS) within aldehyde-fixed tissue. This technique leverages the principles of expansion microscopy (ExM), a method that facilitates nanoscale imaging on conventional microscopes through physical magnification of specimens, thereby supporting improved visualization of various cellular and tissue components including proteins, nucleic acids, and lipids 1. The ECS forms a continuous environment between cells2. Its presence throughout neural tissue makes it an attractive target for contrast-based techniques such as shadow imaging, where the ECS is selectively labeled to produce negative contrast, revealing cell shapes and boundaries as unlabeled silhouettes within a labeled background. Although ECS delineation in fixed tissue is limited by the fidelity of fixation and may not fully reflect its live-state structure, the resulting contrast with the intracellular environment may offer useful contrast for investigating neural morphology and connectivity, offering a useful approximation of network organization. A key component of the ExECS methodology is the introduction of a customengineered ECS Filler solution. This formulation, detailed later, includes a macromolecular probe intended to serve as a proxy for the ECS. When applied to aldehyde-fixed tissue, the filler is designed to diffuse throughout the sample, preferentially occupying extracellular compartments while remaining largely excluded from intracellular regions. This selective distribution is expected to persist even in areas where aldehyde fixation may have increased membrane permeability. This diffusion behavior is presumed to result from a combination of size-based exclusion and intermolecular interactions between the hyaluronan polymers, which form the main component of the filler solution, and the plasma membrane. The constituent hyaluronan is functionalized with amine groups to enable covalent crosslinking and with azide groups to allow fluorescent tagging via click chemistry. These modifications are intended to enable the ECS filler to act as a contrast agent by labeling the extracellular space, providing a foundation for a shadow-based imaging strategy to delineate morphology of cellular structures. In parallel, we introduce a lipid-targeted form of ExM, termed membrane expansion microscopy (mExM). This approach employs a custom chemical tag that enables nanoscale optical imaging of lipid membranes using a lipid-optimized expansion protocol. mExM, via a novel post-expansion antibody labeling protocol, enables protein-lipid relationships to be imaged in intracellular organelles. This technique may offer new opportunities to examine aspects of neural circuitry by linking cellular morphology with molecular identity. Together, ExECS and mExM offer a potential basis for a light microscopy-based framework for connectomic reconstructions. Unlike traditional electron microscopy approaches, which are labor-intensive and low-throughput3, this strategy aims to improve throughput in mapping of neuronal morphology with enhanced resolution that surpasses diffraction limitations. With the aim of bridging the gap between tissue ultrastructure and optical accessibility, this work may contribute to efforts toward scalable, high-resolution analysis of neural tissue organization.

Decoding Disease Drivers Through Single-Cell Omics and Scalable Phenotypic Screens

Thu, 01 May 2025 00:00:00 GMT

Decoding Disease Drivers Through Single-Cell Omics and Scalable Phenotypic Screens Liu, Nuo At the heart of any human disease is an imbalance between normal and aberrant physiological processes— a disproportion between hypo-immunity and hyperimmunity—a lack of homeostasis. In many cases, a more comprehensive understanding of the molecular basis underlying disease progression and therapeutic failure is still required to devise new strategies for improving patient outcomes. Technological advancements in biomedical research, especially in single-cell omics (e.g. single-cell RNA sequencing, single-cell spatial profiling) have given us unprecedented power to decipher the intricate cellular and molecular features that maintain—or disrupt—this balance. However, validating the causality of these features remains a huge challenge, as the wealth of data often results in a considerable number of hypotheses to test. In this thesis, I explore applications of single-cell genomics tools to understand cellular features associated with disease, with a particular focus on tuberculosis (TB). I then present a potential solution for performing phenotypic screens at scale. In the first part, I applied single-cell RNA sequencing and analysis to human lung samples from a TB-endemic region in South Africa. Using contrastive analysis, I identify key cell populations that are differentially abundant between TB-diseased and TB-negative lung including several neutrophils, macrophages, and fibroblasts subsets. I discovered a de novo gene program highly enriched in the MMP1+CXCL5+ Fibroblast that correlates with TB burden in a non-human primates (NHP) granuloma dataset, supporting the importance of this subset in TB. In a collaborative effort, we validate that this MMP1+CXCL5+ Fibroblast localizes to TB granuloma on independent TB-diseased lung tissues using immunohistochemistry assays and recapitulate the induction of this population from lung-derived fibroblast through in vitro stimulation experiment with M.tb. I further report a SPP1+ macrophage population that is enhanced in TB diseased lungs through single-cell analysis. Moreover, I identified a prominent cross talk between SPP1+ macrophages and fibroblasts in TB diseased lung that mimics similar observations in cancer and fibrosis, supporting an important role for this axis in TB. These distinctive cell populations could serve as potential targets for novel host-directed therapies in tuberculosis. In the second part, I developed a method to compress small molecule phenotypic screens by designing randomized drug pools with replicates of distinct candidates across different drug pools. Our team demonstrated that linear regression models can be applied to computationally deconvolute the individual hits, enabling the identification of top effectors for downstream validation. We benchmarked and demonstrated the efficacy of this approach in a cost-effective imaging platform and then moved into applications on pancreatic ductal adenocarcinoma (PDAC), where we discovered a new perturbation response signature to IL-4/IL-13 with prognostic value for patient survival. We also showcased the utility of this tool on understanding immunomodulation effects in heterogenous mixtures of primary blood cells. Together, this thesis describes novel cellular features important to TB in human lungs, offering new insights that complement existing knowledge from animal models. It also presents a bold, yet effective strategy to scale up phenotypic screen across different biological systems, providing a much-needed solution that bridges the translational gap between human disease and experimental model.

Data-Driven and Dynamically Feasible Trajectory Generation for Real-Time Powered Descent Guidance and Robotic Exploration

Sun, 01 Sep 2024 00:00:00 GMT

Data-Driven and Dynamically Feasible Trajectory Generation for Real-Time Powered Descent Guidance and Robotic Exploration Briden, Julia Increasingly complex and high-mass planetary missions require autonomous long-horizon trajectory generation to achieve dynamically feasible powered descent guidance. While analytical and indirect methods are computationally efficient, significant simplifications of the dynamics and constraints are required for both problem formulations. Numerical optimization algorithms enable minimum-energy trajectory generation subject to system dynamics and safety constraints but currently remain computationally infeasible on flight-grade processors, taking seconds to minutes to compute a single trajectory. The objective of this dissertation is to develop new algorithms to advance the state of the art in trajectory optimization and planning for autonomous systems. Due to the limited computational abilities of radiation-hardened processors and an increased need for spacecraft and robotic autonomy, specialized algorithms capable of running in realtime constitute enabling technologies for space exploration. Three major contributions are developed in this dissertation. First, a transformer neural network-based algorithm is created to predict the tight constraints that recover the solution and parameter sets for constrained optimization problems. By training on prior runs of the numerical optimization solver, the learned mapping can construct a reduced problem formulation that recovers the optimal solution while reducing runtime by up to an order of magnitude. Second, a method to embed problem-specific information into the neural network training process was developed. By embedding the Lagrangian and Lagrangian gradient merit functions into the training process, neural network-generated control policies are biased toward constraint satisfaction. Third, an autonomous hybrid targeting and guidance algorithm was designed to utilize probabilistic risk maps and numerical optimization to select and navigate to minimum-risk landing sites. Applications in planetary powered descent and landing, as well as rover path planning, are used to benchmark algorithm performance.

Quantum theory of mode locking.

Fri, 01 Jan 1971 00:00:00 GMT

Quantum theory of mode locking. Lang, W. R. (W. Roy) Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1971; Vita.; Bibliography: leaves 88-90.

Extrageniculate and extrastriate affiliates of the geniculocortical pathway in the cat

Tue, 01 Jan 1980 00:00:00 GMT

Extrageniculate and extrastriate affiliates of the geniculocortical pathway in the cat Berson, David Matthew. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Psychology, 1980; Vita.; Bibliography: leaves 114-126.

New theoretical methods for the study of the electronic structure of solids.

Sun, 01 Jan 1978 00:00:00 GMT

New theoretical methods for the study of the electronic structure of solids. Mele, Eugene John. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1978; Includes bibliographical references.

Fracture Mechanics of Networks

Thu, 01 May 2025 00:00:00 GMT

Fracture Mechanics of Networks Hartquist, Chase M. Networks of interconnected materials permeate throughout nature, biology, and technology due to exceptional mechanical performance. Despite the importance of failure resistance in network design and utility, no existing physical model effectively reconciles strand mechanics and connectivity to predict fracture in diverse networks that constitute polymeric, architected, and biological materials. While traditional models predict the intrinsic fracture energy – the minimum energy to propagate a crack per unit area – of a polymer network is the energy to rupture a layer of chains, they can underestimate experiments by up to two orders of magnitude. In Part I, we show that the intrinsic fracture energy of polymer-like networks stems from nonlocal energy dissipation. We then reveal a general scaling law that captures nonlocal energetic contributions and connects strand mechanics with topological connectivity to universally predict the intrinsic fracture energy of stretchable networks. We measure intrinsic fracture energy using experiments and simulations of 2D and 3D networks with various strand constitutive behaviors, defect densities, strand length distributions, lattice topologies, and length scales. Results show that local strand rupture and nonlocal energy release contribute synergistically to the measured intrinsic fracture energy in networks. These effects align such that the intrinsic fracture energy scales independent of the energy to rupture a strand; it instead depends on the strand rupture force, breaking length, and connectivity. In Part II, we present a model for real polymer fracture and design elastomers with highly regular connectivity. End-linking then deswelling star polymers produces a class of elastomers with low defects and no trapped entanglements, enabling ultrahigh straininduced crystallinity of up to 50% and stretchability that scales beyond the saturated limit. These features promote a pronounced elastocaloric cooling effect and enable reversible two-way tuning of thermal conductivity by strain or temperature modulation. The mechanical and thermal properties of these polymer networks offer promise in addressing challenges in clean energy, thermal management, and biomedicine. Our findings establish a physical basis for understanding network fracture and design principles for fabricating tough polymeric, biological, and architected materials across multiple length scales for advanced applications.

Modeling the Sit-to-Stand Transition using Koopman Lifting Linearization and Human State Estimation

Thu, 01 May 2025 00:00:00 GMT

Modeling the Sit-to-Stand Transition using Koopman Lifting Linearization and Human State Estimation Bell IV, John H. The Sit-to-Stand (STS) transition is one of the most dangerous daily activities for the elderly population, as it is one of the situations in which falls occur most often. Despite its risks, STS dynamics remain poorly understood, and current STS assistance devices fail to utilize knowledge of STS dynamics to effect their support. This thesis presents contributions to the dynamic modeling of STS and to human-robot collaboration for improving robotic assistance of STS. To coherently capture the multi-phase nature of STS, lifting linearization, a dynamic modeling methodology inspired by Koopman operator theory, to subsume segmented local dynamics in a globally linear dynamic model. A novel class of lifting linearization basis functions, termed “State-Membership Product (SMP)” observables, enables both the seamless blending of local dynamics into a global model, and the direct extraction of phase-specific behaviors from the global model. It is shown that an SMP-Koopman linear model tuned to published data of STS experiments is capable of reproducing the multi-phase STS dynamics with a single linear model. Building on this framework, STS is additionally modeled as a lifted linear feedback control system, composed of an SMP-Koopman-based open-loop biomechanical model of the human body and a linear quadratic regulator (LQR) which guides the body to stand up. The LQR controller, tuned to replicate STS motion, guides the human body model through the phases of STS without explicit phase-switches, improving system robustness. To enhance human-robot collaboration in STS assistance, a framework for estimating patient cooperativeness is also introduced, leveraging a simplified dynamic model and an Extended Kalman Filter. By analyzing a human’s initial response to applied physical and verbal cues, the estimation framework assesses willingness to engage in assisted STS. Together, these contributions advance both the modeling and estimation of STS, offering insights crucial for the development of safe, effective robotic assistance.

Quantifying Human Balance Performance and Control to Inform Therapy

Thu, 01 May 2025 00:00:00 GMT

Quantifying Human Balance Performance and Control to Inform Therapy Shiozawa, Kaymie S. Maintaining balance is essential for daily activities and overall health. However, balance capability often declines with age or due to health conditions such as stroke, increasing fall risk. Falls among older adults are a major public health concern, affecting 14 million older adults annually in the US and directly causing over 40,000 deaths. Timely and accurate assessment of balance impairment is crucial to prevent falls and promote independence. Current assessments rely heavily on subjective therapist evaluations, underscoring the need for objective, quantitative methods. With the growing strain on healthcare systems due to an aging population, continuous at-home balance monitoring is also increasingly important. Additionally, a comprehensive understanding of the motor control mechanisms that deteriorate with aging or disease is crucial for informing therapy methods and technologies. The goal of this thesis was to develop and validate methods that quantify quiet balance ability and control in unimpaired and impaired human participants. The first part focuses on assessing balance ability, the capacity to maintain upright posture during quiet stance that is currently often quantified by measures of body sway. A review of the strengths and limitations of current clinical and instrumented balance assessments highlighted a critical need for continuous assessment methods that enable objective monitoring of balance function outside of clinical settings. Addressing this need, a novel algorithm that quantifies balance ability using only force and motion sensors embedded in an instrumented cane was developed. Well-established balance measures were successfully estimated in both younger and older adults, demonstrating the proposed method's potential to facilitate continuous balance monitoring in real-world environments. The next part focuses on identifying balance control strategies. The novel intersection-point analysis, based on foot-force direction and point of application, was used in conjunction with a simple biomechanical model and an optimal controller to quantify balance control. The first study demonstrated that unimpaired quiet balance in a challenging environment was best described by a controller that maintained minimal effort by adjusting relative ankle and hip joint torques. Applying this method to aging populations in a subsequent study revealed that older adults rely more on neural feedback, possibly to compensate for muscle strength deficiency. This study also quantified individual balance controllers, highlighting the method's potential as a diagnostic tool for aging populations. Finally, the model was extended to describe balance control after stroke. The results suggest that the non-paretic limb compensated for the paretic limb's abnormal coordination pattern by strongly favoring neural feedback. As one of the first studies to model quiet balance after stroke, this work lays the foundation for future efforts on studying balance impairments. The contributions of this thesis are instrumental to enhancing at-home monitoring, advancing clinical practices, and reducing fall-related injuries, ultimately improving quality of life for aging and neurologically impaired populations.

Deformable Object Manipulation with a Tactile Reactive Gripper

Thu, 01 May 2025 00:00:00 GMT

Deformable Object Manipulation with a Tactile Reactive Gripper Sunil, Neha Manipulating deformable objects remains a fundamental challenge in robotics, as techniques developed for rigid objects often fail to generalize. Deformable objects exhibit infinite-dimensional configuration spaces, frequent self-occlusion, and high model uncertainty, making global state estimation and predictive modeling unreliable. To address these challenges, we propose a perception-driven framework that combines global visual understanding with local tactile feedback. Rather than modeling the full configuration of the object, we leverage local constraints, grounded in modular visual and tactile representations, to enable robust, reactive, and generalizable manipulation. The primary contributions of this work include: • Chapter 2: Cable Following. A tactile control strategy for in-hand cable manipulation that decouples contact regulation from object pose control, enabling fast, reactive sliding and closed-loop plug insertion using only local tactile feedback. • Chapter 3: Towel Edge Tracing. An extension of contact-based control to fabric edge following and the learned tactile perception networks to support this capability. • Chapter 4: Visuotactile Grasp Affordance. A grasp affordance model trained in simulation and refined with tactile self-supervision, enabling high-confidence edge grasping on towels. • Chapter 5: Dense Object Correspondence. A confidence-aware dense descriptor representation. Supports correspondence across crumpled and symmetric garments in air and on a table. • Chapter 6: Behavior Architecture and Planning Interfaces. Integration of perception modules into a reactive, confidence-based folding system and an exploration of how dense descriptors can interface with demonstrations, language, and task and motion planning. Collectively, these contributions show that global state estimation and dynamics prediction are not required for reliable deformable manipulation. Instead, semantically meaningful local interactions, guided by modular visual and tactile representations, can drive scalable, long-horizon behaviors across varied objects, configurations, and tasks.

A Model-Based Planning and Control Framework for Parkour-Style Legged Locomotion

Thu, 01 May 2025 00:00:00 GMT

A Model-Based Planning and Control Framework for Parkour-Style Legged Locomotion Chignoli, Matthew T. Legged robots have long been envisioned as a means of expanding robotic capabilities beyond structured environments, yet achieving high-agility locomotion remains a fundamental challenge. This thesis presents a model-based framework for parkour-style locomotion, enabling robots to execute highly dynamic maneuvers such as jumps, rolls, and flips with precision and robustness. A key challenge in planning these motions is selecting an appropriate dynamic model that balances computational efficiency with physical accuracy. To address this, a model assessment strategy is introduced to determine the simplest model capable of capturing task-relevant dynamics. Even with well-chosen models, solving long-horizon trajectory optimization problems for dynamic motions is computationally demanding. This thesis introduces graduated optimization techniques, which improve solver efficiency and reliability by generating high-quality initial guesses through progressively refined problem formulations. Additionally, a novel formulation of rigid-body dynamics algorithms for systems with kinematic loops accelerates trajectory optimization and simulation. Finally, two control strategies are proposed to execute planned motions on hardware: a model-based tracking controller for real-time adjustments and an imitation learning policy trained on optimal trajectories to enhance robustness. Extensive experiments on hardware validate the framework, demonstrating the successful execution of complex, high-impact locomotion behaviors. By integrating advanced planning, optimization, and control techniques, this work establishes a foundation for high-agility legged locomotion, pushing beyond conventional automation toward real-world, dynamic robotic movement.

Fractured Practices: How Schooling Norms Limit Modeling Practices in Traditional Technical Thermal-Fluids Engineering Courses -- And the Possibilities Emerging through the Cracks

Thu, 01 May 2025 00:00:00 GMT

Fractured Practices: How Schooling Norms Limit Modeling Practices in Traditional Technical Thermal-Fluids Engineering Courses -- And the Possibilities Emerging through the Cracks Huffman, Sandra In professional science and engineering contexts, modeling practices are frequent and diverse. To understand, analyze, and communicate, scientists and engineers simplify and distort the complex systems with which they work. This practice is known as modeling. Typically, scientists create models to predict and explain phenomena while engineers develop them to analyze and test systems, make design decisions, and predict the performance of built systems. Models can include verbal (ex. analogy, story), visual (ex. diagrams, graphs, images), and symbolic (ex. equations) representations. When scientists and engineers model, they do so expansively: pulling from different resources, combining modeling strategies, engaging in critique and iteration, and contextualizing their claims in the work of their field. This is not the case for students in technical engineering classes who are attempting to learn these skills. Traditional, lecture-based courses are the norm for introducing technical material to undergraduate engineering students. These courses typically consist of lectures, recitations, problem sets, and exams. In this type of class, students report homework and test problems as having an outsized influence on their learning approach. These problems tend to be narrow and prescribed. Colloquially known as ‘Textbook-Style’ problems, well-defined, single-solution problems are not sucient to prepare students to successfully tackle the ill-defined, multifaceted engineering problems they will face in their careers. These problems do not elicit student engagement in scientific or engineering modeling practices. Instead, they lead to inauthentic, bounded learning where students develop strategies adequate for groups of similar problems, but too narrow for use outside of the classroom. There has been significant research on innovative educational interventions and alternative problem types shown to improve classroom learning. However, educators work within established structures that resist change, leading to the perpetuation of insucient practices. The gap between textbook-style problems and the problems engineers face, therefore, exists not just in the problem type, but in the context surrounding the task. In this work, I describe and characterize the norms and practices of the classroom environment through three qualitative studies, each centered on traditional technical thermal-fluids courses. Specifically, I investigate the ways in which the development of student modeling practices are supported or undermined. I do this, in part, by adapting the theoretical framework of Figured Worlds. Originally developed by Dorothy Holland and later used in Engineering Education research, figured worlds is a situative framework that allows researchers to look at distinct, sometimes contradictory cultural worlds within the same group and activity. In the first study, I look at individual student approaches to classroom tasks in a think-aloud study, comparing their problem solving approaches and analyzing prompt-student interactions. In the second study, I analyze small groups’ modeling practices and how they are limited by the cultural practices of schooling. In the third study, through semi-structured interviews, I document instructor perceptions of their research and teaching, and discuss the misalignments within and between these contexts. Together, these works outline the mechanisms by which school practices can inhibit the development of student modeling capabilities and the role of students and instructors in perpetuating these practices. In describing student and instructor behavior and contextualizing practices that may otherwise be ascribed to misconceptions, carelessness, or ignorance, I hope to build a foundation for future research into pragmatic educational interventions for enhanced learning outcomes.

Design for Longevity: Service and System Innovation

Thu, 01 May 2025 00:00:00 GMT

Design for Longevity: Service and System Innovation Lee, Sheng-Hung The global demographic shift toward an aging population presents complex social, economic, and systemic challenges, necessitating innovative approaches to service design, systems thinking, and financial planning. This dissertation, Design for Longevity: Service and System Innovation, examines these transformations and proposes strategies to foster a “longevity society”, a new era in society necessitating a fundamental rethinking of age and ageing to effectively harness the opportunities afforded by increased life expectancy (Scott, 2021). This research is built upon five relevant paradigm shifts: 1. from age-based to stage-based mindsets, 2. from product-driven to service-driven solutions, 3. from human-centered to humanity-centered design, 4. from circular to longevity economics, and 5. from an aging society to a longevity society. These shifts redefine the role of designers and researchers in creating adaptive, inclusive, and sustainable systems for the future. This dissertation explores how tangible artifacts, Longevity Planning Blocks (LPBs), can be employed to create effective service encounters. The research questions explore 1. how to use boundary objects (BOs) to uncover and define latent user needs, 2. how to use a mixed-method approach to analyze experiment data, 3. data-driven persona creation, and 4. the design of longevity planning services across financial planning, service innovation, and system thinking. Central to the research is a study of LPBs, BOs designed to facilitate collaborative engagement between a facilitator and 69 Boston-based participants, stratified by age, gender, pre-tax annual income, and assets. LPBs, employed in experiments, help investigate participants’ needs and concerns across various life transitions and stages. These tangible BOs facilitated informal yet insightful discussions, uncovering how individuals navigate ambiguity, make complex decisions, manage their evolving physical, mental, and social health, and perceptions about living solo. Data from in-person longevity planning experiments provided nuanced insights into the interplay of individual, societal, and systemic factors shaping longevity planning services. A mixed-methods approach integrates qualitative and quantitative techniques, including expert and user interviews, co-creation workshops, pre- and post-experiment surveys, hierarchical cluster analysis, K-means clustering for persona development, and causal loop diagrams for longevity planning service system modeling. Constructivist grounded theory and exploratory factor analysis uncover emerging themes and systemic interconnections, emphasizing the importance of adaptive services that align with changing needs and broader social infrastructures. The study introduces the notion of Design for Longevity (D4L), expanding on longevity economics and circular economy principles to address the complexities of extended lifespans. D4L highlights how evolving resources, transformative needs, and systems integrate life stages into the design of products, services, and experiences. This dissertation contributes to service innovation, financial planning, and system design by proposing actionable insights for longevity planning services. It emphasizes multi-stage life planning, intergenerational collaboration, and systemic thinking as foundational to a longevity society. This dissertation contributes a mixed-method approach, offering design practitioners a replicable, data-driven framework for persona creation applicable beyond longevity planning. Concluding with reflections on social infrastructure, community, and culture, the study calls for cross-disciplinary collaboration to address longevity planning challenges. By advancing the understanding of longevity planning and its systemic implications, this work lays a foundation for designing a future where extended lifespans are inclusive and socially engaged.

Integrated Prosthetic Leg Design Frameworks for People with an Above-Knee Amputation

Thu, 01 May 2025 00:00:00 GMT

Integrated Prosthetic Leg Design Frameworks for People with an Above-Knee Amputation Petelina, Nina T. A well-fitting, high-performance prosthesis for people with a lower limb amputation can greatly improve users’ mobility and quality of life. Still, many amputees lack access to high-performance prosthetic components due to the cost and availability of continuous care. This thesis aims to design low-cost, high biomechanical performance above-knee prosthetic leg components (prosthetic foot and knee) that will result in a walking motion likely to be perceived as able-bodied after minimal acclimation time. Above-knee amputees have two common gait deviations from able-bodied and below-knee amputee gait: lack of early stance knee flexion (ESF) and delayed initiation of knee flexion (IOF) during late stance phase. These deviations are likely a result of prioritization of stability at the expense of other functions such as shock absorption and progression through stance. A preliminary perception study was conducted to investigate the acceptable bounds of gait deviation that can be incorporated into a prosthetic leg design without compromising the perception of "typical" walking. Using these results, I created the Hip Trajectory Error (HTE) framework for designing prosthetic feet specifically for people with an above-knee amputation. The HTE framework takes into account the lack of ESF by incorporating the shock absorption function of ESF within the prosthetic foot design. This is achieved by targeting able-bodied hip center motion, which is correlated with sufficient shock absorption during the stance phase. This thesis presents an optimization and performance evaluation process that resulted in a prosthetic foot structure that not only closely replicates able-bodied hip center motion but also could be manufactured for a low cost. An experimental study successfully demonstrated that the Hip Trajectory Error (HTE) framework can be used to predictively design prosthetic feet for aboveknee amputees. HTE-designed prosthetic feet enable comparable biomechanical performance to daily-use tuned and prescribed prosthetic feet within 10-15 minutes of acclimation time and without iterative multi-day fittings. Next, I proposed a method to recommend a damping coefficient for the prosthetic knee to achieve able-bodied peak knee flexion during swing phase. A range of recommended damping coefficients to achieve target peak knee flexion angle in transfemoral amputees was determined using a simple three-step framework. This framework incorporates effects from common transfemoral prosthetic gait deviations, such as slower self-selected walking speeds and delay in initiation of knee flexion during late stance. The calculated range of recommended damping coefficients was experimentally investigated and found to enable a peak knee flexion angle within two standard deviations of able-bodied peak knee flexion angle. Lastly, I created the Full Leg Optimization (FLO) framework to design the prosthetic foot and knee concurrently based on minimal inputs from the user and the prosthetist. The framework anticipates the lack of ESF and delay initiation of late stance knee flexion and uses the HTE framework to predict the orientation and location of the knee mechanism. Using this prediction, the rotational axes of the prosthetic knee can be positioned to start knee flexion at a point in late stance chosen by the prosthetist to provide sufficient stability to the user. A proof-of-concept study demonstrated the accuracy of the prediction for one user after minimal acclimation time, confirming the ability to predictively design prosthetic leg components in tandem. The FLO framework can therefore be used to predictively design a passive prosthetic leg for above-knee amputees while considering common gait deviations due to stability needs. This doctoral work demonstrates that the presented frameworks can be used to quantitatively design prosthetic feet and knees based on the needs of above-knee amputees, which could save fitting time, manufacturing cost, and improve mobility.

Development of a Hierarchical Reflexive Control Framework for Autonomous Robotic Manipulation

Thu, 01 May 2025 00:00:00 GMT

Development of a Hierarchical Reflexive Control Framework for Autonomous Robotic Manipulation SaLoutos, Andrew Within the field of robotic manipulation, much research focus has been placed on improving perception and planning algorithms, assuming that the actions output by these high-level planners will be easily achieved by the robot systems. However, to surpass human manipulation performance, fast and robust execution of manipulation plans is just as critical as improved perception and planning methods. In this thesis, we introduce the last centimeter problem, which states that the most difficult part of grasp execution is when less than a centimeter remains between fingertips and an object, and contact is imminent. To solve this problem, we propose a reflexive control framework, which is a manipulation control architecture that decouples low-level, high-bandwidth behaviors, which we call reflexes, from broad high-level plans. The reflexes are fast, autonomous reactions to local sensing information that are designed to add robustness to high-level manipulation plans while also reducing the necessary complexity of manipulation planning problems. To deploy our reflexes, we design hardware platforms that incorporate high-bandwidth actuation and low-latency tactile sensing, allowing us to maximize the reactive capabilities of the overall manipulation system. We validate our approach through studies on teleoperated grasping and autonomous planar grasping, which show that our reflexive controllers increase manipulation speed and robustness. Then, we perform extensive simulation studies for autonomous grasping in SE(3), conducting experiments with single objects as well as cluttered scenes, using a variety of state-of-the-art grasp planners. Our results show greatly improved grasp robustness with our reflexive controllers, across all object types and grasp planners. Further experiments show that the benefits of our reflexes persist across sets of objects that are larger, heavier, and more slippery, and with increasing magnitudes of errors in the executed grasp poses. While this thesis demonstrates that the reflexive control framework is effective at increasing grasp robustness during picking, our framework is constructed in a way that is amenable to extension to other tasks, like in-hand manipulation or constrained object placement, as well as application to more complex grippers, such as those with three or more dexterous fingers and more diverse sensing.

On the Certification of Deep Learning-based Dynamical System Identification

Thu, 01 May 2025 00:00:00 GMT

On the Certification of Deep Learning-based Dynamical System Identification Zhang, Wang Dynamical system identification, the reconstruction of the system governing equations from observations, has been studied for decades. With the recent emergence of deep learning techniques, neural network-based parameterization enriches this classical field by offering new capabilities in modeling complex systems. While promising advances have been made, these black box models face significant challenges due to their limited interpretability and lack of physical guarantees, raising concerns about their applicability in scenarios where trustworthiness is critical. In this thesis, we developed a comprehensive framework to analyze, understand and learn dynamical systems. We start with a contrastive learning method to capture system invariants (i.e., conserved quantities) from trajectory observation of dynamical systems. Building on these learned invariants or known priors, we introduce a projection layer for neural networks that guarantees the preservation of physics constraints in the learned dynamics models. This two-step approach significantly improves the trustworthiness and interpretability of the traditional black-box models. On top of this, we extend this methodology to learn physically meaningful embeddings corresponding to inter-system characteristics, enabling zero-shot meta-learning capabilities for dynamical system models. Finally, we reduce the bias gap in the classical neural network-based aleatoric uncertainty estimators. We identify overestimation issues in existing variance attenuation methods and propose a novel denoising-based approach that provides more accurate estimates of data uncertainty. This method not only applies to regression tasks but also extends to dynamical system observations.

Design Theories for Compact, Low-energy, Clog-resistant Drip Irrigation Emitters

Thu, 01 May 2025 00:00:00 GMT

Design Theories for Compact, Low-energy, Clog-resistant Drip Irrigation Emitters Ghodgaonkar, Aditya This thesis presents the derivation, experimental validation, and demonstration of new design theories for compact, low-pressure, clog-resistant drip emitters that can make drip irrigation affordable, reliable, and easier for farmers to adopt. Broad adoption of water-efficient irrigation methods such as drip irrigation is imperative to sustainably meet projected global food demand against the backdrop of diminishing freshwater resources, constrained arable land, and climate change. In drip irrigation systems, emitters are passive flow-regulating devices that are inserted into the drip tube to align with every plant. They are designed to provide a constant flow rate once they are pressurized to at least their activation pressure, thus ensuring uniform, localized irrigation of plants. However, conventional emitters directly contribute to three barriers that have limited drip irrigation adoption – high raw material-driven equipment costs, high pumping power costs associated with pressurizing all emitters in the field to their activation pressure, and gradual loss of reliability due to clogging. Compact, low-pressure, clog-resistant emitters can address these challenges, but to design them, we must model and tune their operating physics, which is centered around two complex features – a millimeter-scale tortuous passage called the labyrinth, and fluid-structure interaction (FSI) involving a flexible silicone rubber diaphragm and a micro-duct. This makes conventional design approaches relying on high-fidelity simulation software or empirical trial-and-error too expensive and time-consuming to use for the development of compact, low-pressure, clog-resistant emitters on competitive industrial timelines. This thesis addresses these challenges through three contributions. The first contribution presents an empirically derived hydraulic model of emitter labyrinths, which are typically the most volume-intensive feature of emitters. The model relates labyrinth flow rate to select material volume agnostic parameters, allowing designers to create compact labyrinths with desired hydraulic performance. The compact labyrinths can enable up to 10% reduction in the raw material-driven cost of drip equipment. The second contribution presents a 1-dimensional model of the FSI in emitters that can predict their flow rate-pressure performance in 2-3 minutes and within 8-14% error, cutting down on design cycle times by orders of magnitude. This facilitated the rapid synthesis of low-pressure emitter designs having 50-60% less activation pressure than conventional emitters, cutting pumping power costs by an estimated 18-23%. Together, the first two contributions can enable an estimated 18% reduction in the lifetime costs of drip irrigation, but long-term adoption requires that the emitters be clog-resistant and compatible with the current maintenance practices of farmers. To that end, the third contribution presents an experimental investigation of clogging in low-pressure emitters. The results of the investigation directly correlated the geometry of emitter hydraulic features to the critical particle size that would clog them. As a result, compact, low-pressure emitters could be designed to be compatible with the same filters and maintenance practices as current state-of-the-art products that have higher activation pressures. This was confirmed by field testing the compact, low-pressure, clog-resistant (MIT) emitters alongside commercial reference designs with their prescribed filters for nearly 1200 hours. At the end of the field test, the MIT emitters still held 90-94% of their initial flow rate, putting them on par with or better than the reference products in terms of irrigation reliability. The collective contributions of this thesis present the knowledge needed to design emitters that can make drip irrigation more affordable to adopt by farmers and demonstrate that substantial capital and operating cost reductions can be realized without sacrificing product reliability or requiring expensive changes to current farmer maintenance practices.

Tailoring Complexity of Model-Based Controllers for Legged Robots

Thu, 01 May 2025 00:00:00 GMT

Tailoring Complexity of Model-Based Controllers for Legged Robots Khazoom, Charles Humanoid robots promise human-like mobility, but must manage complex and often conflicting control objectives. While model-based controllers can address these challenges using online optimization, they have high computational demands. Model predictive control (MPC) provides closed-loop stability with online trajectory optimization, but achieving real-time rates is difficult for high-dimensional systems. To mitigate this limitation, most MPC implementations rely on reduced-order models (ROMs) that simplify planning but fail to capture whole-body constraints like joint limits and self-collisions. Reactive whole-body controllers (WBCs) partially address this limitation by projecting ROM trajectories onto some wholebody constraints, but these are restricted to acceleration-level constraints like friction cones and torque limits. This thesis advances humanoid planning and control through a renewed focus on model fidelity, solution accuracy ans solve times with three key contributions. First, we propose the CBF-WBC, which augments reactive WBCs with position constraints using control barrier functions (CBFs), enabling the MIT Humanoid to avoid selfcollisions with minimal computational overhead. As a result, the robot can reactively deviate from infeasible trajectories from a reduced-order MPC. Despite fast solve times below 100 microseconds, conflicts can arise between the reduced-order MPC and the CBF-WBC. To address this, we enable real-time whole-body MPC using the alternating direction method of multipliers (ADMM) to provide low-accuracy solutions at high feedback rates. The controller is reliably deployed on hardware and enables the MIT Humanoid to walk robustly on rough terrains and plan complex crossed-leg and arm motions that enhance stability when recovering from significant disturbances. While low-accuracy solutions often suffice for real-time control, we found that higher accuracy could still improve closed-loop performance if computational speed allows. Building on this insight, we propose a framework to simultaneously optimize solution accuracy and model complexity to maximize closed-loop performance. Instead of planning with a single model that is too complex or too simple, solve times can be reduced by planning over a sequence of models of reducing complexity. We extract ROMs from whole-body dynamics equations and optimize their horizons, discretization timesteps and solution accuracy using blackbox optimization. The optimizer can sacrifice model complexity for additional ADMM iterations, reducing falls by nine-fold and enabling a 2 m/s walking speed on hardware.

Optimized Sustainable Hydrogen Generation from Liquid Metal Activated Aluminum-Water Reactions

Thu, 01 May 2025 00:00:00 GMT

Optimized Sustainable Hydrogen Generation from Liquid Metal Activated Aluminum-Water Reactions Kombargi, Aly This study presents a sustainable and cost-effective method for hydrogen generation using aluminum waste, addressing both energy and environmental challenges. Activated aluminum reacts with water to produce hydrogen, heat, and aluminum oxyhydroxide (boehmite), a commercially valuable byproduct. As a safe, efficient, and cost-effective energy carrier with an energy density exceeding 20 kWh/L (8 kWh/kg), aluminum enables on-demand hydrogen production for diverse applications, including maritime transport and off-grid power systems. This research optimizes reaction kinetics to enhance hydrogen yield and rate while minimizing costs and carbon emissions. Activation involves coating aluminum with a gallium-indium eutectic (eGaIn) liquid metal, which disrupts the oxide layer and enables spontaneous reaction in aqueous environments. The study investigates seawater as an ionic medium for eGaIn eutectic agglomeration and reuse. However, chlorine binding slows the reaction, which was countered using high-temperature operation and catalytic enhancement. Adding 0.02 M imidazole accelerated the reaction 60-fold, enabled 92% eutectic recovery, and achieved 99% of the theoretical hydrogen yield. Environmental conditions significantly influence reaction efficiency. Increasing seawater temperature from 20°C to 90°C enhanced reaction rates 44-fold, aligning with Arrhenius Law. Isochoric reactions at high pressure were tested to simulate deep-sea vehicle environments using onboard hydrogen reactors fueled by aluminum and surrounding seawater. Results showed a 33% yield increase at 6 MPa (586 m depth) compared to atmospheric pressure, primarily due to surface tension effects that reduce hydrogen bubble size, improving aluminum-water contact at higher pressures. A life cycle and cost analysis identified an optimized production scenario with a carbon footprint of 1.45 kgCO2eq/kg H2, meeting green hydrogen standards. Major contributors include recycled aluminum use and processing, and the eGaIn alloy; but eutectic recovery and thermal energy reuse further reduce emissions. Using scrap aluminum and recovering byproducts, hydrogen production costs are estimated at $9.2/kg. Additionally, reselling boehmite (market price $2.5/kg) could generate revenue 5.6 times greater than input costs, significantly improving economic viability. To demonstrate scalability, a modular hydrogen reactor was developed and directly integrated with a commercial generator, reliably producing 400W of power from on-demand, 99% purity lab-tested hydrogen. The envisioned application is a fully integrated aluminum recycling system that utilizes aluminum waste and seawater to generate hydrogen, thermal energy, and boehmite. This approach advances clean energy technology by providing a scalable and economically viable hydrogen production pathway. Beyond its direct application in underwater technologies, this optimized reaction can support energy-intensive operations such as heating, desalination, transportation, industrial hydrogen production for refining and fertilizer synthesis, stationary energy systems for off-grid power, and renewable energy storage. Its versatility strengthens energy security and decarbonization efforts while offering a cost-competitive alternative to conventional fuels, positioning it as a key enabler of a sustainable energy future.

Essays in Venture Capital and Corporate Finance

Thu, 01 May 2025 00:00:00 GMT

Essays in Venture Capital and Corporate Finance Paine, Fiona This thesis is three chapters. In the first chapter, I study the impact of restricting foreign venture capital investments for national security reasons. Countries have increasingly been using economic policies to further geopolitical and national security goals. Thus far, economists have focused on studying tariffs and subsidies despite a broader range of economic tools actually being implemented. How costly are these other policies and what are their effects on capital markets, investment, and the economy more broadly? In this paper, I examine a 2018 U.S. law (FIRRMA), which expanded the government’s ability to review and block transactions on national security grounds to include venture capital (VC) investments by foreign investors. I use the passing of FIRRMA, its differential impact on specific VC industries, and the role of Chinese investors in U.S. venture capital to study whether foreign investment screening impacts capital supply. I find that FIRRMA had a negative effect on capital supply in impacted industries due to two factors: 1) the specialization of VC investing (such that the substitution of outside capital into impacted industries is low) and 2) networks in VC investing (there are spillovers to domestic syndication partners in impacted industries). I further find that the change in capital supply is costly, leading to lower innovation by startups. I introduce a novel way of measuring innovation early in the life of a startup using text from startup websites. I use this measure to show there is a selection effect where VCs give first round funding to less innovative startups after FIRRMA. Finally, in a case study of the biotechnology industry, I show that impacted startups suspend drug projects at higher rates, and in particular their risky projects. In the second chapter, joint with Johnathan Jensen, we study municipal cyber risk. Cyber attacks are estimated to cost billions of dollars per year. However, cyber risk is hard to study since companies rarely disclose hacks and don’t share information on cyber security investment. This paper takes a novel approach by looking at municipal hacking. We use a dataset of municipal ransomware attacks merged with hand collected IT investment data and municipal bond data. We find that lower IT investment predicts hacking. Furthermore, following a ransomware attack, municipal bond yields fall by 13 basis points and IT investment as a share of total town expenditure increases by 23 basis points. We investigate potential channels leading to decreased yields post hacking. We find evidence that being hacked reduces cyber risk by disciplining municipalities to move closer to the optimal level of IT spending. The third chapter investigates the impact of firm data collection and analysis of collected data on the riskiness of firm cash flows. I use a scraped data set of the third party resources loaded on firms’ websites as a measure of firm data collection and analysis practices. I find that firm u se of less effective web analytics is as sociated with an increase in the variance of sales, inventory, and both fixed and variable costs. This effect is de spite a lack of change in the level of these variables. Looking at the effect of treatment on the treated, there i s higher profit and sales variance during times of higher uncertainty. I use differences in web analytics technology and a change in their relative effectiveness as my identification strategy. As a case study of a large negative demand shock, I look at differences in fi rm reactions to COVID-19 based on their web analytics usage.

Coevolution of Small Business Strategy and Regulation: A Mixed-Methods Study of United States Craft Breweries

Thu, 01 May 2025 00:00:00 GMT

Coevolution of Small Business Strategy and Regulation: A Mixed-Methods Study of United States Craft Breweries Rixey V, Eppa This dissertation asks how do small firms overcome regulatory constraints despite powerful opposition? Significant research has documented the nonmarket strategies of large, multinational firms seeking to benefit from and capture regulatory systems. However, despite the historically important role of small and medium-sized enterprises (SMEs) in the economic and civic structures of the US, there is much we do not know about whether and how they attempt to exert their own influence in regulatory environments. To explore this, the US beer industry was selected as a strategic research site where SMEs have had a range of successes and failures in developing policy influence. In the late 1970s, the US beer industry rapidly consolidated to less than 100 breweries, but today, with the rise of small, craft breweries, there are over 9,000 breweries in the US. Over 7,000 of these focus on direct-to-consumer (DTC) sales, which were explicitly or practically illegal in all 50 states in 1980. How did this market and regulatory transformation take place and why did some states significantly change their policies to support small brewers while others did not? Two studies were conducted to explore this, an in-depth qualitative study of a single state and a mixed-methods comparative study of six states. The single state was selected for variation in policy outcomes over time and at local levels. Through interviews and archival research, it was revealed that craft breweries engaged in a bottoms-up approach, through which individual firms venue shift downwards, from state to local regulators, to successfully ease state-level constraints. In local public hearings, individual entrepreneurs blended local corporate social responsibility (CSR) with an experimental approach to corporate political activity (CPA) that motivated city-based regulators to challenge state-level restrictions on DTC business models. To understand how this process of developing policy influence unfolds in the absence of local regulators, the national trade associations in the beer industry were analyzed and six states where the state has near exclusive control over alcohol regulations were selected for further analysis. Controlling for a range of factors through a cross-sectional database led to a geographically proximate sample of six comparable states with wide variation in the favorability of policies and the number of breweries per capita. A unique dataset of over 5,000 legislative updates on proposed and enacted federal and state policy changes was supplemented with archival and interview data to assess policy influence. The conventional approach described in the literature, collective action via a trade association, was important but often insufficient. Each state had a functioning trade association representing most craft breweries, but sustained policy influence was observed only in states where full-time leaders of these associations understood the political landscape and developed policy partnerships to tilt the odds in their favor. Policy partnerships entailed legislation alleviating regulatory constraints while also including new provisions that ensured long-term alignment among the partners. Taken together, these studies reveal the vital importance of collective action extending beyond the focal industry for SMEs to develop policy influence at the local or state level.

Advancing Tendon-Driven Robotic Systems: From Climbing Robots to String Actuators

Thu, 01 May 2025 00:00:00 GMT

Advancing Tendon-Driven Robotic Systems: From Climbing Robots to String Actuators Poon, Ryan Joseph Mar Tendon-driven mechanisms provide a range of benefits for robotic systems, particularly by allowing actuators to be mounted at the base of a manipulator and reducing its inertia. This thesis explores two projects that exploit and advance tendon-driven mechanisms: a wheeled-grasping hybrid climbing robot with modular tendon-driven grasping arms and a hybrid twisted-winching string actuator. Called CLIMR (Cabled Limb Interlocking Modular Robot), the novel climbing robot adapts to columns of varying diameters by adding or removing modular arm links. CLIMR also features capabilities like self-locking (the ability of the robot to stay on the column without power), autonomous grasping, and rotation around the column axis. Mathematical models describe conditions for self-locking, vertical wheeled climbing, and complete grasping of a column. Simulations and experimental results validate the proposed models. The insights from CLIMR are then extended into general design strategies for future developments of similar hybrid climbing robots, focusing on methods to inform design decisions and assess metrics such as adaptability. Ultimately, this work provides a comprehensive framework for designing hybrid climbing robots, highlighting the potential of autonomous solutions for environments where climbing tall structures is critical. Stemming from this climbing robot work is a novel actuator system combining a twisted string actuator (TSA) with a winch mechanism. Relative to traditional hydraulic and pneumatic systems, TSAs are compact but face limitations in stroke length and velocity. This TSA-winch system overcomes these constraints without risking overtwisting by providing both high displacement winching and high force twisting modes. The design features a rotating turret that houses a winch and a worm gear transmission driven by a through-hole drive shaft. Models are developed for the combined displacement and velocity control of this system. Experiments validate the open loop model as well as the closed loop model, which uses a conductive string feedback controller with a gain scheduling and control effort allocation scheme. For specific cases that require large displacement winching followed by high force twisting over several repeatable cycles, an alternate design sacrifices complete string state control and replaces a motor with passive automatic clutches to achieve a seamless transition between modes triggered by the string load. The models of the clutch torque thresholds for this version of the actuator are verified by experiments. Overall, this research contributes to the development of more versatile and efficient actuation systems for tendon-driven robotic applications.

Coordination of distributed energy resources for a reliable, resilient, and affordable decarbonized grid

Thu, 01 May 2025 00:00:00 GMT

Coordination of distributed energy resources for a reliable, resilient, and affordable decarbonized grid Jagadeesan Nair, Vineet Rapid decarbonization of the power grid is essential to meet climate goals by reducing emissions and enabling sustainable electrification of sectors like transport and heating. This requires shifting from centralized fossil-fuel generation to variable renewables like wind and solar. The grid must also adapt to a growing number of small-scale, distributed energy resources (DERs) at the edge, such as rooftop solar, batteries, electric vehicles, and heat pumps. This thesis focuses on modeling, optimizing, and coordinating DERs to enable a flexible, resilient, and affordable grid. First, it proposes a novel hierarchical local electricity market for low and medium-voltage distribution grids. This structure enables DER participation through decentralized and distributed optimization, respecting grid physics while preserving privacy and scalability. The market is applicable to both balanced and unbalanced radial grids using two different convex relaxations and power flow models. Grid services are also priced based on duality theory. Numerical simulations show improved dispatch efficiency, reliability, voltage regulation, and lower retail electricity rates. Second, the thesis applies game theory and mechanism design to extract flexibility from autonomous, strategic DER owners. A repeated Stackelberg game with incomplete information and intertemporal constraints yields equilibrium pricing with closed-form solutions. Third, a distributed decision-making framework is developed to coordinate DERs for grid resilience. It mitigates cyber-physical attacks and outages, ranging from 5 to 40% of peak load, using local flexibility and grid reconfiguration, extensively validated through both software and hardware-in-the-loop simulations. Finally, the thesis addresses DER hosting capacity. New algorithms are developed that co-optimize the siting and sizing of diverse DERs under uncertainty using Monte Carlo sampling, stochastic programming, and k-means clustering for scenario reduction. Results show that intelligent DER coordination can defer grid infrastructure upgrades and support greater renewable integration and electrified demand growth. Together, these contributions provide analytical and simulation tools to improve the planning and real-time operation of future distributed, low-carbon power grids.

The Development and Utilization of Tandem Fluency in Human-Exoskeleton Interaction

Thu, 01 May 2025 00:00:00 GMT

The Development and Utilization of Tandem Fluency in Human-Exoskeleton Interaction Koo, Bon H. (Brandon) There is strong demand for portable technologies that enhance human power output while maintaining safety and range, not only in defense and industry but also in aerospace. Exoskeletons and other wearable powered devices have been proposed as solutions, but a major barrier to adoption is the issue of “fluency”: a combination of metrics representing the seamlessness of human-robot interaction. Most current exoskeleton systems, especially for non-cyclic motions, disrupt user intent and movement, often offering no benefit, or even causing harm by increasing discomfort and injury risk. This lack of fluency is frequently linked to poor intent recognition and absence of predictive control. To address this, we propose developing a human motion prediction system and studying its impact on fluency in exoskeleton-like devices and related human-centered technologies in real-world applications. We introduce an expanded metric “tandem fluency” based on conventional fluency, tailored for evaluating human-robot interaction (HRI) systems where human and robot agents are kinematically synchronized to perform functional tasks. We then develop a proof-of-concept and a functional deep neural network (DNN) capable of detecting human motion intent and predicting motion trajectories in advance using biosignals such as surface electromyography (sEMG). In parallel, we build and test prototype exoskeleton hardware with both single and multiple degrees of freedom. Finally, we conduct human trials with the full closed-loop tandem human-exoskeleton system to evaluate the impact of motion prediction-based control on tandem fluency. The results show that classification and regression prediction of human motion prior to initiation of physical motion is possible and can have performance necessary for practical application of this information, the prediction can be generated not only prior to the physical motion initiation, but often even before the full electrical activation of the primary agonist in many motions, the DNN is robust to variations in sensor hardware and input formatting, and furthermore the use of this prediction in the controls of a tandem robot system has potential to improve tandem fluency by positively affecting both subjective experience and objective/metabolic results.

Quantifying over Individual Concepts

Fri, 01 Sep 2023 00:00:00 GMT

Quantifying over Individual Concepts Kobayashi, Filipe Hisao Since Montague (1973), it has been assumed that quantificational DPs must, at least sometimes, be analyzed as quantifiers over individual concepts (i.e., functions from indices of evaluation to individuals). Because the domain of individual concepts is significantly greater than that of individuals, the challenge has always been how to properly constrain quantification over these objects. This dissertation proposes a solution to this problem by developing a novel theory as to how NPs are shifted from predicates of individual into predicates of individual concepts. The idea is that, since NPs are interpreted as restrictors, the nature of this shifting mechanism will constrain quantification. The proposal bears a strong resemblance to the analysis of interrogative clauses of Karttunen (1977): suitable predicates of individual concepts are built from the interaction of a type-shifting operation and existential quantifiers. In three cases studies, I show how this theory can solve old and new puzzles: (i) the different interpretations of sentences of the form ‘[Det NP] changed’ (Nathan 2006); (ii) two ambiguities in the interpretation of concealed questions (Heim 1979); and (iii) question intruders, a novel puzzle concerning the interpretation of both embedded interrogative clauses and concealed questions.

Within ‘Reason’: A Study of Normative Language

Fri, 01 Sep 2023 00:00:00 GMT

Within ‘Reason’: A Study of Normative Language Watkins, Eliot What do we mean when we say that someone ought to do something? What do we mean when we say that someone has a reason to do something? What do we mean when we say that someone has more reason to do one thing rather than another? The primary goal of this project is to shed light on these semantic questions. The picture of normative talk that I develop across this thesis has a distinctive feature: the notion of a reason (roughly, a fact that counts in favour of something) isn’t given any fundamental role to play. Instead, the meanings of ‘ought’, ‘must’ and ‘is a reason for…’ are all understood in terms of something gradable – they’re understood in terms of facts about how much reason there is for something to be done. Chapter One focuses on deontic modals like ‘ought’ and ‘must’. I argue that the standard semantics for these expressions is incompatible with the idea that facts about what you ought to do are connected with facts about what you have reason to do. I develop a new semantics for deontic modals which builds-in the connections between ought and reasons from the ground up. Chapter Two centres on ‘reason’. We use ‘reason’ as both a count noun (as in “there is a reason for you to read my dissertation”) and a mass noun (as in “there is some reason for you to read my dissertation”). I argue that the best semantics for ‘reason’ will treat the mass form as fundamental. ‘Reason’ is a predicate of a particular kind of state – the state someone is in when they have reason to do something. I turn this result into an argument against the enduringly popular idea that count noun reasons are normatively fundamental. Chapter Three stays with reasons. According to a standard picture, normative reasons do not extend beyond the boundaries of agency. If something isn’t an agent – if it can’t do rudimentary practical reasoning – then there can’t be normative reasons for it to do one thing rather than another. I argue that this standard picture gets things totally wrong: there are reasons for non-agents to be certain ways and do certain things. We must not analyse what it is to be a reason by appealing to distinctively agential capacities.

Lessons from CP in Passamaquoddy and beyond

Fri, 01 Sep 2023 00:00:00 GMT

Lessons from CP in Passamaquoddy and beyond Grishin, Peter Nicholas This thesis explores various aspects of CP morphosyntax in Passamaquoddy-Wolastoqey and other Algonquian languages and their consequences for broader generative syntactic theory. It consists of two parts: one investigates clause typing and clause size in Passamaquoddy, and the other investigates the properties of a CP-layer agreement marker, the peripheral suffix, across Algonquian. In addition, a lengthy background chapter offers new data and insight on the correct analysis of the inverse and obviation in Passamaquoddy and across Algonquian. Part I studies the distribution of the three morphologically-distinguished non-imperative clause types in Passamaquoddy: the independent, the conjunct, and the subordinative. I argue that their distribution in complementation and coordination structures falls out naturally from their structural size, following the work of Wurmbrand and Lohninger (2023) and Bjorkman (2012, 2013). I support this conclusion by carefully investigating how each clause type interacts with Ā phenomena like wh movement and long distance agreement, showing that various complex interactions between these syntactic processes are derivative of clause size: independent clauses and conjunct clauses under epistemic attitudes are large, phasal CPs, conjunct clauses under direct perception predicates are smaller, non-phasal CPs, and subordinative clauses are bare TPs. Part II studies two unexpected properties of peripheral agreement across Algonquian: (i) its preference for agreeing with third persons, no matter their syntactic role (found in all Algonquian languages); and (ii) its preference for agreeing with the least local goal (found in languages like Passamaquoddy, Ojibwe, and Wampanoag). I explore the consequences of these typologically unusual properties for the theory of φ agreement and provide an analysis of the cross-Algonquian variation we find in peripheral agreement (building on Xu 2021, 2022). I argue that Algonquian third person preference forces us to accept Nevins (2007) and Trommer’s (2008) conclusion that third person cannot be underspecified relative to first and second person, even in the syntax (contra Preminger 2019a and van Alem 2023). Additionally, I show that Algonquian lowest preference doesn’t force us to give up on standard locality properties of Agree, and argue for an analysis under which C agrees with all matching accessible goals, but only spells out the last Agree relation—Expone Outermost—building a parallel with similar ideas in the domain of multiple case assignment. Finally, I capture cross-Algonquian variation in peripheral agreement by varying the specification of the peripheral agreement probe and varying which arguments are able to shift out of the VP phase.

*ABA in Multidimensional Paradigms: A MAX/DEP-based account

Fri, 01 Sep 2023 00:00:00 GMT

*ABA in Multidimensional Paradigms: A MAX/DEP-based account Zompì, Stanislao The last decade and a half has witnessed intensive research into *ABA universals—generalizations such as “If a nominative and the corresponding dative have the same exponent, then the corresponding accusative has that exponent, too” (Caha 2009; Smith et al. 2019). Most existing work on these universals has only focused on one ‘paradigm column’ at a time, by checking a given paradigm’s nominative singular, accusative singular, and dative singular, for example, with no heed to whether any of the relevant exponents would also show up in that paradigm’s nominative plural, accusative plural, or dative plural. However, some recent literature has pointed out that inspecting full paradigms is crucial to our understanding of *ABA, because some classic accounts that derive *ABA column-internally turn out to also make predictions as to what may or may not happen across columns, and those predictions are often incorrect (cf., among others, Christopoulos & Zompì 2022). In this dissertation, I review those incorrect predictions and replace them with a novel generalization specifically concerning *ABA-like effects in multidimensional paradigms. I then set out to derive this generalization by setting up an exponent-selection system wherein exponents may both be underspecified and be overspecified with respect to their exponenda, with each of these departures from a perfect match being penalized but not necessarily fatal. In particular, I explicitly implement this intuition in optimality-theoretic terms, via a strict-domination ranking of violable Max and Dep constraints (cf. in particular Ackema & Neeleman 2005; Wolf 2008; Müller 2020), and I show that the resulting system, while restrictive enough to derive the desired generalization, is also powerful enough to afford a natural account of some notoriously unnatural (‘morphomic’) exponent distributions in the inflection of Germanic pronouns and Romance verbs.

Essays on Bayesian Entrepreneurship: Evaluating and Commercializing Unconventional Ideas

Thu, 01 May 2025 00:00:00 GMT

Essays on Bayesian Entrepreneurship: Evaluating and Commercializing Unconventional Ideas Gius, Luca This dissertation investigates a fundamental challenge complicating the evaluation and commercialization of entrepreneurial opportunities: some ideas are valuable precisely because not everyone recognizes their worth. The first essay analyzes barriers against the commercialization of contrarian ideas. Researchers working with unpopular AI algorithms tend to commercialize their work only after a successful public evaluation. Those who clear this hurdle subsequently achieve better entrepreneurial outcomes. A regression-discontinuity analysis shows that this partly reflects status quo bias: for unpopular methods only, winning a contest serves as a certification, channeling disproportionate resources to the winner while equally strong near-misses remain sidelined. The second essay finds that greater judge disagreement in venture competitions predicts higher future success, especially for more distinctive startups. The third essay shows that skewness in idea value exacerbates asymmetric information in markets for ideas. Using data from auctions for digital businesses, I illustrate how this can explain why online marketplaces for ideas have struggled to emerge despite lowering transaction costs: informational frictions severely depress bids and prevent high-value digital startups from trading. The final essay, coauthored with Alfonso Gambardella and Scott Stern, introduces the archetype of Homo Entrepreneuricus: an entrepreneur who deliberately tests subjective beliefs through structured experimentation to navigate uncertainty.

Exploiting Additive Structure in Algorithm Design and Fine-Grained Complexity

Thu, 01 May 2025 00:00:00 GMT

Exploiting Additive Structure in Algorithm Design and Fine-Grained Complexity Jin, Ce In this thesis, we investigate the fine-grained complexity of various algorithmic problems with an additive flavor, including 3SUM, Subset Sum, and their close relatives. We explore their connections to various areas, such as graph algorithms, discrete optimization, combinatorial pattern matching, and computational geometry. Our new results include improved algorithms and conditional lower bounds for a wide range of problems, answering multiple open questions from the literature: • Conditional lower bounds for graph problems: We prove new lower bounds for 4-Cycle Listing and Approximate Distance Oracles conditioned on the 3SUM Hypothesis. As a key intermediate step, we show a fine-grained reduction from 3SUM to the special case of 3SUM where all pairwise sums of input numbers are distinct. • Combinatorial pattern matching: We design improved algorithms for Text-to-Pattern Hamming Distances, Pattern Matching with Wildcards, and Geometric Pattern Matching, by drawing connections from 3SUM and sparse convolution. • Knapsack-type problems: We obtain a pseudo-polynomial time algorithm for 0-1 Knapsack with (conditionally) near-optimal dependence on the maximum item weight, an improved approximation scheme for the counting problem #Knapsack, and improved exponential time algorithms for the total search problem Pigeonhole Equal Subset Sum. In order to obtain these results, we employ and develop techniques based on convolution algorithms and their extensions, as well as classic tools from additive combinatorics.

Content Creator Conduct

Thu, 01 May 2025 00:00:00 GMT

Content Creator Conduct Du, Jason This thesis investigates the behaviors of content creators. The first study examines whether musicians learn from the success of earlier songs when they create new ones, finding that tracks on a musician’s next album tend to be more similar to the songs that performed better on their current album. The second study explores the cultural, social, and psychological aspects of content creation by tracing first-person singular pronoun usage in contemporary music, revealing geographic, temporal, and genre-based patterns. The third study analyzes the association between content creators' learning tendencies and the explainability of previous outcomes, showing that news editors are more likely to resemble previous popular headlines when those outcomes are more explainable. Collectively, these studies facilitate understanding of the factors that underlie content creation.

Industrial Pollution and Firm Ownership Structure: Evidence from M&A

Thu, 01 May 2025 00:00:00 GMT

Industrial Pollution and Firm Ownership Structure: Evidence from M&A Zhang, Cindy This paper studies whether firm ownership structure influences pollutive activity. Using facility-level data from the Toxics Release Inventory, I employ a differences-in-differences (DiD) approach to compare toxic chemical release and pollution prevention activity between public and private firms' facilities by exploiting ownership changes. I compare facilities initially owned by private firms that were acquired by public firms and those that were acquired by private firms in the same year. My findings suggest that public acquirers significantly reduce toxic release activity relative to private acquirers. In the reverse case, I find that private acquirers decrease abatement, but pollution volume does not differ significantly. However, for later ownership changes in my sample, private acquirers increase toxic release volume and intensity significantly relative to public acquirers. Lastly, I explore how financial constraints and the local political environment moderate pollution activity. Debt-constrained public acquirers show no significant difference in pollution activity from private acquirers. In Democrat-leaning counties, public acquirers reduce toxic releases more than private acquirers, but in Republican-leaning counties, the differences are less pronounced. Overall, my findings suggest that public firms have decreased toxic release activity over time, but the declines have been offset by increases from private firms.

Corporate Transparency and Cybersecurity Risks

Thu, 01 May 2025 00:00:00 GMT

Corporate Transparency and Cybersecurity Risks Kim, David Sunghyo I study whether disclosure mandates alter the equilibrium of cyberattacks by unintentionally informing cybercriminals. The California Consumer Privacy Act (CCPA) requires companies to disclose their personal information collection practices to consumers, inadvertently informing cybercriminals about the potential benefits of breaching each firm. Using a difference-in-differences design, I find that firms disclosing the collection of valuable personal data face an increased probability of data breaches. These firms also strengthen their cyberdefenses both in terms of cybersecurity software and cybersecurity specialists. Firms trade off cybersecurity costs against the risk of data breaches, with the increase in breach probabilities more pronounced among firms that invest less in cybersecurity. Finally, I find that firms adjust their data collection policies as additional defense strategies. Overall, this study highlights the trade-off between transparency and cybersecurity risks in today’s digital economy.

A Systematic Political Philosophy of Education

Thu, 01 May 2025 00:00:00 GMT

A Systematic Political Philosophy of Education Pavel, Sonia Maria My dissertation proposes a fundamental repositioning of philosophy of education relative to political philosophy. I argue that we cannot afford doing political philosophy without a theory of education, just as we cannot afford making philosophy of education modular, insulated from the rest of political philosophy. To this end, I propose a systematic political philosophy of education, meaning both a systematization of existing approaches to education and a comprehensive assessment of their merits and limitations. I reconstruct the main theories of education – liberal, conservative, democratic, and critical – from their most basic social ontological assumptions to their political programs for education. I then argue that they all struggle to realize their goals for education either as a result of flawed social ontological assumptions or because of a failure to institutionalize their commitments in practice. The lessons I draw from these critiques form the basis of my own novel systematic theory of education. My theory combines traditional political philosophy with insights spanning critical theory, social ontology, and education studies. The central goal is to reconfigure the school as a democratic institution of social learning that not only enables the flourishing of all students but helps society as a whole progress. The project advances on two levels: a methodological and a substantive-normative one. Methodologically, I resist a growing tendency towards the unmooring of political philosophy and philosophy of education. This tendency is peculiar both from a historical and a conceptual perspective. Historically, education was a core issue of political philosophy. Many, even most, of the canonical political philosophers started from the assumption that education is a central purpose of political life. In my substantive introduction, I take a historical excursus through the canonical political thinkers who best exemplify this emphasis on education: Plato, Rousseau, and Dewey. For all the differences in their views, all three understood education as essential to realizing their visions. They would have regarded any political philosophy that failed to address education as incomplete. Today, however, few political philosophers address the subject at all, let alone give it pride of place in their theories. This unmooring has had bad consequences for both subfields. Much contemporary work in philosophy of education takes for granted a liberal social ontology and liberal normative commitments without sufficient critical scrutiny. Similarly, most contemporary political theory neglects the topic of education and operates under the assumption of fully formed liberal agents. The lack of conceptual clarity is mirrored in political practice. Education is marred by persistent and seemingly intractable disagreements – from controversies about indoctrination to failures to realize the ideal of equality of opportunity. Our substantive disagreements about education, I argue in my first chapter, are not merely value disagreements about the goals of education. They stem from deep-rooted social ontological assumptions about the nature of human beings and society. But these social ontological assumptions are rarely acknowledged, let alone articulated, by political philosophers or philosophers of education. To correct this, I propose a novel metatheory that shows the systematic connections between the social ontology, normative commitments, and political programs of our dominant approaches to education (liberal, conservative, democratic). My reconstruction illuminates several surprising agreements and differences between them. For example, it reveals that many of our most heated political debates about education, between left and right liberals, are merely intramural disagreements among thinkers committed to the same individualist ontology. The systematic reconstruction also illuminates these theories’ failure to generate a coherent vision for education. My critiques show that each approach is characterized by a flawed or incomplete social theory which prevents it from promoting its own values and fulfilling its aims for education. In the case of liberal theories, I show that the liberal goal of cultivating autonomy is selfundermining in light of liberal theory’s individualist social ontology. In the second chapter, I turn to critical theories, which focus on the function of education in reproducing our broader social system. Whereas the dominant approaches start by asking about the nature and goals of education in general, critical theories analyze our contemporary educational systems under specific political and economic conditions. They reveal how schools contribute to perpetuating an oppressive and unjust social system. In other words, the focus of these theories is not on the school as a standalone institution, but as a particularly important subsystem in a larger process of social reproduction. While they are promising in many ways, I nevertheless argue that critical theories of education also have distinct limitations. In particular, even though their social theory and normative commitment are more compelling than the dominant views’, they do not satisfactorily translate these into practical proposals for remaking our systems of education. Having found none of the existing approaches fully satisfactory, I start developing the positive and evaluative dimensions of my own view in the third chapter. I go beyond critical social theory while relying on the broad strokes of its ontology of the human. My aim is to supplement this ontology by drawing on both empirical social studies and complexity theory to more precisely characterize the social relations and practices that constitute the domain of education. More specifically, I argue that we can best understand the educational subsystem by attending to its overlap and co-integration with the family, the state, and economic production. Schools are the mediating institutional domain between the family on one hand and the polity and economic production on the other. At the evaluative level, I articulate three critiques of social pathologies that I believe have been ignored or underutilized in critical education studies: alienation, commodification, and fragmentation. Alienation refers to a pathological relation of disconnection from one’s own learning, other students, and teachers. Commodification and fragmentation, on the other hand, are problems with the organization and distribution of resources in the education system. In my fourth and final chapter, I propose a new program for education that seeks to overcome some of the barriers faced by other systematic theories of education. Attempting to counter the problems I diagnosed and explained in the third chapter, I argue for a few different kinds of interventions. First, I propose restructuring the educational system in order to resist fragmentation by pursuing a unified distributive pool, consolidating school districts, and abolishing charters. Second, I argue for a reconfiguration of the co-integrated subsystems of the family, the school, and production that seeks to empower both children and those involved in their care to be involved in free, meaningful work. Finally, I articulate a set of classroom-level practices that seek to equalize access to development for individual students while cultivating their collective social and political imagination. One of the long-term goals is to make schools into democratic institutions of social learning, through which we strive to remove social blockages such as ideology and reflexivity deficits, in order to collectively solve political problems.

A Semantic Account of Distributional Constraints on Temporal in-Adverbials

Fri, 01 Sep 2023 00:00:00 GMT

A Semantic Account of Distributional Constraints on Temporal in-Adverbials Rouillard, Vincent S Temporal in-adverbials (TIAs) are a class of English expressions that can be exemplified with in three days. They are remarkable in that, depending on the syntactic position they occupy, TIAs are subject to very different distributional constraints. In some configurations, their licensing is conditioned by the lexical aspect of verbal predicates. In others, these expressions are negative polarity items. Though both varieties of TIAs have been discussed extensively in the semantics literature (Gajewski, 2005, 2007; Hoeksema, 2006; Iatridou and Zeijlstra, 2017, 2021; Krifka, 1989, 1998), no attempt has been made to understand the relationship between the two. I offer a unified semantic analysis of TIAs, which derives from semantic principles their eclectic distributional constraints.

Metrical Grids and Active Edges

Fri, 01 Sep 2023 00:00:00 GMT

Metrical Grids and Active Edges Asherov, Daniel Theories of word stress assignment differ in the kind of representations they adopt. One family of theories takes stress to be assigned by grouping stress-bearing elements into small units below the level of the word (typically, metrical feet), such that one element in each unit is marked as stronger, hence stressed (e.g., Liberman and Prince 1977; Hayes 1980). Another family of theories, often referred to as grid-only, models stress assignment without appealing to feet or similar bracketed representations above the syllable (Prince 1983; Selkirk 1984; Gordon 2002). While the grid-only approach generates the attested languages with relatively simple representations, it also generates a host of patterns which are very different from those attested in human languages (Hayes 1995; Kager 2012; also see Stanton 2016). This thesis aims to solve a set of overgeneration problems that arise in the grid-only approach. The solution involves three components. The first is a novel class of constraints that are sensitive to word edges but unspecified to the edge they apply to (left or right). The value of this edge, considered the “active” edge, is determined by the ranking between two competing constraints (cf. Richards 2016). The second component involves a specific characterization of alignment constraints and the crucial exclusion of computationally weaker or stronger alternatives. The third component is a set of principled fixed rankings between two classes of constraints. In particular, I propose that constraints sensitive to the active edge systematically outrank constraints that regulate rhythmic alternations (cf. van der Hulst 1997; 2012). The result is a grid-only theory of stress that has a significantly tighter fit to the typology compared to previous theories.

Visibility in synthetic aperture radar satellite data: metric formulation, observation scheduling, and orbit design

Thu, 01 May 2025 00:00:00 GMT

Visibility in synthetic aperture radar satellite data: metric formulation, observation scheduling, and orbit design Kramer, Evan L. Earth observation satellites serve as vital information gatherers for effectively addressing some of humanity’s most pressing challenges including management of limited resources and minimization of losses from disasters. Synthetic aperture radar (SAR) is a type of active remote sensing instrument that operates in the microwave portion of the electromagnetic spectrum and is a preferred Earth observation system thanks to the reliable imagery it can collect in all illumination and weather conditions. SAR data is acquired using a side-looking viewing geometry in which the radar is pointed perpendicular to the satellite platform’s direction of motion. This viewing geometry, in conjunction with the illuminated terrain’s topography, results in geometric distortions termed layover and shadow. These distortions degrade the utility of the collected imagery since they effectively obscure portions of the image and preclude extraction of actionable insights. While geometric distortions will be everpresent in SAR imagery, their location and coverage can be manipulated by controlling the relative orientation between the satellite and the illuminated topography. Such manipulation has historically been infeasible for legacy SAR satellites that collect globally consistent data sets under rigid operating requirements. However, the recent advent of commercial SAR satellite constellations has re-framed the practicality of carefully tuned observation geometries that maximize region of interest visibility. Commercial SAR constellations operate on a task-wise basis that grants data end-users flexibility in specifying desired observation parameters including acquisition times and observation geometries. However, a mismatch between on-orbit capabilities and delivered data quality exists due to a lack of formalized tools for planning observations with maximum region of interest visibility. Specifically, no systematic method for identifying visibility-favorable observation geometries exists. This dissertation addresses this gap in a stepwise approach. First, an extension of opensource radar processing software is developed that enables prediction of layover and shadow in a 2D distortion mask for any satellite-target relative geometry. Visibility metrics are then defined to represent the favorability of a particular observation geometry with respect to a distortion mask. The computation of visibility metric scores at geometries spanning the entire sample space enables creation of visibility maps that completely characterize the visibility characteristics of a given region of interest. To broaden the suitability of visibility maps for observation planning, a set of generalizable visibility maps are created to enable estimation of region of interest visibility characteristics in mission scenarios that are computationallyconstrained and information-limited. Visibility maps are then directly integrated into satellite operations by developing the first SAR observation scheduling algorithm that explicitly accounts for visibility. Finally, visibility is considered in the orbit design process to establish general guidance on optimal repeat ground track orbit parameters for pre-defined region of interest visibility characteristics. Region of interest visibility improvements of up to 90% are obtained for individual tasks when using the observation planning tools developed in this dissertation. Constellation-wide visibility improvements of 18% are achieved with modest reductions in traditional performance measures when integrating visibility into observation scheduling. Two-fold improvements in the visibility characteristics of observation opportunities are attained for orbits designed to maximize overpass geometry quality. The contributions of this dissertation are timely, given the concurrent proliferation of flexible, high-resolution SAR observation capabilities, and lay the groundwork for enabling the acquisition of SAR data that is maximally useful for limited resource management, disaster response, and other applications.

Essays on Content Moderation Interventions for Addressing Online Misinformation

Thu, 01 May 2025 00:00:00 GMT

Essays on Content Moderation Interventions for Addressing Online Misinformation Martel, Cameron In Chapter 1, I examine the efficacy of fact-checker warning labels as a content moderation intervention for addressing online misinformation. Warning labels from professional fact-checkers are one of the most historically used interventions against online misinformation. But are fact-checker warning labels effective for those who distrust fact-checkers? In a first correlational study, we validate a measure of trust in fact-checkers. Next, we conduct meta-analyses across 21 experiments in which participants evaluated true and false news posts and were randomized to either see no warning labels or to see warning labels on a high proportion of the false posts. Warning labels were on average effective at reducing belief in, and sharing of, false headlines. While warning effects were smaller for participants with less trust in fact-checkers, warning labels nonetheless significantly reduced belief in, and sharing of, false news even for those most distrusting of fact-checkers. Our results suggest fact-checker warning labels are a broadly effective tool for combatting misinformation. In Chapter 2, joint with Jennifer Allen, Gordon Pennycook, and David G. Rand, I investigate the potential of crowdsourced fact-checking systems to identify misleading online content. Social media platforms are increasingly adopting crowd-based content moderation interventions for identifying false or misleading content. However, existing theories posit that lay individuals can be highly politically biased, and that these strong political motivations inherently undermine accuracy. Alternatively, we propose that political and accuracy motivations may, in some cases, operate in tandem – in which case politically motivated individuals need not hamper truth discernment. We empirically assess this by analyzing a survey study of misinformation flagging and field data from X’s Community Notes. Consistent with a simple model of flagging behavior, posts that are both false and politically discordant are flagged the most. Importantly, we find that more politically motivated users flag a greater number of posts, engage in more politically biased flagging, and yet exhibit the same or better flagging discernment. Together, these results show that politically motivated individuals are integral to provisioning a high overall quantity and quality of crowdsourced fact-checks. In Chapter 3, I assess the perceived legitimacy of different content moderation interventions for addressing online misinformation. Current content moderation practices have been criticized as unjust. This raises an important question – who do Americans want deciding whether online content is harmfully misleading? We conducted a nationally representative survey experiment in which U.S. participants evaluated the legitimacy of hypothetical content moderation juries tasked with evaluating whether online content was harmfully misleading. These moderation juries varied on whether they were described as consisting of experts, laypeople, or non-juries. We also randomized features of jury composition (size, necessary qualifications) and whether juries engaged in discussion during content evaluation. Overall, participants evaluated expert juries as more legitimate than layperson juries or a computer algorithm. However, modifying layperson jury features helped increase legitimacy perceptions – nationally representative or politically balanced composition enhanced legitimacy, as did increased size, individual juror knowledge qualifications, and enabling juror discussion. Our findings shed light on the foundations of institutional legitimacy in content moderation and have implications for the design of online moderation systems.

Materials and Devices for Optoelectronic Packaging

Sat, 01 Feb 2025 00:00:00 GMT

Materials and Devices for Optoelectronic Packaging Weninger, Drew Michael Over the last two decades, improvements in semiconductor manufacturing have allowed for the commercialization of silicon photonic integrated circuits with over 10,000 devices. These chips are critical to data and telecommunications networks where they convert and encode optical signals to electrical signals, and vice versa, in the form of transceivers. Scaling up the number of transceivers and optical fiber connections, or optical input/output (I/O), will be critical to meet the exponential rise in demand for cloud data capacity since 2010. However, the costly process of active alignment and bonding of optical fiber arrays directly to photonic chips presents a barrier to their high volume packaging and assembly. This approach limits optical I/O density to a maximum of 8 connections per millimeter since optical fibers for communications applications have cladding diameters of 125 micron. To address this challenge, this thesis explored a new field of silicon integrated photonics involving chip-to-chip (i.e. flip-chip) optical coupling. Evanescent chip-to-chip couplers were designed, fabricated, packaged, and tested for directly connecting silicon photonic chips to other silicon photonic chips, interposers, or printed circuit boards using automated assembly. The design's compact footprint allows for coupler pitches below 10 micron, or an optical I/O density of greater than 100 connections per millimeter, to be realized - an order of magnitude improvement over fiber-to-chip connections. By designing the coupler to use silicon materials and back-end-of-line compatible packaging processes, ease of integration with existing microelectronic foundry tool sets was ensured. Results from an experimental flip-chip coupler prototype showed greater than 90% coupling efficiency with micron scale alignment tolerances when coupling from silicon nitride to silicon-on-insulator waveguides, the first demonstration of such a device. To further improve optical flip-chip coupler performance, designs were proposed for combining the evanescent coupler with an integrated graded index lens using silicon oxynitride films. Such a device would provide a universal coupling interface in silicon photonics for both chip-to-chip or fiber-to-chip connections. Simulations showed sub-dB coupling loss across all interfaces including flip-chip coupling across a 10 micron gap. Initial fabrication processes were established to deposit, pattern, and etch greater than 10 micron thick silicon oxynitride graded index lenses on silicon and glass substrates. In showing that automated pick-and-place tools can be used for photonic chip assembly, this work represents a critical step in eliminating active alignment and sustainably scaling optical I/O in future transceiver packages.

Selecting for and Selecting Despite: A Javanese case study

Fri, 01 Sep 2023 00:00:00 GMT

Selecting for and Selecting Despite: A Javanese case study Lesure, Cora This is an investigation of the argument structure of Javanese (Austronesian, Indonesia) which focuses on the distribution of four core derivational morphemes: the Actor Voice prefix, and the suffixes -ake, -i, and -an. The project is based on original consultant work conducted with a speaker of the Central dialect of Javanese. The work establishes language internal diagnostics for various aspects of a stem's lexical semantics and lexical category and then utilizes these criteria to analyze a wide variety of morphological derivatives, both verbal and nominal. The resulting analysis is able to predict the distribution of derivational morphemes and the nature of their resulting derivatives to a higher degree than what was previously understood to be possible.

Uncovering Mandarin Speaker Knowledge with Language Game Experiments

Fri, 01 Sep 2023 00:00:00 GMT

Uncovering Mandarin Speaker Knowledge with Language Game Experiments Fu, Boer Mandarin Chinese offers many intriguing puzzles for linguists because it has a shortage of morphophonological alternations. This has resulted in indeterminacy in various aspects of its phonological grammar, triggering much debate on syllable structure and allophonic mapping. The ambiguity of the data is also a problem for children acquiring Mandarin since alternative grammars can account for the surface forms equally well. In order to find out what Mandarin speakers have learned about the phonology of their language, I conducted two language game experiments based on fanqie secret languages. It was found that markedness and faithfulness constraints are psychologically real for Mandarin speakers. Furthermore, the interactions between markedness and faithfulness constraints are shown to have an effect on glide movement in the language game. In addition, much speaker variation was observed in the experiment. I demonstrate that it is the result of constraint ranking variation. Nevertheless, general population-level trends on constraint ranking could still be identified. These trends lead to insights on phonological learning beyond Mandarin, showing evidence for naturalness bias and lexicon optimization.

Risk-Aware Reinforcement Learning with Safety Constraints

Thu, 01 May 2025 00:00:00 GMT

Risk-Aware Reinforcement Learning with Safety Constraints Feng, Meng Safety is a critical concern in reinforcement learning (RL) and learning-based systems more broadly, as ensuring reliable and safe decision-making is essential for their deployment in real-world applications. Traditional approaches to address safety often rely on techniques such as reward shaping, carefully curated training data, or explicit handcrafted rules to avoid unsafe actions. More recent advancements have adopted the Constrained Markov Decision Process (CMDP) framework, which trains agents while explicitly enforcing constraints on auxiliary measures such as safety or risk. However, these methods often suffer from significant constraint violations. This thesis identifies the root cause of such violations as stemming from the pursuit of maximal task performance in each policy update. Given the inherent limitations of sample-based constraint assessments in RL, where data is limited and approximation errors are inevitable, these methods often fail near constraint boundaries, leading to excessive violations. To address this, we propose a novel constrained reinforcement learning algorithm that dynamically adjusts its conservativeness during policy updates. By incorporating the risk of constraint violation into the update process, our method can shift focus toward constraint satisfaction when violations are likely, while still striving to improve task performance whenever feasible. Our algorithm reduces constraint violations by up to 99% compared to state-of-the-art baselines while achieving comparable task performance. In the second part of this thesis, we extend CMDPs to address multi-goal, long-horizon problems. We augment the CMDP formulation to incorporate goals, enabling it to handle multiple goals while preserving goal-independent constraint specifications in CMDP. To tackle the complexity of long-horizon tasks with high-dimensional inputs (e.g., visual observations), we propose a method that integrates planning with safe reinforcement learning. By leveraging deep reinforcement learning, we acquire the essential components for planning, including a low-dimensional state-space representation and planning heuristics. The planning algorithm then decomposes long-horizon problems into a sequence of shorter, easier subgoal-reaching tasks. The learned agents safely navigate toward these subgoals step by step, ultimately reaching the final goal. We evaluate our method on both single-agent and multi-agent tasks. In 2D navigation, our approach demonstrated up to 74.2% risk reduction, while in visual navigation, it achieved up to 49.3% risk reduction, all while reaching comparable or better success rates.

Obscured universality in Mandarin

Fri, 01 Sep 2023 00:00:00 GMT

Obscured universality in Mandarin Chen, Fulang In this dissertation, I investigate the apparently distinctive syntactic properties associated with the BEI-construction, the BA-construction, and resultative constructions in Mandarin Chinese, which I argue obscure properties that are universal across natural languages. In the case of the Mandarin BEI-construction, it exhibits both passive-like and tough-movementlike properties. I argue for a novel analysis of the BEI-construction as a passive construction, where the passive head/BEI hosts a composite probe [𝜙 + Ā], which triggers composite A/Ā-movement, in the sense of Van Urk (2015). The subject in the BEI-construction is derived via (successivecyclic) composite A/Ā-movement, followed by a terminating step of A-movement, similar to Longenbaugh’s (2017) analysis of English tough-movement. Under the proposed analysis, the mixed A/Ā-properties associated with the BEI-construction are direct consequences of composite A/Āmovement (following Van Urk 2015; Longenbaugh 2017). In the case of the Mandarin BA-construction, it involves an apparently pre-posed noun phrase (the post-BA NP) with an affectedness interpretation, which can be identified with either the subject of a resultative phrase in a complex predicate or the direct object of a simple transitive verb. I argue for a novel analysis of the Mandarin BA-construction as a causative construction, where the causative head, which selects a predicate of the caused/resulting event and projects a predicate of the causing event (following Pylkkänen 2002, 2008), has two additional arguments: a causer and a causee. The post-BA NP, as the causee argument of the causative head, also controls a PRO subject in a resultative phrase (following Huang 1992), which is overt in a complex-predicate BAconstruction and is phonologically null in a simple-transitive BA-construction (following Sybesma 1992, 1999). The post-BA NP is interpreted as being affected in the causing event, in the sense that it is caused to perform an action or undergo a change of state (following Alsina 1992). Lastly, in Mandarin, there is no apparent unaccusative-unergative distinction in resultative constructions, unlike languages like English, where distinctions between resultative constructions with unaccusative and unergative matrix verbs follow from the Unaccusativity Hypothesis (Perlmutter 1978; Burzio 1986) and general principles such as the Direct Object Restriction (Simpson 1983; Levin & Rappaport Hovav 1995) and Burzio’s generalization (Burzio 1986). I argue that resultative constructions in Mandarin are causative constructions, where the causative head has four possible argument structures, depending on whether the matrix verb is unaccusative, unergative, or transitive, as well as the semantic relation between the matrix subject and the matrix verb (and between the post-verbal NP and the matrix verb). Despite the fact that the argument structure of the causative head obscures the argument structure of the matrix verb, I argue that in Mandarin resultative constructions, the sole argument of an unaccusative matrix verb is always a causee argument, whether or not an additional causer external argument is present, while the sole argument of an unergative matrix verb, which is a causer external argument otherwise, is a causer argument when the causer is an internal argument. The dissertation showcases how Mandarin provides insight in defending and expanding our knowledge of cross-linguistic properties such as passivization (which embodies Burzio’s generalization and feature-driven movement), composite probing, the bi-clausal syntax and bi-eventive semantics of causative constructions, as well as the nature of affectedness (in causative constructions) and implications for the Unaccusativity Hypothesis and the Uniformity of Theta-Assignment Hypothesis (Baker 1988).

How Joint Inference of the Lexicon and Phonology Affects the Learnability of Process Interactions

Fri, 01 Sep 2023 00:00:00 GMT

How Joint Inference of the Lexicon and Phonology Affects the Learnability of Process Interactions Yang, Christopher Contemporary phonological research has increasingly become interested in exploring the topic of learnability through the use of computational models. However, many of the proposed models lack one or more of the following properties. (1) Many models do not consider the effect of the lexicon at all on performance, and those that do fail to consider the effect contextual allomorphy has on performance. (2) Many models characterize learnability in terms of the algorithmic implementation of search, rather than a more principled relationship between the data and the hypothesis space. These properties are critically relevant when it comes to the learnability of process interactions. The experimental literature has demonstrated that artificial languages exhibiting patterns generated from certain process interactions are more likely to be successfully reproduced and generalized by participants than others (Ettlinger 2008; Kim 2012; Brooks, Pajak, & Baković 2013; Prickett 2019). The historical literature has likewise noted that surface patterns generated from particular process interactions are more likely to change in systematic ways than others, including lexicalization, in which an alternation is encoded into the lexicon instead of the phonology, and reanalysis, in which a surface generalization is lost or changed entirely (Kiparsky 1968, 1971). Each of these hypotheses make different predictions when generating forms not seen during training. In this dissertation, I make the following contributions. (1) I propose a novel noisy-channel model of morphophonological learning. This model jointly infers a weighted space of consistent and nearly consistent lexicons and grammars from labelled, unparsed surface data. Predictions are generated given the entirety of the inferred weighted space. (2) I compare the predictions of the model to the results two artificial language learning experiments, which, despite involving the same underlying processes, produced contradictory results. I show that the model is able to achieve the results of both experiments under a unified account: the generalizability of a pattern is determined by the number of hypotheses compatible or nearly compatible with that pattern.

The chemical and physical constitution of reduced copper-red glazes

Sun, 01 Jan 1961 00:00:00 GMT

The chemical and physical constitution of reduced copper-red glazes Brown, Sherwood Fiske. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Metallurgy, 1961; Includes bibliographical references (leaf 60).

Essays on structuralism and development

Thu, 01 Jan 1981 00:00:00 GMT

Essays on structuralism and development Boutros-Ghali, Y. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1981; Includes bibliographies.

Neutron scattering study of the magnetism and structural phases of superconducting La₂CuO₄₊y̳

Sat, 01 Jan 2000 00:00:00 GMT

Neutron scattering study of the magnetism and structural phases of superconducting La₂CuO₄₊y̳ Lee, Young Sang, 1971- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 2000; In title on t.p., double-underscored "y" appears as subscript.; Includes bibliographical references (p. 195-215).

Resilient Object Perception for Robotics

Thu, 01 May 2025 00:00:00 GMT

Resilient Object Perception for Robotics Shi, Jingnan A broad array of applications, ranging from search and rescue to self-driving vehicles, requires robots to perceive and understand the geometry of objects in the environment. Object perception needs to reliably work in a variety of scenarios and preserve a desired level of performance in the face of outliers and shifts from the training domain. Obtaining such a level of performance requires robust estimation algorithms that are able to identify and reject outliers, as well as techniques to continually improve performance of learningbased perception modules during test-time. In this thesis, we address these challenges by proposing (1) certifiably optimal solvers and a graph-theoretic framework that together help achieve state-of-the-art pose estimation performance even under high outlier rates, (2) self-supervised object pose estimators that can improve performance during test-time with accuracy comparable to state-of-the-art supervised methods, and (3) a test-time adaptation method for both object shape reconstruction and pose estimation without the need for CAD models. Throughout the thesis, we demonstrate that by using a variety of tools from optimization and learning, we can develop resilient object perception systems that perform reliably in a wide range of conditions.

Grain Boundary Solute Segregation in Vanadium

Sat, 01 Feb 2025 00:00:00 GMT

Grain Boundary Solute Segregation in Vanadium Ng, Daniel S. Vanadium alloys are a candidate structural material in nuclear fusion applications, where the presence of grain boundaries can improve mechanical properties and act as a sink for radiation- induced defects. Solutes with a thermodynamic preference to segregate to grain boundaries can stabilize them, making this a prime consideration for alloy design, but there are limited quantitative solute segregation data for vanadium. Based on results from an ab-initio computational framework for predicting the spectrum of grain boundary segregation energies across the periodic table, select nanocrystalline vanadium-based binary alloy systems were synthesized via mechanical alloying for targeted experiments characterizing differences in segregation strength. Scanning transmission electron microscopy and energy-dispersive x-ray spectroscopy measurements of solute concentrations in the grain boundary and bulk validate computational predictions of the average segregation strengths for different solutes, while showing inhomogeneous solute distributions along the grain boundary network that confirm the necessity for a spectral model that captures the behavior of site-specific segregation energies. After establishing the segregation behavior of different solutes in vanadium, the effects of solute segregation on other properties are examined. Heating experiments demonstrate that vanadium alloys containing strongly segregating species retain smaller grain sizes upon thermal annealing, indicating better grain boundary stability. The powder metallurgical route used produce these vanadium alloys requires a subsequent sintering step to densify powders into bulk parts for engineering applications, and dilatometry experiments reveal that that the addition of strongly segregating solutes also dramatically suppresses the sintering behavior. A kinetic analysis of the dilatometry data suggests that rapid grain boundary diffusion pathways that are necessary for effective sintering are obstructed by solute segregant, which has important repercussions for the processability of these alloys. Finally, microstructural characterization and nanohardness testing after ion-irradiation experiments demonstrate that the alloys with solute-stabilized grain boundaries are more resistant to nanovoid formation and radiation hardening. The work in this thesis advances our understanding of solute segregation and its effects in vanadium alloys, and highlights an approach for controlling grain boundaries that may facilitate future alloy design efforts for improved microstructural stability and radiation damage tolerance.

Modular Construction of Complex-Architected Bottlebrush Block Copolymers and Their Self-Assembly Behaviors

Sat, 01 Feb 2025 00:00:00 GMT

Modular Construction of Complex-Architected Bottlebrush Block Copolymers and Their Self-Assembly Behaviors Sun, Zehao Microphase-separated block copolymers are attractive materials for self-assembled nanolithography, yet there is a disconnect between the simple patterns commonly formed by block copolymers and the complex patterns required for many nanoscale applications, particularly in microelectronics. To meet this challenge, researchers have sought to design and build copolymer systems at ever-increasing levels of complexity in the (macro)molecular level, which promises to show emergent intriguing properties that are otherwise absent. However, the synthetic challenge as well as the vastly increased parameter space have obscured the systematic study of such complex systems. An efficient, modular synthetic route is thus highly desired for Lego-like molecular construction of property-decoupled, individually-tunable target materials. In this thesis, we will highlight the research endeavor in developing a multiblock Janus bottlebrush copolymer architecture as a novel platform for generation of diverse nanostructures that have been challenging to fabricate. The architecture, which features two orthogonal Janus domains, can be facilely constructed from corresponding building blocks by graft-through synthesis and can yield hierarchically engineerable phase-in-phase patterns. Surprisingly, the two constituent domains, though relatively independent of each other, behave significantly differently when combined together under certain circumstances. Their collective behavior gives rise to two low-symmetry mesh-like network phases (monoclinic and tetragonal respectively) that have not been observed in other soft materials before, which are of both fundamental and technological interest. Through a suite of experimental and computational study, we show that this peculiar phenomenon is an outcome of intrinsic molecular confinement, an emergent effect unique to multi-body, multi-hierarchy complex architectures. This work demonstrates that intrinsic molecular confinement is a viable path to bottom-up assembly of new geometrical phases of soft matter, extending the capabilities of block copolymer nanofabrication. As another example of modular synthesis, we will show an iterative polymerization methodology for controlled synthesis of bottlebrush copolymers with expanded compositional and architectural scope. When synergizing with other components, this strategy allows rapid access to functional materials that display different phase behavior when compared to the self-assembly of conventional copolymers. Our work introduced here is expected to facilitate the synthesis of complex functional copolymers, spark interest in the exploration of their property-function relationship, and enable more opportunities for their application in nanopatterning and other advanced materials.

Characterization, Processing, and Synthesis of Extreme-Performance Continuous Carbon Nanotube Network Composites

Sat, 01 Feb 2025 00:00:00 GMT

Characterization, Processing, and Synthesis of Extreme-Performance Continuous Carbon Nanotube Network Composites Durso, Michael Nathan Continuous carbon nanotube (CNT) networks are an emerging, hierarchically-structured, and commercially available nanomaterial built from countless CNT nanocrystals. These macroscopic yarn materials promise to bridge the gap between microscopic CNT fibers – which are well-known for their superlative material properties – and human-scale fiber reinforcements for extreme-performance composites. Yet because the constituent CNTs interact only via intermolecular forces, network properties fall short of their building blocks. Although these materials show promise as reinforcement in composites, the networks’ low-permeability and tortuous nanoporous structure renders imbibition with liquids like a polymer matrix or surface functionalizing agents challenging. Thus, traditional composite fabrication strategies can be ineffective when applied to CNT yarns, especially commercial products subject to proprietary microstructural manipulation. Using commercially-available CNT yarns fabricated through floating-catalyst chemical vapor deposition (FCCVD) as model systems, we first explore yarn characteristics which are unique to their hierarchical, bundled-fiber structure, placing focus on the oxygen-rich amorphous carbon phase found in pre-densified, chemically-stretched yarns. A green hydrothermal technique is explored to remove this phase from the surface level inward, allowing for purification and improved infiltrability. However, we find this phase is distinct from previously-reported amorphous carbons found in CNTs, showing it behaves as a matrix which may improve polymer bonding. An analysis of imbibition and fluid transport in these CNT yarns finds that while infiltration of low-viscosity liquids like water is thermodynamically-favored, it is limited when surpassing the threshold of capillary pore percolation. Nevertheless, infiltration in lower-density networks is not only observed, but exploited through the demonstration of dielectric heating in a microwave reactor, where we show fluid imbibed within the network can be boiled to induce swelling and exfoliation of CNT bundles (or conversely, this may be avoided) through optimization of the heating parameters and solvent. Next, with a firm understanding of the yarn networks’ properties and the impact of various processing effects, we demonstrate two techniques of producing polymer in-situ using dissolved monomers to side-step slow infiltration. The first technique is in-situ interfacial polymerization (ISIP), which is adapted to the yarns studied in this work to yield polyetherimide–CNT yarn composites. When applied to chemically-stretched yarn, specific strengths as high as 2.2 GPa/(g-cm3) are achieved in the flexible and durable yarn composite. We show parameters and conditions which maximize tensile properties and challenges associated with the rapid nature of the process, concluding with the successful demonstration of a roll-to-roll fabrication scheme for producing arbitrary amounts of polymer. In our second technique, we produce extreme-performance polyimide and polybenzimidazole composites through green in-situ polymerizations (ISSP) in CNTs and macroscopic fiber networks. This approach utilizes superheated water and alcohol as a powerful medium to disperse monomers and initiate polymerization of high-performance coatings within a porous network. We demonstrate ISSP-CNT composites with variable coating morphologies (conformal, shish-kebab, etc.), in-air stability to over 500°C, and doubled specific stiffness and specific strength. Finally, we validate the multifunctional behavior of polyimide-CNT composites by showing a strong, flexible composite can store energy and behave as a free-standing battery electrode.

Census-Based Population Autonomy for Marine Robots: Theory and Experiments

Thu, 01 May 2025 00:00:00 GMT

Census-Based Population Autonomy for Marine Robots: Theory and Experiments Paine, Tyler Collaborating groups of robots show promise due in their ability to complete missions more efficiently and with improved robustness, attributes that are particularly useful for systems operating in marine environments. A key issue is how to model, analyze, and design these multi-robot systems to realize the full benefits of collaboration even with limited communication, a challenging task since the domain of multi-robot autonomy encompasses both collective and individual behaviors. This thesis presents a layered model of multi-robot autonomy that uses the principle of census, or a weighted count of the inputs from neighbors, for collective decision-making coupled with multi-objective behavior optimization for individual decision-making. The census component is expressed as a nonlinear opinion dynamics model and the multi-objective behavior optimization is accomplished using interval programming. This model can be reduced to recover foundational algorithms in distributed optimization and control, while the full model enables new types of collective behaviors that are useful in real-world scenarios. To illustrate these points, a new method for distributed optimization of subgroup allocation is introduced where robots use a gradient descent algorithm to minimize portions of the cost functions that are locally known, while being influenced by the opinion states from neighbors to account for the unobservable costs. With this method the group can collectively use the information contained in the Hessian matrix of the total global cost. In addition, the critical issue of controlling subgroup size to minimize a collective cost signal is addressed, an initial step toward establishing a general definition of controllability of the nonlinear opinion dynamics model. The utility of this model is experimentally validated in three categorically different experiments with fleets of autonomous surface vehicles: an adaptive sampling scenario, a high value unit protection scenario, and a competitive game of capture the flag.

Experimental Quantification of the Phonon Drag Deformation Mechanism in Metals at Extreme Strain Rates

Sat, 01 Feb 2025 00:00:00 GMT

Experimental Quantification of the Phonon Drag Deformation Mechanism in Metals at Extreme Strain Rates Dowding, Ian Extreme strain rate deformations, above 10⁶ s⁻¹, are seen across many fields of science and engineering; from meteorite impacts and impact induced crystallographic phase changes to high-speed machining and additive manufacturing. Despite the range of applications, many common high-rate impact experiments are intrinsically limited to strain rates of only 10⁴ s⁻¹ before complicating the material deformation with a superimposing state of shock due to high impact pressures. However, recent advances in optically driven microballistics using laser induced projectile impact tests have provided a new quantitative look into extreme mechanics of materials, at rates above 106 s-1 and well below the onset of shock effects. As deformation strain rates increase, additional strengthening mechanisms in metals become available, leading to a change in the underlying physics of dislocation motion and an increase in strength. This thesis first explores the mechanical properties of pure metals when deformed at extreme strain rates − both in ambient conditions and elevated temperatures. Using an array of complimentary characterization methods, two independent measurements of strength, the dynamic strength and dynamic hardness, are assessed. As the temperature is increased from ambient, the strength and hardness of pure metals both increase an appreciable amount. At these deformation rates, conventional thermal softening effects are now in competition with anti-thermal hardening that arises from ballistic transport of dislocations from phonon interactions in the crystal lattice. These effects are quantified systematically and it is shown that the anomalous thermal strengthening seen is, thermodynamically and kinetically, the expected form of plasticity under these impact conditions. Next, the limits of where this anomalous thermal strengthening occur in metals are investigated. First, solute elements are added to pure Ni to evaluate how additional dislocation pinning mechanisms effect the strength at ambient and elevated temperatures during extreme strain rate deformations. The strengthen increase due to solute pinning of dislocations is additive to the other strengthening mechanisms, yet thermally controlled, which provides a transition from ballistic transport of dislocations to thermally activated strengthening at a critical concentration of solutes. Finally, the upper bound of temperature for dislocation phonon drag strengthening is assessed. While it was shown that pure metals increase strength with increasing temperature, this “hotter-is-stronger” trend breaks down as the temperature approaches the melting point of the metal. Using Sn, due to its low melting temperature, the breakdown from “hotter-is-stronger” to “hotter-is-softer” as the initial substrate temperature approaches the melting temperature is systematically explored.

Stairway to Autonomy: Hierarchical Decision-Making for LLM-Guided Planning, Bandit-Driven Exploration, and Multi-Agent Navigation

Thu, 01 May 2025 00:00:00 GMT

Stairway to Autonomy: Hierarchical Decision-Making for LLM-Guided Planning, Bandit-Driven Exploration, and Multi-Agent Navigation Nayak, Siddharth Nagar Autonomous multi-agent systems must efficiently plan, explore, and navigate in dynamic and unknown environments, particularly for tasks like search & rescue and environmental monitoring. These settings are often characterized by partial observability, limited communication, and dynamic objectives that require flexible coordination across agents. Designing autonomy that scales with team size and task complexity requires modular decision-making systems capable of high-level reasoning, information-driven exploration, and robust decentralized execution. This dissertation presents a hierarchical decision-making framework that addresses these challenges across three complementary levels of autonomy: high-level planning, adaptive exploration, and decentralized scalable navigation. At the highest level, LLaMAR (Language Model-based Long-Horizon Planner for Multi-Agent Robotics) leverages large language models (LLMs) to decompose long-horizon tasks into structured subtasks, enabling agents to adapt their strategies dynamically. However, the effective execution of these plans requires knowledge about the environment. Our mid-level exploration strategy, BaTMaN (Banditbased Tracking and Monitoring and Navigation), systematically prioritizes waypoints that maximize information gain while balancing real-world constraints such as energy efficiency and sensor reliability. Finally, InforMARL provides a scalable, decentralized navigation by leveraging graph-based local information aggregation, improving sample efficiency, and demonstrating transferability to unseen team sizes. This dissertation develops each of these modules to address a distinct level of the autonomy stack. LLaMAR functions as the high-level planner, translating natural language goals into structured sequences of subtasks and incorporating real-time corrections through a plan-act-correct-verify cycle. BaTMaN serves as the mid-level exploration engine, guiding sensor-equipped agents to prioritize informative regions based on uncertainty. InforMARL operates at the execution level, enabling decentralized agents to navigate through dynamic environments using graph-based local information aggregation and reactive control policies. Each module is independently deployable and optimized for different challenges: strategic reasoning, data-efficient monitoring, and scalable navigation, respectively. When combined, the three modules form a coherent autonomy stack for multi-agent systems operating under uncertainty.

Enabling End-to-End Sensitivity Analysis of Integrated Models

Thu, 01 May 2025 00:00:00 GMT

Enabling End-to-End Sensitivity Analysis of Integrated Models Davidson, Rosemary K. As space-based precision-pointed telescopes continue to grow in scale and complexity, integrated models are increasingly relied upon to inform early design decisions and support system-level verification. When ground testing of full-system configurations is infeasible, integrated models, including structural-thermal-optical performance models, are essential for predicting performance and validating requirements across multidisciplinary, coupled domains. In early design phases, when uncertainty is high and design decisions have long-term implications for cost and schedule, it is especially important to understand which uncertain parameters most influence system performance. Global sensitivity analysis can help identify dominant uncertainty sources and inform decisions about model reduction, testing priorities, and resource allocation. However, the computational cost of applying global sensitivity analysis to integrated models often exceeds available resources. The presence of cross-disciplinary coupling between subsystem models further complicates analysis efforts. Coupled and dependent variables obscure how specific inputs influence system-level performance, limiting the ability to reduce model dimensionality or focus testing efforts on individual subsystems. There is a need for integrated modeling methodologies that enable tractable global sensitivity analysis of large, feedforward-coupled systems while preserving the accuracy needed to support early-phase design. This thesis develops both exact and approximate methods for performing global sensitivity analysis on integrated models. A set of exact propagation techniques is introduced to compute end-to-end sensitivity indices when specific structural conditions are met, including functional linearity, non-interacting transforms, and monotonic intermediate mappings. These methods are evaluated using a suite of benchmark test cases that isolate when the exact sensitivity analysis method is valid and when structural assumptions begin to break down. A modular modeling framework is developed to compute exact or approximate end-to-end sensitivity indices and to enable automated mapping between disciplinary models in the integrated chain. The approach is also applied to a representative linearized structural-thermal-optical performance model, demonstrating how end-to-end global sensitivity analysis can be performed efficiently across thermal, structural, and optical subsystems. To extend tractable sensitivity analysis to black-box models, several approximate strategies are introduced, including multifidelity surrogate modeling and statistical regression. These methods support both forward uncertainty propagation and variance-based global sensitivity analysis for structurally complex integrated models, without requiring full-system evaluation at every iteration. Together, the exact and approximate strategies developed in this work provide a foundation for scalable end-to-end global sensitivity analysis in early-phase design, where identifying influential parameters and constraining model complexity are essential for evaluating candidate architectures and informing mission decisions.

Embedded Computing for Wavefront Control on Future Space Telescopes

Thu, 01 May 2025 00:00:00 GMT

Embedded Computing for Wavefront Control on Future Space Telescopes Belsten, Nicholas Future space telescopes will use adaptive optics to suppress starlight to directly image and characterize exoplanets. A measurement using this technique may be the first to detect extraterrestrial life in the universe. However, the real-time execution of adaptive optics control algorithms places unprecedented demands on spaceborne processors. Previous work has determined that processing limitations can degrade the achievable contrast and scientific yield of future exoplanet imaging missions. In this work, we quantify the relationship between adaptive optics processing needs and high contrast performance for the Habitable Worlds Observatory (HWO), a mission expected to launch in the 2040s and achieve the 10^-10 contrast necessary to image Earth-like planets around Sun-like stars. We survey the current landscape of high-order wavefront sensing and control (HOWFSC) algorithms for a future mission like HWO. We parameterize the compute requirements of multiple algorithms through analyses of computational patterns, benchmarks, and problem scaling. In parallel, we assess the capabilities of current and emerging spaceborne processors. We integrate these findings to model processor requirements across several dimensions of telescope design, and we predict whether various processor choices can meet the computational demands of specific HWO configurations. To validate our models, we implement HOWFSC algorithms on representative embedded processors and compare measured performance to predictions. These implementations also reduce risk for spaceflight by increasing the technology readiness level (TRL) of the algorithm–processor pairing to TRL 4. Given the significant uncertainty in HWO’s eventual design, we extend our deterministic models using Monte Carlo methods to evaluate system performance under uncertainty. We identify key sources of uncertainty and estimate the achievable contrast across a range of system configurations. Our results show that offloading computation to the ground is an important architectural option for most HWO designs. Even under optimistic assumptions, current space processors are insufficient to support the full range of HWO configurations. However, newly developed efficient algorithms substantially reduce the computational burden. Overall, we estimate that current technology has only a 40% probability of supporting HWO’s mission goals without additional architectural innovations. We conclude by recommending combinations of onboard computing, ground offloading, and optical design constraints to help close this technology gap as the mission design matures. In particular, we find that telescope stability and ground-in-the-loop performance are primary drivers of contrast performance, while algorithmic advances such as AD-EFC and onboard compute approaching ground-based GPU performance also provide significant benefits.

Accelerating Novel Energy Catalyst Discovery Using Automation, Active Learning, and AI

Wed, 01 May 2024 00:00:00 GMT

Accelerating Novel Energy Catalyst Discovery Using Automation, Active Learning, and AI Ren, Zhichu The discovery of novel energy catalysts is a critical challenge in the field of materials science. Traditional methods for materials discovery are labor-intensive and time-consuming, hindering the rapid development of new catalysts. To address this issue, we introduce a comprehensive approach that integrates automation, active learning, and artificial intelligence (AI) to accelerate the discovery process. Our approach introduces the Copilot for Real-world Experimental Scientist (CRESt) system, which combines a large multimodal model (LMM) with an active learning-guided robotic system. CRESt streamlines the workflow of composition selection, high-throughput materials synthesis, electrochemical screening and characterization for the optimization of high-entropy alloy catalysts. The system allows researchers, regardless of their programming skills, to interact with the robotic platform using voice commands, making it highly accessible and user-friendly. We demonstrate the effectiveness of our approach by experimentally exploring over 700 chemistries and 1300 samples. The optimized 8-dimensional alloy (Pd-Pt-Cu-Au-Ir-Ce-Nb-Cr) achieved approximately 10 times the cost-specific performance of commercial catalysts for the direct formate fuel cell. This breakthrough highlights the potential of our approach to accelerate the discovery of novel energy catalysts across various domains. Furthermore, we discuss the challenges and considerations associated with implementing active learning in real-world experiments. We provide guidance on addressing model-centric and data-centric issues, such as model customization and data irreproducibility, to ensure the successful application of active learning in materials research projects. Looking ahead, we explore the role of human experimentalists in the era of AI-driven discovery. While AI and automation are poised to transform many aspects of experimental research, we argue that human experimentalists remain irreplaceable for now. Our ability to exercise critical thinking and engage in complex real-world interactions sets us apart from abiotic intelligence. However, as AI becomes more deeply integrated into research practices, the experimental landscape is bound to undergo significant changes.

Exploring Atom-Light Scattering in the Quantum Regime

Thu, 01 May 2025 00:00:00 GMT

Exploring Atom-Light Scattering in the Quantum Regime Lu, Yu-Kun Ultracold atoms and molecules are promising platforms for exploring modern quantum science and technologies, such as quantum simulation and quantum computation. Here, light is the essential tool to manipulate and probe these systems. However, unlike in condensed matter systems where scattering experiments are routinely employed to characterize materials, ultracold atom and molecule systems are usually probed by imaging and not by light scattering. In this thesis, I present a systematic investigation of atom-light scattering under various scenarios. When atoms are confined in optical lattices, light scattering can be used to explore single-body, two-body, and many-body physics. Focusing on single-atom physics, I study coherent and incoherent light scattering of single-atom wavepackets and the relation to which-way information. For two atoms tightly localized to a 20nm size on the same lattice site, I demonstrate the strong electric dipolar interactions between them, which result in large momentum transfers and spectroscopic shift of the resonance. On the many-body side, I show how light scattering can reveal distinct quantum phases at thermal equilibrium or defect generation in dynamical ramps. For atoms released from the optical lattice, I demonstrate that light scattering can read out the quantum statistical information and initial density correlations hidden in the interference of atomic wavepackets. When atoms move freely in the form of degenerate quantum gases, I investigate how quantum statistics, phase transition, and interactions modify the atomic pair correlation and consequently the light scattering. For thermal gas at high density, I demonstrate nonlinear optical effects from high optical density and high scattering rate. Finally, I describe our recent efforts on manipulating atoms at subwavelength length scales. I discuss our attempts in optical tweezers and in optical lattices, and the prospect of observing magnetic pairing between two distant layers under attractive dipolar interaction. The techniques presented in this thesis should be of general use for pursuing quantum science and technology with ultracold atoms and molecules.

Initial segments in ordinal recursion theory.

Mon, 01 Jan 1979 00:00:00 GMT

Initial segments in ordinal recursion theory. Dorer, David John. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1979; Vita.; Bibliography: leaf 49.

Models for investigating the unreliability of freight shipments by rail.

Sat, 01 Jan 1972 00:00:00 GMT

Models for investigating the unreliability of freight shipments by rail. Folk, Joseph Frederick. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering, 1972; Vita.; Bibliography: leaves 279-284.

Boiling heat transfer in rotating channels with reference to gas turbine blade cooling

Sun, 01 Jan 1984 00:00:00 GMT

Boiling heat transfer in rotating channels with reference to gas turbine blade cooling Mudawar, Issam Abdallah. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1984; Includes bibliographical references.

Lentiviral Vector Engineering for High-Throughput Immune Profiling

Tue, 01 Feb 2022 00:00:00 GMT

Lentiviral Vector Engineering for High-Throughput Immune Profiling Dobson, Connor S. The ability to decipher immune recognition is critical to understanding a broad range of diseases, including cancer, infection, and autoimmunity, as well as for the development of countermeasures such as vaccines and immunotherapy. Efforts to do so have been hampered by a lack of technologies that are capable of scaling to simultaneously capture the complexity of the adaptive immune repertoire and the landscape of potential antigens. Each individual’s immune repertoire consists of tens of millions of unique receptors that are responsible for surveying the trillions of possible antigens that might be encountered in one’s lifetime. As a result, there has been intense focus on the development of tools for screening large antigen sets or large collections of potential immune receptors, but most of these capture complexity on only one side of the interaction. We have therefore used synthetic virology approaches to engineer a “lentivirus surface display” platform capable of screening complex antigen mixtures against the full complexity of the adaptive immune repertoire. In Chapter 2 of this thesis, we describe our molecular engineering approaches that enabled the development of VSVGmut, an efficient and modular targeted pseudotyping strategy. In Chapter 3, we leverage VSVGmut and further advances to enable one-pot library on library antigen identification screens for T cells by displaying antigens on the surface of lentiviruses and encoding their identity in the viral genome. Antigen-specific viral infection of cells allows readout of both antigen and receptor identities via single-cell sequencing. In Chapters 4 and 5, we extend our approaches to B cells and present preliminary data for applications in both cellular and humoral profiling. Taken together, our approaches represent a new class of tools for identifying the molecular targets of the adaptive immune response at scale.

Combustion Physics and Inverse Modeling of Energetic Materials

Wed, 01 May 2024 00:00:00 GMT

Combustion Physics and Inverse Modeling of Energetic Materials Kim, Suyong Energetic material combustion involves intricate multi-scale and multi-phase dynamics, where the interplay of chemical reaction and transport processes results in complex wave patterns across a wide range of length scales from nano to millimeter. Our limited fundamental understanding of these combustion processes poses a challenge to design and optimize combustion properties, leading to significant reliance on empirical knowledge. Deeper comprehension can be achieved by linking fundamental aspects of reaction and transport to combustion dynamics. However, there are very limited diagnostic tools available to quantify material properties and chemical kinetics for heterogeneous materials under combustion, which hinders the quantitative analysis of combustion waves. Furthermore, combustion wave dynamics and flame structures in modern nanocomposite energetic materials have not been fully resolved. This lack of breath in modeling techniques and experimental characterization has prevented quantitative analysis of combustion wave dynamics for energetic materials. This thesis aims to establish theories for combustion waves in energetic materials by correlating their intrinsic chemical reaction and transport properties with wave dynamics. To achieve this goal, two major steps are involved. First, we propose a novel inverse modeling approach to infer material properties and chemical kinetics using PDE-constrained optimization, which allows for deciphering the reaction-transport coupling from observable dynamics in currently available combustion diagnostic tools. We further discuss training challenges of neural differential equations with data subject to scale separation and propose mitigation strategies that enable learning stiff dynamical systems. Secondly, we investigate flame structures and dynamics in nanocomposite energetic materials at length scale ranging from micron to sub-millimeter using high-speed microscopic imaging techniques. Two distinct combustion wave patterns are characterized by flame dynamics and stability. Based on inverse modeling and microscopic observation, we finally construct two theories of combustion wave propagation and wave stability, by performing scaling analysis on wave dynamics in terms of mass and thermal transports and chemical reaction. A systematic view of energetic material combustion allows for deeper comprehension of how multi-scale dynamics of reaction and transports evolve in macro-scale combustion waves, potentially leading to the development of predictive models for the intricate heterogeneous combustion dynamics of energetic materials.

Computational methods for small-molecule transparent semiconductors

Sun, 01 May 2022 00:00:00 GMT

Computational methods for small-molecule transparent semiconductors Carter, Ki-Jana Solar energy has enormous potential to meet global energy demand in a renewable and environmentally sustainable manner. Although silicon-based photovoltaic (PV) devices have become significantly more affordable and accessible in recent decades, there is a need to develop alternative PV technologies which can be deployed more widely and cheaply. Visibly transparent PV devices based on organic semiconductors are well-suited to this role due to their ability to be installed on windows and building facades, their mechanical flexibility, and their high degree of tunability. However, in order for transparent PV to become commercially viable, further research is needed to shed light on the systematic tuning of the optical properties of molecular materials with visible transparency. This work applies computational tools — namely density functional theory (DFT) and graph neural networks — to gain a deeper understanding of how molecular structure impacts macroscopic optical properties and suggest directions for future study. In this work we employ linear-response time-dependent DFT with optimally tuned and screened range-separated hybrid functionals in order to compute accurate photoabsorption spectra with relatively low computational cost. Additionally, we utilize molecular graph neural networks as a means to leverage quantum mechanical datasets to accelerate the materials discovery process. These methods are combined to make progress on the optical design of organic semiconductors. This thesis document is organized as follows. Chapter 1 introduces transparent photovoltaics and the associated materials design considerations. Chapter 2 summarizes the computational methods employed in this work. Chapter 3 describes the first-principles modeling of small-molecule transparent absorbers using perylene dimide derivatives as a case study. Chapter 4 studies principles underlying the design of molecular graph neural networks. Chapter 5 applies these modeling techniques to construct a spectral dataset and train a scalable spectral model; screen a large dataset of organic molecules; identify physical trends and structure-property relationships; and suggest promising candidate materials for transparent photovoltaic applications.

Topology optimization of buildings-scale structures with material and fabrication constraints

Thu, 01 May 2025 00:00:00 GMT

Topology optimization of buildings-scale structures with material and fabrication constraints Jewett, Jackson L. The construction industry releases about 10% of anthropogenic Carbon Dioxide every year, primarily due to the manufacturing of construction materials. Structural optimization has been proposed as means of improving material efficiency in buildings, and thus reducing material demand for construction projects. Topology optimization has great potential for materially-efficient design because it is a free-form optimization method, allowing for performant geometries to be computationally derived with minimal input from the user. However, topology optimization algorithms must be modified to account for the specific fabrication and material constraints that are inherent in construction practices. This thesis shares a collection of research projects related to the use of topology optimization for large-scale structures relevant to the construction industry. First, a novel algorithm is proposed for large-scale 3D printed structures. The work focuses on the limitations presented by the printing nozzle, and the anisotropies that arise in 3D printed systems. Second, topology optimization is modified for design of structural glass. Several algorithms are developed, which are then used to design, fabricate, and test physical specimen to evaluate their real-world performance. Third, a framework is presented to design low-weight reinforced concrete structures. This system is used to design, build, and test reinforced concrete beams, so their performance can be compared to conventionally designed specimen. This thesis considers the diverse ways that topology optimization could be applied to design large-scale structures of various construction materials. The results demonstrate the types of computational techniques that can be used for generative design in the built environment.

Experimental Design in Operations

Thu, 01 May 2025 00:00:00 GMT

Experimental Design in Operations Wang, Chonghuan Experimental design has been fundamental to many fields, yet applications in operations research (OR) and operations management (OM) bring in complexities, as well as opportunities, that extend beyond classical statistical goals. This thesis discusses why OR/OM should care about experimentation and its design, where the challenges lie in operational and service systems for classical experimental design, and why OR/OM researchers are uniquely suited to address the challenges. More specifically, this thesis advances experimental design by introducing more operational perspectives, addressing two core challenges: incorporating operational objectives and leveraging operational modeling to enhance experimentation. First, traditional experimental approaches, such as A/B testing, primarily aim at statistical efficiency (e.g., reducing variance or bias). However, OR/OM applications frequently involve additional operational considerations, such as welfare preservation, revenue optimization, risk control, and non-stationarity. We investigate these settings in Chapters 2–4, developing frameworks for multi-objective experimental design. In Chapter 2, we introduce a minimax multi-objective optimization formulation to balance statistical power and welfare loss, derive necessary and sufficient conditions for Pareto optimal solutions, and propose robust multi-armed bandit designs. Chapter 3 extends this approach to pricing experiments, exploring trade-offs between estimating causal effects (price elasticity), maximizing revenue, and controlling tail risks, along with robust statistical inference methods. Chapter 4 addresses non-stationary experimental environments where treatment effects dynamically evolve, designing experiments that optimally balance accurate estimation of changing effects and welfare minimization. Furthermore, we highlight the substantial value of operational models—particularly Markov Decision Processes (MDPs)—in experimental design. In Chapter 5, we address the challenge of estimating long-term cumulative outcomes, such as customer lifetime value, using short-term experimental data. We develop novel inference methods grounded in MDPs, which effectively bridge short-term data to long-term outcomes. Moreover, by recognizing many real-world treatments tend to be localized for practical efficiency, we introduce novel estimators that leverage the localized structures to achieve substantial variance reductions. In summary, this thesis underscores how OR/OM contexts uniquely enrich experimental design, offering robust theoretical frameworks and practical solutions to operational challenges, ultimately broadening both the theoretical foundations and the practical impacts of experimentation.

Junctions and Strands: Breaking Property Tradeoffs in Polymer Networks and Composite Polymer Electrolytes

Thu, 01 May 2025 00:00:00 GMT

Junctions and Strands: Breaking Property Tradeoffs in Polymer Networks and Composite Polymer Electrolytes Herzog-Arbeitman, Abraham This dissertation first examines the mechanics of polymer networks, specifically material toughness and the nature of material fracture. Polymer networks, which include tire rubber, windmill turbine blades, tissue engineering scaffolds, polymer electrolytes (vide infra) and many other materials, possess a useful lifetime typically limited by a fracture event. Thus, methods of controlling toughness (the resistance of a material to tearing) without compromising composition or other properties would dramatically affect waste generation and energy use in the myriad applications in which polymer networks are employed. Toughness in rubbery polymer networks derives from the length and density of the polymer strands; thus, it is generally inversely related to stiffness, as captured in the classic Lake-Thomas theory. This inverse relationship has been perturbed through incorporation of forceresponsive molecules (mechanophores) that may either toughen or weaken the material depending on network construction and topology. The first part of this thesis identifies a new class of mechanophores called tetrafunctional cyclobutanes (TCBs), which can be used to either toughen or weaken a network of single topology without substantial change of network composition, even in dilute gels which are difficult to toughen by other methods. TCBs are then used to identify the mechanisms of mechanophore toughening or weakening in other networks, through a proposed topological metric called network strand continuity (NSC). We show that TCB substituents control the regio- and chemo-selectivity of the cyclobutane core under stress, and this molecular-level selectivity is responsible for network toughening or weakening on the macroscale. These effects can be predicted based on knowledge of activation energetics of the junction guided by NSC. Subsequently, effects of other network structure parameters on the magnitude of toughening or weakening are considered and the molecular design of second-generation highly active TCBs is described. The second part of this dissertation concerns the design of microporous polymer electrolytes and the applications of their composites and gels in batteries. Polymer electrolytes are a highly anticipated alternative to the liquid electrolytes currently in use, which are toxic, flammable, and incompatible with next-generation battery chemistries. Previous polymer electrolytes exhibit inadequate conductivity and a severe tradeoff between conductivity and mechanical properties. These challenges are accentuated in single-ion conductors, which are theorized to have the strongest rate capability. A new class of single-ion conducting polymer electrolytes that mimics the conduction mechanism of ceramic electrolytes to achieve strong mechanical properties, high conductivity, processability, stability, and recyclability is described. These polymers constitute the first regular microporous polyanions, and the most dissociative microporous polyanions to date. These polymers, alongside other rigid (but not microporous) polysulfonimides enable strong conductivity performance when coupled with suitable dopant (here succinonitrile) in low weight fractions. Low molecular weight controls flexible polymer analogs show inferior mechanical and conductivity properties. In fact, microporous composites outperform even liquid analogs. These composites show best-in-class combinations of mechanical and conductivity properties, and can even conduct divalent cations like Zn(II), a challenging but energy-dense battery metal. Simulations show that polymer-succinonitrile interactions enable fast conduction at the pore edge which results in synergistic behavior.

Abiotic and Biotic Polymer Degradation to Inform Sustainable Design

Thu, 01 May 2025 00:00:00 GMT

Abiotic and Biotic Polymer Degradation to Inform Sustainable Design Tantawi, Omar As global plastic production continues to rise, understanding environmental processes governing plastic degradation is crucial to inform the sustainable design of polymers. This thesis is structured into three chapters, each addressing critical aspects of polymer degradation: In the first chapter, I develop and apply a sequential abiotic (photodegradation and hydrolysis) and biotic degradation test to a diverse suite of 18 polymers, including novel polyhydroxyalkanoates polyesters, commercially available bio-based polymers (e.g., polylactic acid, poly-3-hydroxybutyrate), and conventional fossil-derived polymers (e.g., polypropylene, polyethylene terephthalate). Results illustrate that current biodegradation standard methods relying only on mineralization underestimate polymer degradation by up to two-fold. Simulated sunlight notably enhanced polymer degradation by mobilizing dissolved organic carbon (DOC), which proved highly biodegradable in marine environment. Chemical structural differences were clearly linked to degradation behaviors, emphasizing the utility of the developed workflow for rapidly identifying environmentally relevant degradation mechanisms, which can inform structure-property relationships for future polymer designs. In the second chapter, I delve deeper into characterizing polymer-derived dissolved degradation products. Conducting Mass Remainder Analysis (MARA) using non-target liquid chromatography–high-resolution mass spectrometry (LC-HRMS) data, we systematically identified oligomeric degradation products and homologous series of polyamide-6 (PA6), polycaprolactone (PCL), and polylactic acid (PLA). Complementary experimental approaches (retention-time shifts across varied mobile phase pH, fragmentation analysis, and spectral matching) were essential to improve structure elucidation and determine acid-base properties (pKa) and hydrophobicity (logKow and logD). The experimental findings emphasized large deviations of oligomers hydrophobicity from computational predictions, underscoring the necessity for oligomer-specific experimental data to enhance environmental fate modeling and risk assessment accuracy. In the third chapter, I investigate the fate of polymer-derived dissolved organic carbon (p-DOC) from PLA, PCL and PA6, focusing specifically on oligomer chemistry. Using natural marine microbial communities, PLA- and PCL-derived DOC demonstrated rapid biodegradation (82-85% within six days), while PA6-derived DOC exhibited resistance. Detailed analysis using high-resolution mass spectrometry and MARA revealed significant chemical structure dependence in biodegradation rates, with rapid degradation of aliphatic ester-containing cyclic and linear oligomers. Larger cyclic oligomers degraded faster, while short linear oligomers showed transient accumulation followed by degradation. PA6 oligomers exhibited limited biodegradability, with cyclic oligomers showing minimal degradation. The results emphasize the critical influence of oligomer chemistry and microbial enzymatic specificity, providing essential insights for designing sustainable polymers compatible with marine environments.

Hyperspectral Remote Sensing for UXO Detection and Damage Assessment on Airfield Pavements

Thu, 01 May 2025 00:00:00 GMT

Hyperspectral Remote Sensing for UXO Detection and Damage Assessment on Airfield Pavements Pietersen, Randall If an airfield being operated by the U.S. Air Force is attacked, the current method for assessing its condition is a slow visual and manual inspection process, exposing personnel to dangerous conditions and delaying repair operations. Developing a fully autonomous remote assessment solution would improve the speed and safety of this critical task, but remains an unsolved problem despite continued advances in drone technology, deep learning, and computer vision. This research explores using near-surface hyperspectral sensors as an alternative to red, green, blue (RGB) digital cameras, in hopes of improving detection precision and accuracy for airfield assessment. However, even with modern hyperspectral sensors the benefit of increasing spectral image resolution comes at a cost, creating addition complexity, uncertainty, and sensitivity in the acquisition, data correction, and downstream detection processes. This work presents a series of tests, each designed to better understand and refine a full hyperspectral image detection sequence, starting with sensor selection and raw data acquisition, proceeding to radiometric correction, and culminating in image recognition by means of supervised deep learning (DL). Regarding sensor selection and data acquisition, these findings indicate that for many applications of computer vision, using a hyperspectral camera with high spectral resolution is unnecessary. It is more beneficial to select a camera with snapshot imaging that instead maximizes spectral range or spatial resolution. Radiometric correction is then explored, and experiments demonstrate that correction makes machine learning classification models less sensitive to changes in scene illumination, thus improving overall image recognition performance. Finally, deep learning models for image recognition are tested and a new method for generating synthetic hyperspectral data is developed and shown to be useful for estimating hyperspectral model performance on larger datasets, when real data are limited. Overall, the findings presented in this thesis suggest that by refining the methods used for data acquisition, correction, and detection, hyperspectral imaging improves image recognition when compared to traditional RGB cameras. This applies not only for airfield damage assessment but extends to other real-world applications requiring computer vision and scene understanding.

New Measurement Approaches to the Study of Secondary Organic Aerosol

Thu, 01 May 2025 00:00:00 GMT

New Measurement Approaches to the Study of Secondary Organic Aerosol Helstrom, Erik Aerosol particles constitute a class of atmospheric pollutants that are detrimental to human health and influence the Earth’s climate. A significant fraction of aerosol mass is composed of organic material, produced by photochemical reactions of organic trace gases which form secondary organic aerosol (SOA). However, the large diversity of volatile organic compounds (VOCs) makes it challenging to identify all of the chemical reactions contributing to SOA formation. In addition to this chemical complexity, our ability to identify and measure all of the relevant organic compounds, especially species present in aerosol particles, is limited by challenges in efficiently sampling and detecting the various classes of molecules formed. Improving knowledge of the chemical behavior of aerosol will improve our ability to predict how changing emissions and chemical conditions will impact the formation and properties of particulate matter in the future. This thesis will explore recent improvements in instrumentation and measurement techniques and apply them to laboratory studies of organic carbon and SOA. First, we adapt a technique for measuring total suspended carbon to laboratory chamber experiments, converting organic compounds with high temperature catalysis to carbon dioxide, which is then monitored in real time. This allows for a “top-down” constraint on the overall concentration of all organic species (including SOA) as experiments proceed, as some lower volatility products are lost to the surfaces of the laboratory chamber. Second, we compare the measurements of SOA from three chemical ionization mass spectrometers using different ionization and desorption methods to detect particle-phase species. Clear differences emerge in the detected formulas across instruments, highlighting variations in chemical sensitivities to different classes of compounds and the influence of fragmentation on the detected products. Finally, we explore how changing peroxy radical fate influences SOA formation by monitoring SOA composition with extractive electrospray ionization (EESI) mass spectrometry. Differences in particle-phase products, particularly nitrates, hydroperoxides, and dimers, make the dependence of initial SOA composition on peroxy radical pathways clear. Over time, we observe a convergence of SOA spectra formed under different peroxy radical regimes, suggesting the influence of secondary products and particle-phase chemistry, though some differences persist from the initial gas-phase peroxy radical fate. Overall, this thesis demonstrates improved tools for constraining and investigating VOC oxidation pathways leading to particle-phase organic species.

Computational methods for dissecting multicellular mechanisms of complex diseases

Thu, 01 May 2025 00:00:00 GMT

Computational methods for dissecting multicellular mechanisms of complex diseases Mitchel, Jonathan Single-cell genomics technologies have enabled unbiased characterization of cell types and cellular states. However, the high-dimensional nature of this data necessitates computational and statistical methods to uncover the biological processes that shape it. In my thesis research, I developed three computational methods to explore genetic regulatory mechanisms underlying common diseases and the resulting multicellular patterns of dysfunction. In the first project, I developed a method called scITD to investigate how cellular processes across distinct cell types coordinate in disease contexts. scITD identifies sets of genes in one or more cell types that co-vary together across biological samples. Through the application of this tool to various immune-cell datasets, we uncovered highly reproducible gene expression patterns associated with autoimmune patient phenotypes. In the second project, I characterized technical artifacts prevalent in imaging-based spatial transcriptomics data. These artifacts arise from the misassignment of transcript molecules to incorrect cells. I further demonstrated how these artifacts confound downstream analyses, including differential expression and cell-cell interaction inference. To address this, I jointly developed a correction method that mitigates these artifacts, thereby uncovering novel biological insights in cancer datasets. In the third project, I introduced a computational method to unravel the mechanisms of genetic variants identified from genome-wide association study loci. This method tests whether these same genetic variants also underly changes to gene expression in specific cell types or states. Applying this tool to autoimmune and neurodegenerative datasets uncovered new SNP-gene-phenotype links and localized their effects to specific cell populations, helping to refine our understanding of these pathologies.

Scalable Packaging and Integration Solutions for Next-Generation Photonic Systems

Thu, 01 May 2025 00:00:00 GMT

Scalable Packaging and Integration Solutions for Next-Generation Photonic Systems Ranno, Luigi The ever-increasing demand for faster and more efficient computation has propelled the rapid growth of integrated photonics, with commercial products starting to reach global markets in recent years. Nevertheless, integrated photonics still lacks the scale required to meet market demands and falls short of the performance targets necessary for many critical applications. Innovative solutions are imperative if photonics is to drive technological advancement and become a ubiquitous part of next-generation systems, rather than being confined to niche or high-end applications. Among the key bottlenecks is photonics packaging, which refers to the challenge of electrically, optically, and thermally interfacing with a photonic integrated circuit (PIC). Current packaging solutions often impose significant design tradeoffs, contributing to industry fragmentation and high costs. Two-photon lithography (TPL), a high-resolution 3D manufacturing technique, has emerged as a promising enabler of robust and efficient optical interconnects. However, existing research has focused heavily on performance, often relying on additional chip processing steps (e.g., cladding removal) that hinder scalability. Moreover, prior work largely restricts itself to parameterized geometries, such as quadratic curves or spherical sections, that underutilize the true design freedom of TPL. My work addresses both of these limitations. I developed a freeform, facet-attached micro-reflector solution that is fully compatible with standard foundry processes, adaptable to challenging coupling scenarios, and computationally efficient to design. This coupling solution demonstrates all the properties desired in an ideal optical interface: low insertion loss (~0.6 dB), wide bandwidth (>300 nm), foundry compatibility, and geometric universality across PIC platforms. Another major challenge facing the photonics industry is the lack of critical functionalities within current foundry processes due to limited material availability. Significant gains in performance and capability can be realized by integrating new materials on-chip, but doing so while maintaining CMOS-foundry compatibility remains a formidable task. To address this, I helped develop a novel photonics platform enabling substrate-inverted multi-material integration. This platform supports seamless integration of diverse materials while leveraging existing PIC process stacks, including metallization layers, unlocking new classes of high-performance devices. Building on this idea, I further demonstrated how material integration can directly enable new applications. Specifically, I developed a selective and ultra-sensitive environmental lead (Pb²⁺) sensor, based on a crown ether functionalization layer. This device showcases the potential of hybrid material platforms to deliver practical, field-relevant solutions in environmental monitoring and beyond.

Synthetic and Post-Synthetic Methods towards Fine Tuning the Chemical and Physical Properties of Metal-Organic Frameworks

Thu, 01 May 2025 00:00:00 GMT

Synthetic and Post-Synthetic Methods towards Fine Tuning the Chemical and Physical Properties of Metal-Organic Frameworks Iliescu, Andrei This thesis explores synthetic and post-synthetic strategies for tailoring the chemical and physical properties of metal-organic frameworks (MOFs), with a particular emphasis on modulating redox activity, framework composition, and ionic conductivity. The first part of the work focuses on leveraging MOF-embedded polynuclear metal clusters for multi-electron redox chemistry. A square-planar tetramanganese cluster was shown to reversibly interconvert between molecular oxygen and metal-oxo species via a four-electron pathway. This reactivity was then investigated by varying the identity and redox potential of the metal centers within the tetrametal cluster. The Fe(II) and Co(II) analogs reveal distinct metal-specific behavior and provide insight into the tunability of redox-active SBUs within MOFs. Next, post-synthetic cation exchange was employed to access a previously unreported Zn-based MOF, ZnZnBTT, which exhibits significant Zn-ion conductivity due to mobile charge-balancing cations. This material demonstrates the potential of MOFs in next-generation solid-state battery technologies. Finally, the impact of linker electron donicity on cluster structure and reactivity was explored using a new mixed-azolate ligand. Four isostructural MOFs incorporating Co, Ni, Cu, and Cd were synthesized, revealing that the electron-rich pyrazolate groups modulate cluster composition and redox behavior. Notably, CoBTDP exhibits O₂ reactivity, unlike its all-tetrazolate counterpart, underscoring the role of linker design in tuning MOF function. Together, these studies demonstrate how careful control over MOF synthesis and post-synthetic modification can be used to fine-tune redox behavior, framework composition, and ion transport, providing new avenues for the design of functional porous materials.

Leveraging Metal Complexes for Optical Read-out of Magnetic Fields

Thu, 01 May 2025 00:00:00 GMT

Leveraging Metal Complexes for Optical Read-out of Magnetic Fields Yi, Seungyeon Optical detection of magnetic phenomena offers a compelling pathway toward the development of highly sensitive and versatile molecular sensors. This thesis investigates the design of metal complexes tailored for magnetic field read-out through light–matter interactions, focusing on two strategies. The first section explores magnetochiral dichroism (MChD), an optical effect that emerges from the interplay between molecular chirality and magnetism. By systematically varying the metal centers within a series of chiral lanthanide complexes—specifically, Tb³⁺ and Dy³⁺—we examine how differences in magnetic moment modulate the MChD response. This comparative study reveals fundamental chemical design principles for enhancing MChD intensity and deepens our understanding of how structural and electronic factors jointly shape this directional optical effect. The second section addresses the challenge of engineering optically addressable molecular qubits based on Ni²⁺ complexes. Realizing effective spin-state read-out in these systems requires precise control over both magnetic and photophysical properties. To this end, we investigate ligand modification strategies aimed at enhancing luminescence while preserving an S = 1 ground state suitable for quantum applications. Collectively, we hope these studies contribute to a better understanding of the design space for spin–photon coupled molecular systems, offering new tools for magnetooptical sensing.

Quantifying Plasticity and Temperature in High Velocity Microparticle Impacts

Thu, 01 May 2025 00:00:00 GMT

Quantifying Plasticity and Temperature in High Velocity Microparticle Impacts Lucas, Tyler J. The Laser Induced Particle Impact Test, or LIPIT, is a benchtop experimental setup that enables in-situ observation of micron-scale particles impacting targets at velocities ~10-1500 m/s. Through a combination of the high velocity and small length-scale of the impact, strain rates exceeding 10⁷ /s can be achieved while maintaining a subsonic plastic wave, preventing formation of strong shockwaves and hydrodynamic behavior. The LIPIT has been effectively applied to study phenomena in mechanical behavior, cold spray, and astronomical impacts, all of which will be further investigated in this work. This thesis combines LIPIT experiments and finite element modeling to explore the dynamic behavior of pure metals in the unique regime of strain, strain rate, and pressure achieved in high velocity impact conditions. First, the effect of material microstructure on the mechanical behavior of copper at high strain rates is explored to improve the capability of constitutive strength models in accurately representing experiments. Next, a method is introduced to measure the dynamic yield strength of ductile microparticles, effectively removing the need for the tacit assumption that the properties of bulk materials can be imposed on powders despite differences in processing. The understanding of microstructure and particle behavior are then combined to study the influence of material microstructure on the solid-state bonding of copper particles to copper substrates of different temper. Finally, this work applies the new understanding of plasticity and dynamic modeling in high strain rate conditions to quantitatively study the behavior of metals with a phase transition in absence of strong shockwaves.

Endothelial cell plasticity as a marker of vascular disease and predictor of adverse outcomes to stress

Thu, 01 May 2025 00:00:00 GMT

Endothelial cell plasticity as a marker of vascular disease and predictor of adverse outcomes to stress Salazar Martín, Antonio Gabino Endothelial cells (ECs) dynamically sense and adapt to their local biomechanical and biochemical environments, a process crucial for maintaining vascular homeostasis. Loss of this plasticity is implicated in vascular diseases, where endothelial dysfunction and maladaptive responses exacerbate disease progression and limit therapeutic efficacy. We investigated the role of endothelial cell plasticity under pathophysiological conditions and its impact on therapeutic interventions – mechanical, pharmacologic and genetic. Specifically, Aim 1 characterizes the modulation of EC plasticity by shear stress, revealing that flow patterns drive distinct transcriptional signatures and subpopulation behaviors, as demonstrated through singlecell transcriptomics in human aortic endothelial cells. Aim 2 examines the interplay between EC dynamism and antiproliferative drugs, in particular rapamycin and paclitaxel – the agents released from drug-eluting stents, showing that biomechanical cues from flow dominate EC responses, potentially limiting drug efficacy in regions of disturbed flow. Aim 3, extends the investigation by moving beyond the overwhelming of cells with pharmacologic dosing into the domain of controlled genetic modification, which is in concert with the direction of modern therapeutics and also provides a further dimension to the perspective of endothelial biology. We sought to discern if genetically modified cells maintain their characteristic "endothelial" profile or if the interplay among genomic alterations, transcriptional and proteomic shifts, and environmental cues leads to a state that challenges the hypothesis that ECs remain plastic until they become committed to flow. The integration of single-cell transcriptomics and in vivo models provides novel insights into the heterogeneity of endothelial responses and underscores the importance of considering biomechanical and biological factors in developing targeted therapeutic strategies for vascular diseases.

Order Under Pressure: Structural and Magnetic Characterization at Extreme Stresses

Thu, 01 May 2025 00:00:00 GMT

Order Under Pressure: Structural and Magnetic Characterization at Extreme Stresses Riesel, Eric Alan Mechanical stress is an exquisitely versatile tool for controlling chemical bonding. This multi-dimensional synthetic lever tunes the electronic structure of elements and changes the way that atoms arrange and coordinate to one another. These unique electronic configurations and coordination environments have profound impacts on the properties of materials giving rise to functionality ranging from high-temperature superconductivity to diverse magnetism. Despite over a century of research on solid-state materials over one gigapascal (GPa), experimental and theoretical obstacles remain for structural and physical characterization of complex phases which only persist at these conditions. We begin to address the wide-reaching challenge of structural characterization in complex, bulky sample environments by employing recent advancements in generative artificial intelligence to develop a generalized approach to solving the structure of crystalline solid-state materials. We demonstrate that our model achieves a 42% match rate on a curated set of experimental powder diffraction patterns, and we then use our model to solve the structure of several previously unsolved structures at high pressure. We proceed to focus on a different structural characterization problem: defects which arise exclusively under mechanical stress. We demonstrate that site-disorder is unlikely to occur at room temperature and high pressure in InBi and instead propose a set of defects which explain the X-ray spectra and scattering patterns equally well. Progressing to properties characterization and magnetic ordering at high pressure, we experimentally demonstrate that MnBi2, a compound which does not persist to ambient pressure, is a permanent magnet. Comparing the orbital and spin contributions to the total moment across compounds in the Mn–Bi system, we build up design principles for permanent magnets using heavy main-group elements. The combination of our work in structural and physical characterization at extreme stresses charts a path towards the discovery of functional high-pressure bulk materials and defects.

Coherent Dynamic Nuclear Polarization: Mechanistic Insights and Experimental Advances

Thu, 01 May 2025 00:00:00 GMT

Coherent Dynamic Nuclear Polarization: Mechanistic Insights and Experimental Advances Ouyang, Yifu Dynamic Nuclear Polarization (DNP) enhances the sensitivity of solid-state Nuclear Magnetic Resonance (NMR) by transferring polarization from electrons to nuclei. While traditional continuous-wave (CW) DNP has advanced through improved radical design, the development of pulsed DNP—employing short, high-power microwave bursts—has shown the advantages of coherent spin control. We present both theoretical and experimental investigations aimed at understanding and optimizing polarization transfer. On the theoretical side, we examined multiple DNP mechanisms, including a re-evaluation of the Overhauser effect in insulating solids and a foundational treatment of the chirped solid effect. We also identified a new transfer channel, termed Resonant Mixing, arising from interference effects under off-resonance driving. Building on these insights, we developed a general framework for analyzing amplitude-, phase-, and frequency-modulated pulses. This approach enables the design of hybrid pulse sequences that combine modulation and chirping to produce efficient, selective spin transfer. These sequences maintain high enhancement even at reduced microwave power, thereby improving scalability to high magnetic fields. To test the practical viability of this approach, we designed and evaluated a prototype 400 MHz/263 GHz probe incorporating new resonator and RF technologies. While the initial performance was limited, the system provided a testbed for future high-field pulsed DNP experiments under realistic conditions. Together, these results establish a theoretical and technical foundation for next-generation pulsed DNP, emphasizing coherent spin manipulation, power-efficient design, and applicability to high-field, static-solid NMR systems.

Development of Quantitative Solid-State NMR Methods to Characterize Membrane Proteins

Thu, 01 May 2025 00:00:00 GMT

Development of Quantitative Solid-State NMR Methods to Characterize Membrane Proteins Somberg, Noah H. Membrane proteins are critical components of all cells and viruses. While about one quarter of human proteins are membrane proteins, they constitute half of all drug targets. Despite their importance, membrane proteins are underrepresented among known protein structures, constituting only about 2 percent of the Protein Data Bank. This discrepancy is due to the unique difficulties in studying membrane proteins, which make many techniques commonly used in structural biology extremely challenging. Membrane proteins often display a structural dependence on the local environment. It is therefore essential to have structural biology tools to study these critical proteins in native-like environments. Solid-state Nuclear Magnetic Resonance (NMR) spectroscopy provides one of the few methods available to study the structure and dynamics of membrane proteins directly in the lipid bilayer. Herein, practical and theoretical considerations of dipolar and chemical shift anisotropy recoupling experiments are presented. These experiments were applied to the study of membrane proteins. New experiments and novel analysis techniques were developed, and the results guided biophysical understanding and drug development. Among the membrane proteins of SARS-CoV-2, the Envelope (E) protein is the least understood. E forms a membrane-bound ion channel and is associated with inducing the respiratory symptoms of the disease. The exact oligomeric state of E was not known. The fluorine centerband-only detection of exchange (CODEX) experiment was employed to directly measure the oligomeric state of E in lipid bilayers. The transmembrane domain of E forms a pentamer, while a construct including the ectodomain forms a dimer. Under certain conditions, the pentamers cluster together, forming supramolecular assemblies that may have a unique role in the virus life cycle. New sensitivity-enhanced carbon-fluorine rotational-echo double-resonance (REDOR) experiments are developed and used to investigate the drug binding of E. The small molecule drug hexamethylene amiloride binds to E at the protein-lipid interface. This informed the development of higher affinity inhibitors, which were also shown to bind E at the lipid interface. A novel strategy to identify ligand binding sites of proteins without sequential resonance assignment is presented. The technique uses a computationally efficient second moment approximation to calculate REDOR dephasing, and simulated annealing to explore the associated parameter space. The new methods and advances in quantification and simulation of the REDOR and CODEX experiments enhance the available solid-state NMR toolkit for the study of critical membrane proteins.

Improving the reliability of optical phase change materials-based devices

Thu, 01 May 2025 00:00:00 GMT

Improving the reliability of optical phase change materials-based devices Popescu, Cosmin-Constantin Optical components are part of our daily lives, including vision and camera systems, data transmission in telecommunication, sensing applications, manufacturing and medicine, and more. Compact on-chip integrated optics such as photonic integrated circuits and optical metasurfaces can provide us with the desired functionality, but there is a continuous need for active non-volatile tuning capabilities of these devices. Chalcogenide optical phase change materials (PCM) (e.g. Ge₂Sb₂Te₅) have gathered sustained interest in the past several years in the photonics community exactly due to their potential for non-volatile control of optical signals. Prior work had showcased the integration of PCMs via free-space metal heaters for metasurfaces, demonstrating switching for several tens of cycles. To understand the limiting mechanisms preventing extended cycling of such devices, we have developed a near IR transparent platform on doped silicon-on-insulator for testing both material behavior and device performance, along with the auxiliary code and design needed for such testing. Using this platform, a Ge₂Sb₂Se₄Te-based transmissive metasurface filter was demonstrated with a cycling performance of 1250 cycles. Following, the mechanisms limiting the performance of such devices were explored, providing guidelines to improve their reliability and endurance both at the phase change material scale as well as the accompanying device level. Furthermore, we showcase future potential devices that can be leveraged for PCM photonics, including a theoretical design that avoids free carrier absorption losses from the doped silicon heater by placing the dopants at the node of a resonant mode, limiting their overlap with the regions of high field amplitude, and a matrix array of heaters for higher device functionality. Finally, we point towards areas to focus in order to scale these concepts to commercial applications.

Precision Medicine in Diabetes Using Continuous Glucose Monitoring

Thu, 01 May 2025 00:00:00 GMT

Precision Medicine in Diabetes Using Continuous Glucose Monitoring Healey, Elizabeth Diabetes affects millions of individuals around the world and is a leading cause of death. The risk of serious long-term complications in diabetes can be mitigated through early interventions in the form of medication and behavioral changes. However, the pathophysiology of diabetes and the course of the disease are markedly heterogeneous, making it essential that disease management is tailored to the individual. Continuous glucose monitoring (CGM) helps patients manage their disease through the collection of real-time measurements of interstitial glucose, providing insight into glycemic dynamics that laboratory measurements cannot capture. In this thesis, we investigate how CGM can be used to enable personalized disease management in diabetes using modern methods from machine learning and signal processing. We first investigate a model-based approach to estimate metabolic parameters from CGM data. We show that parameters estimated from daily CGM data correlate with parameters derived from in-clinic laboratory measurements. Then, we explore how the rapidly emerging field of generative artificial intelligence can be integrated into diabetes care through analysis of CGM data. We show how large language model agents hold promising potential to assist patients and clinicians in managing diabetes through the synthesis and narrative summarization of large amounts of CGM data. Finally, we leverage observational CGM data to understand heterogeneity in type 2 diabetes. The work in this thesis shows how modern computational methods in machine learning can enable precision medicine in diabetes by leveraging wearable CGM data for improved disease management.

Structural Insights into Mycobacteriales Galactan Biosynthesis

Thu, 01 May 2025 00:00:00 GMT

Structural Insights into Mycobacteriales Galactan Biosynthesis Carter, Alan Wylde The order Mycobacteriales includes a number of severe human pathogens, including Mycobacterium tuberculosis, the causative agent of tuberculosis and a leading cause of infectious disease-related mortality worldwide. The unique cell wall structure of these bacteria is essential for their viability, and has been studied as a potential target for novel therapeutics development. A key component of the mycobacterial cell wall is the galactan, a 30-40 residue linear polysaccharide of galactofuranose (Galf) with an alternating β(1,5) and β(1,6) linkage pattern, synthesized by the polymerase Galactofuranosyl Transferase 2 (GlfT2). While GlfT2 has been established as a processive polymerase with intrinsic sequence control, the mechanism underlying this activity remains unclear. In the studies presented here, we provide structural insights into Nocardia brasiliensis GlfT2 (NbrGlfT2) using X-ray crystallography and cryo-electron microscopy. We characterize both the acceptor-bound and membrane-embedded structures of NbrGlfT2 and propose three models for its catalysis: Processive Galactan Sliding, Feedback-Regulated Sequence Control, and Membrane Curvature-Mediated Polymerization. Furthermore, we structurally characterize a previously undescribed GlfT2 paralog from Rhodococcus equi, which we term ReqGlfT3. We confirm its galactofuranosyl transferase activity and identify the production of β(1,3) and β(1,5) linkages. These findings offer new insights into GlfT2 and related polymerizing glycosyltransferases, which will provide insights into enzymatic regioselectivity mechanisms and polysaccharide biosynthesis across the bacterial kingdom.

Making Energy Work: Enacting Renewable Transitions in the Deserts of Chile and California

Thu, 01 May 2025 00:00:00 GMT

Making Energy Work: Enacting Renewable Transitions in the Deserts of Chile and California White-Nockleby, Caroline Celeste This dissertation explores how different people enact and engage the “Energy Transition,” a temporal orientation increasingly used to describe a range of activities that operate on and through electricity, renewable energy, and fossil fuels. I ask, given the infrastructural, political, economic, and material-semiotic continuities and imbrications between renewable energies and fossil fuels, how do actors craft, stabilize, and mobilize the [just] renewable energy transition? How, in other words, do people distinguish the activities and projects of the energy transition from continuity and more ordinary change? I also ask, what kind of political work is “transition” – along with its usual modifiers, energy, renewability, and justice – doing in the world? Building on scholarship that explores the history, genealogy, materiality, and political economy of energies and resources, I investigate these questions by analyzing energy transition projects across two region-scale field sites: Antofagasta, Chile and Imperial County, California. I find that self-consciously small-scale technologies like maps, models, and pilot projects are vital to assembling just, renewable resources – and to demarcating particular places and projects as in transition. Though these technologies often aspire to make and move green energy 24/7 and worldwide, they face substantial obstacles to doing so. Their value is, thus, often drawn more from the future they index than their present functionality. I term these temporal indexical technologies “anticipatory devices,” and show that such devices gain significance in relation to the particular forms of expertise that actors draw on to design them. Each chapter, therefore, analyzes a different disciplinary form of expertise in which the concepts of renewability, justice, transition, and energy, aided by various anticipatory devices, take shape: cartography (Chapters 1 and 2), chemistry (Chapter 3), engineering (Chapter 4), and economics (Chapter 5). Ultimately, I find that energy transition is often treated as a universalizing, singular narrative, which can shape and limit the scope of climate mitigation projects. Profit motives often incentivize corporate actors to design projects to align with the more temporary kinds of transitions that have long been constitutive of capitalism, even though it is longer-term changes that will most effectively mitigate climate change. The same is true for renewability, which can easily be articulated as an ideal that supports visions for unfettered capitalist growth. Moreover, approaches that treat “transition” as universal can also easily echo and reinvigorate an evolutionist approach to time, in which places and countries compare their relative advancement towards carbon neutrality along a single, teleological temporal axis. Yet I also encountered many actors engaged in more situated projects that attended to local histories of land use, industry, and power. These projects pluralized transition – or did not use the term at all – offering situated and distributive visions of energetic change that might enable more regenerative futures to germinate.

Containerless measurement and thermodynamic prediction of the physical properties of liquid steels

Thu, 01 May 2025 00:00:00 GMT

Containerless measurement and thermodynamic prediction of the physical properties of liquid steels Benderly-Kremen, Ethan Incomplete control over macrosegregation during steel solidification hinders the development of novel steel alloys and applications by limiting compositions which can be successfully cast. Analysis of macrosegregation at the solidification front is aided by study of the liquid state via fluid mechanics, which can place bounds on when macrosegregation can occur. This non-dimensional analysis requires knowledge of the physical properties of the liquid steel: density, surface tension, viscosity- and how they change with composition as solutes are rejected from solid at the solidification front. Macrosegregation is most pronounced in ferrous liquids containing light, non-metallic species, i.e. carbon, oxygen, and sulfur. Yet, existing literature models for predicting the physical properties of liquid alloys are incapable of accounting for these interstitial species inside an iron lattice. Additionally, direct experimental measurement of these properties is hindered by the requisite high temperature, high reactivity of the melt, and the vast composition space of steel alloys. Herein, both the experimental and modelling challenges are introduced and addressed. An experimental technique using a floating zone furnace, pendant drop geometry, high-speed camera, and video segmentation was developed for simultaneous, containerless, high-throughput measurement of the physical properties of liquid steel samples. The central atom model, a multicomponent solution model, is extended to investigate the statistical structure of alloys consistent with their energetics and solution thermodynamics. This allows liquid structure determination from thermochemical measurements, bypassing structural and atomistic modeling challenges of high-temperature liquid systems. These methods and models are explored on the binary systems of iron-nickel, the major substitutional alloying element in steel, and iron-carbon, the major interstitial species. Results demonstrate successful liquid property measurement at experimental rates far exceeding traditional high-temperature research and introduce a basis for a unified treatment of thermodynamic and physical properties in multicomponent alloy melts.

Chemical Probes and Strategies to Study Mycobacterial Cell Envelope Assembly

Thu, 01 May 2025 00:00:00 GMT

Chemical Probes and Strategies to Study Mycobacterial Cell Envelope Assembly Lee, So Young The cell envelope of Mycobacterium tuberculosis (Mtb) is central to its pathogenicity, immune evasion, and intrinsic drug resistance. While the importance of its glycan components is well recognized, their structural intricacies have hindered efforts to directly perturb and investigate their function. In this work, we discuss chemical approaches to study and manipulate mycobacterial cell envelope biosynthesis. Specifically, we present biosynthetic glycan labeling strategies that leverage the activity of native glycosyltransferases to probe arabinogalactan and mannose-containing glycolipids. Building upon prior work using lipid-linked probes to label mycobacterial arabinan, Chapter 2 details the development of azido-functionalized farnesyl phosphoryl mannose (AzFPM) probes that mimic native polyprenyl-phosphoryl donors and selectively label mannose-containing glycolipids in live mycobacteria. Chapter 3 showcases how these probes enable glycan substructure-specific labeling and biochemical enrichment of glycolipids across Corynebacterium glutamicum, Mycobacterium smegmatis, and Mtb. This strategy provides a platform to study glycolipid dynamics in wild-type cells, a task previously hindered by the lack of selective labeling tools. In Chapter 4, we further interrogate endogenous glycan biosynthesis by applying biosynthetic labeling probes in C. glutamicum. Perturbation of arabinan structure by probe incorporation led to impaired cell wall integrity and growth defects. In glycosyltransferase deletion strains, altered probe incorporation patterns revealed enzyme-specific roles in glycan assembly and architecture. Beyond novel labeling strategies, Chapter 5 describes the development of targeted inhibitors of galactan biosynthesis, an essential yet underexplored component of the mycobacterial cell wall. We employed a prodrug strategy to inhibit UDP-galactopyranose mutase (UGM), which catalyzes the committed step in Galf production. To overcome delivery challenges, we designed amide prodrugs activated intracellularly by amidases. One prodrug exhibited improved efficacy in Mtb, providing a promising lead for antibiotic development. Collectively, these studies establish biosynthetic labeling and targeted galactan inhibition as powerful tools for dissecting the structure and function of the mycobacterial cell envelope, offering new avenues for developing chemical probes and therapeutics against tuberculosis.

Photonic design for chemical analysis

Thu, 01 May 2025 00:00:00 GMT

Photonic design for chemical analysis Ma, Wenchao With light being manipulated at subwavelength scales, photonic design has been explored for various applications. In this dissertation, we investigate potential application of photonic design to chemical analysis, with a focus on spectrometry and chemical sensing. First, we demonstrate inverse design of single-layer metasurfaces with shape optimization. Each of the designed metasurfaces simultaneously focuses light and shapes the spectra of focused light without using any filters. Thus, both spatial and spectral properties of the meta-optics are engineered. We chose the color matching functions of the CIE 1931 XYZ color space as the target spectral shapes and a distant region with a finite size as the focal area. We then present an inverse-design approach for computational spectrometers, in which the scattering media are topology-optimized to achieve higher robustness in inference, without the need of a training set of spectra and noise. Our approach also allows the selection of the inference algorithm to be decoupled from that of the scatterer. For smooth spectra, we devise a regularized reconstruction algorithm based on Chebyshev interpolation, which yields higher accuracy compared with conventional treatment in which the spectra are sampled at equally spaced frequencies or wavelengths with equal weights. Our approaches are numerically demonstrated via inverse design of integrated computational spectrometers and reconstruction of example spectra. The inverse-designed spectrometer exhibits significantly better performance in the presence of noise than its counterparts with random scatterers. Furthermore, we discuss chemical detection using optical resonances, which can increase the sensitivity of measurements to material perturbations and also accelerate photochemical reactions. We show that these two effects can be combined multiplicatively, to enhance the detection via weak/low-concentration photochemical reactions far beyond what could previously be attained. For an optical resonance with a quality factor Q, the sensitivity of our detection scheme is enhanced by ~ Q² (where ~ denotes approximate proportionality), as demonstrated by both theoretical arguments and numerical simulations of a simple optical grating resonance coupled with reaction-diffusion equations. Such an approach opens a door to further improvements by careful design of the resonance: even a 3-parameter optimization of the grating resonance yields an additional ≈ 7 × improvement. Finally, regarding linear electromagnetic systems possessing time-reversal symmetry, we present an approach to bound ratios of internal fields excited from different ports, using only the scattering matrix, improving upon previous related bounds by Sounas and Alù (2017). By reciprocity, emitted-wave amplitudes from internal dipole sources are bounded in a similar way. When applied to coupled-resonant systems, our method constrains ratios of resonant coupling/decay coefficients. We also obtain a relation for the relative phase of fields excited from the two ports and the ratio of field intensities in a two-port system. In addition, although lossy systems do not have time-reversal symmetry, we can still approximately bound loss-induced non-unitarity of the S matrix using only the lossless S matrix. We show numerical validations of the near-tightness of our bounds in various scattering systems.

Sulfidation of Ternary Oxides: A Thermodynamic and Experimental Study Toward Selective Metal Extraction

Thu, 01 May 2025 00:00:00 GMT

Sulfidation of Ternary Oxides: A Thermodynamic and Experimental Study Toward Selective Metal Extraction Boury, Charles A. Conventional metal refining techniques face growing challenges due to increasing ore complexity and their limited ability to accommodate post-consumer recycling feedstocks. Sulfidation is a promising pyrometallurgical approach for the selective separation and recovery of critical elements from such complex feedstocks. This thesis presents a chemical thermodynamic framework for the sulfidation of divalent alkaline earth and transition metal ternary oxides of titanates, molybdates, tungstates, niobates, and tantalates. Modified predominance diagrams were constructed to determine the possible sulfidation outcomes, and a sensitivity analysis on the input thermodynamic data was performed to assess their impact on the outcome of sulfidation. A high-temperature apparatus was designed and tested to compare predicted and observed sulfidation behavior. Together, the model and experimental apparatus provide a new experimental method to estimate the high-temperature Gibbs energy of multicomponent oxides. Application to current chemical metallurgy challenges, including the recycling of tantalum-based capacitors and the refining of tungsten-bearing ores, underscore sulfidation as a powerful step to support new processing route for sustainable metal recovery.

Fundamentals, Voltage Control and Novel Application of Exchange Bias in Magnetic Thin Films

Thu, 01 May 2025 00:00:00 GMT

Fundamentals, Voltage Control and Novel Application of Exchange Bias in Magnetic Thin Films Hasan, Muhammad Usama The past half-century has seen remarkable advances in microelectronics, but as transistors approach their physical limits, there is a growing need for beyond-CMOS technologies. Spintronics, which aims to utilize the electron's spin in magnetic thin films for data storage and manipulation, is a promising alternative. Among the rich physical interactions that appear in magnetic thin films, the exchange-bias (EB) effect is essential for many spintronic devices. EB is an effect that arises at ferromagnet/antiferromagnet interfaces, which imposes an internal field on the ferromagnet, enhancing the range of functionalities that can be derived from devices. This thesis explores EB in Co/Co₁₋ₓNiₓO systems at multiple levels, from fundamental understanding to its manipulation and applications. First, we introduce a new model to predict EB in polycrystalline antiferromagnetic thin films and validate it with experimental data. Second, we tackle another fundamental aspect – disentangling the effects of EB on nucleation and propagation of magnetization reversal. We discover that nucleation and propagation EB can be unequal and demonstrate how that can lead to unexpected behavior of the system, including having asymmetric hysteresis loops. Building on these insights, we demonstrate voltage-controlled ionic gating to manipulate EB, achieving cyclic toggling of the EB sign in a ferrimagnetic system, where the magnetization direction is fully determined by the gating state. Furthermore, by targeting the antiferromagnet directly, we discover EB enhancement up to 100%, which can be explained with the help of the model developed earlier. We demonstrate sub-millisecond and analog operation in this system. Finally, a new approach to improving bit-stability whilst preserving performance in magnetic racetrack memory is proposed which involves incorporating an EB layer with the right properties into the track. The benefits obtained from this strategy can help push this next-gen memory device closer to commercialization. We believe the findings in this thesis substantially extend the state-of-the-art in terms of basic understanding of EB, ways of EB manipulation and unexplored use-cases of EB, paving the way for new functionalities in spintronic devices applicable for non-conventional computing paradigms or next-gen memory devices.

Essays on Urban Resilience to Environmental and Health Risks

Thu, 01 May 2025 00:00:00 GMT

Essays on Urban Resilience to Environmental and Health Risks Fan, Yichun Cities today face growing environmental and health threats due to climate change. Building urban resilience requires understanding the complex interplays between environmental and social systems that account for adaptation dynamics. Using new data, computational tools, and economic analysis, this thesis explores how people and places adapt to environmental risks and the implications for urban policy and infrastructure planning. Chapter 1 examines how the financing structure of climate resilience infrastructure impacts long-term economic dynamics. Using satellite imagery to develop new performance metrics for U.S. flood protection levees, I find that decentralized financing of infrastructure maintenance creates a feedback loop: lower housing values and property tax revenues reduce fiscal capacity for levee maintenance, which increases levee failure risk and further depresses housing values. These feedback dynamics reinforce under-maintenance and perpetuate spatial inequality. Chapter 2 analyzes the social cost of behavioral adaptation. Leveraging 27 million fitness app exercise records and quasi-experimental designs, I find that heavy air pollution reduces outdoor exercise likelihood by 28%, with information and risk awareness as key moderators. This behavioral response results in significant health costs often overlooked in environmental health studies. Chapter 3 explores the role of subjective traits in predicting adaptation behavior. Applying Natural Language Processing to social media posts from 500,000 users, we classify individual fear types and find that pre-pandemic fearfulness strongly predicts social distancing behavior during COVID-19. This project provides a scalable tool for measuring unobserved subjective traits to predict behaviors under risk and target interventions.

Monitoring and Treating Neurological Conditions Through Focal Interfacing with the Brain

Thu, 01 May 2025 00:00:00 GMT

Monitoring and Treating Neurological Conditions Through Focal Interfacing with the Brain Jackson, Hannah Dale Neurological dysregulation serves as the fundamental basis for a spectrum of debilitating disorders such as Parkinson's disease and epilepsy. Despite considerable efforts, our current comprehension of these disorders and ability to treat them remains limited. Neurochemical sampling of the affected tissue can be used to monitor pathological states, but existing tools are limited by tissue reactivity and suboptimal spatiotemporal resolution. Additionally, methods for treating neurological disorders predominantly rely on systemic drug administration which is hampered by inadequate targeting and off-target effects. There is a need for minimally invasive modalities to both monitor and treat neurological disorders that have high spatial resolution, maintain chronic functionality, and preserve overall brain function. This thesis presents the development and implementation of neural implants capable of both infusing and sampling sub-microliter volumes of fluid with exceptional spatial precision. These implants utilize micron-scale technology to minimize scarring following implantation and allow for sustained chronic functionality. We use these devices to answer two key questions: (1) Can the localized delivery of drugs to specific neural circuits provide effective treatment for neurological diseases? and (2) Can micro-invasive sampling of brain interstitial fluid facilitate disease diagnosis and monitoring? We assessed our ability to treat focal epilepsy with this platform by delivering antiseizure medications directly to the seizure focus in a mouse model of temporal lobe epilepsy. We found that localized drug delivery effectively suppressed seizure activity without adverse effects. We also explored micro-invasive, membrane-free sampling of interstitial fluid from different brain regions using our device. We detected hundreds of distinct proteins from minute sample volumes with high spatial resolution and minimal tissue damage. The results from both studies highlight the platform’s potential for targeted drug delivery and biomarker detection across a variety of disease states.

Toward sequence-to-structure predictions of chromatin: Generative AI sheds light on genome organization

Thu, 01 May 2025 00:00:00 GMT

Toward sequence-to-structure predictions of chromatin: Generative AI sheds light on genome organization Schuette, Greg The secrets of the genome have captivated scientists for well over a century, though the active role its spatial organization plays in gene regulation, cell determination, and disease formation has become clear only in recent decades. Significant strides have been made toward characterizing and understanding three-dimensional genome organization, but the scale, complexity, and heterogeneity of the genome and nuclear environment complicate investigations into this system. This thesis alleviates these challenges and holds the potential to accelerate genome organization research by presenting several methodological advances. An efficient Hi-C inversion algorithm appears first. This technique extracts pairwise contact potentials from experimental Hi-C data, uncovering mechanistic details obscured by the correlation between Hi-C contact probabilities. This required the development of a spin-glass model of chromatin and the derivation of a corresponding model inversion; the model may find use in further theoretical studies of chromatin, while the inversion can be applied more broadly. The inversion successfully revealed the location of chromatin loop anchors, supported the phase separation formation of chromatin compartments, and parameterized polymer models that reproduced the experimental Hi-C data with reasonable accuracy. The focus then shifts toward ChromoGen, a generative AI model that predicts three-dimensional chromatin structures directly from DNA sequence and chromatin accessibility data. ChromoGen provided biologically accurate structural ensembles throughout the genome of two cell types, including one omitted from its training data. This transferability suggests that ChromoGen can provide access to the organization of chromatin in a wide variety of cell types while only relying on widely available sequencing data. Afterward, we discuss several strategies to extend ChromoGen to full-chromosome structure prediction tasks. Preliminary results suggest that the technology of today can provide this capability, as we have generated physical chromosome conformations for mouse chromosomes, although sequencing data did not guide this generative process. Correspondingly, we explore the possibility of incorporating a multimodal model with ChromoGen, allowing it to condition structure generation on a wide variety of data types. Success in this area could enable true de novo structure prediction, greatly simplifying research aiming to understand the relationship between sequence, structure, and cellular function while also accelerating the development of treatments for diseases that implicate chromatin dysregulation.

Reparative Urban Science: Challenging the Myth of Neutrality and Crafting Data-Driven Narratives

Thu, 01 May 2025 00:00:00 GMT

Reparative Urban Science: Challenging the Myth of Neutrality and Crafting Data-Driven Narratives So, Wonyoung This dissertation constructs a distinctive lens on how we should see urban technology in the context of a long history of systemic racism, and how we can take a reparative approach to intervene in contemporary situations of racial inequality with technology/data as a method to address systemic racism. The current discourse of urban science often puts an emphasis on newly available (and big) data, primarily values methodologies of “hard” sciences such as physics, computer science, and mathematics, and evolves to incorporate the latest technologies and analytic methods including machine learning and artificial intelligence. However, the role of urban science and analytics that “move[s] beyond analysis” has not been extensively theorized. In particular, the relationship between urban technologies, white supremacy, and racial capitalism has not been extensively studied. Nonetheless, the impact of the applications of such “urban analysis” on people’s lives has been substantial. Building on planning scholars’ calls for reparative planning and emerging discourses of “algorithmic reparation,” this dissertation proposes a normative framework of reparative urban science that challenges whiteness in urban science and embraces the epistemologies and methodologies of reparations. The dissertation follows a three-paper structure, with the first paper serving as the theoretical framework for two empirical studies, and includes a concluding chapter. The first paper introduces the overarching theory of reparative urban science, identifying three mechanisms—formalizing, context removal and legitimization, and penalization—through which urban technologies perpetuate historical inequalities under a race-neutral guise. It then proposes reparative methodologies, including algorithmic auditing, crowd-sourced community data collection, and algorithms designed to simulate and deliberate reparative futures. The second and third papers demonstrate reparative urban science in action, exemplifying these methodologies. The second paper investigates tenant screening services and landlord decision-making. It reveals the mechanisms how tenant screening algorithms contribute to obscuring historical racial biases, and how landlords interact with them to exert harms. The third paper evaluates the reparative potential of housing programs using algorithmic methods, particularly comparing race-neutral versus race-conscious Special Purpose Credit Programs (SPCPs). It demonstrates that race-conscious SPCPs could significantly reduce the racial housing wealth gap than race-neutral ones, showing race-conscious policies as potential reparative tools. The concluding chapter explores theoretical and practical considerations of housing reparations through the lens of reparative justice, arguing for a deeper acknowledgment of the historical and structural harms related to land and property. Overall, this dissertation seeks to reorient urban science toward justice and repair, envisioning a transformative path forward that actively confronts historical harms and promotes healing and equity in urban futures.

Growth-Induced Cation Order and Magnetic Anisotropy Engineering in Iron Garnet Thin Films

Thu, 01 May 2025 00:00:00 GMT

Growth-Induced Cation Order and Magnetic Anisotropy Engineering in Iron Garnet Thin Films Kaczmarek, Allison C. At its heart, Materials Science and Engineering is a discipline seeking to advance technologies by improving the materials that make them. Currently, the development of electronic devices is limited by the need for materials that enhance speed, reduce size, and improve energy efficiency. Spin-based memory devices, which encode data in a material’s magnetic state, offer a promising solution. Magnetic memory technologies are widespread today, powering devices such as memory disks, tapes, and magnetic random access memory (MRAM). While garnet materials have long been studied for these applications, they have faced challenges in becoming adoptable technologies. However, with advanced research techniques and a deeper understanding of material behaviors, magnetic garnets are experiencing a renaissance. This thesis explores the engineering of iron garnet thin films for next-generation spin-based memory applications. The work presented advances the understanding of non-equilibrium growth, characterization, and engineering of iron garnet thin films and their magnetic properties, emphasizing kinetic phenomena that govern atomic organization beyond classic ordering of unit cells and emergent magnetic anisotropy. In a composition series of europium-thulium iron garnet (EuTmIG) films, experiment confirms the 50-year-old theory of cation site preference of Eu and Tm, demonstrating that enhanced magnetic anisotropy, named ’magnetotaxial anisotropy’, is linked to cation ordering during growth. These findings lay the foundation for anisotropy engineering by cation order. Further studies investigate the effects of film formulation, growth kinetics, and post-growth annealing on structural ordering and magnetotaxial anisotropy. In bismuth-yttrium iron garnet (BiYIG) films, a linear relationship between Bi-Y ordering, magnetic anisotropy, and substrate-lattice mismatch provides deeper insight into the forces that drive cation ordering. Annealing is shown to further enhance magnetic anisotropy in these films. In lutetium-yttrium iron garnet (LuYIG) films, the laser pulse rate during growth by pulsed laser deposition is shown to influence Lu-Y ordering and magnetic anisotropy, reinforcing the kinetic nature of the cation ordering. The findings of this thesis contribute to the fundamental understanding of cation ordering in complex oxide films and provide a framework for engineering and characterizing garnet materials, enabling the future development of new spintronic devices.

Controlling and probing nonlinear collective mode dynamics in quantum materials

Thu, 01 May 2025 00:00:00 GMT

Controlling and probing nonlinear collective mode dynamics in quantum materials Zhang, Zhuquan Tailored laser pulses offer a powerful means of driving materials out of equilibrium by selectively addressing specific degrees of freedom. In particular, the excitation of low-energy collective modes in solids—such as lattice vibrations (phonons) and spin precessions (magnons)—to large amplitudes opens fundamentally new pathways for controlling and probing material properties that are otherwise inaccessible under thermal equilibrium conditions. In this regime, both the nonlinear interactions between light and matter and the intrinsic nonlinear dynamics of the driven modes present significant challenges for understanding the underlying mechanisms and for realizing potential applications. This dissertation centers on two major themes: (1) probing equilibrium properties of materials via nonlinear light-matter interactions; and (2) unveiling emergent phenomena hidden in equilibrium by driving collective modes far from equilibrium. I begin by providing an overview of recent advances in controlling and probing quantum materials out of equilibrium, followed by a discussion of the theoretical frameworks and experimental methodologies used to interrogate collective excitations. Building on this foundation, I present two studies demonstrating how terahertz Raman excitation can reveal distinct spectroscopic signatures of material states. Subsequently, I focus on coherent nonlinear magnon-magnon interactions in canted antiferromagnets, induced by tailored terahertz fields. In these experiments, we demonstrate a unidirectional magnon upconversion process and identify correlated magnonic responses at both the sum and difference frequencies of the interacting modes. We achieve parametric amplification of magnon coherence by tuning the magnonic difference-frequency generation into resonance with a low-frequency magnon. Furthermore, by increasing the driving field strength to access a far-from-equilibrium regime, we uncover spectroscopic signatures of non-perturbative dynamics marked by strong magnon self-interactions. Finally, I present an example in which spatially heterogeneous responses of electromagnon modes in a van der Waals multiferroic are revealed through terahertz photon echo measurements. Together, these results highlight how tailored light-matter interactions can be leveraged to probe, control, and manipulate material degrees of freedom, both in and out of equilibrium.

Decoding Brain Somatic Mosaicism with New Single-Cell Copy Number Analysis Methods

Thu, 01 May 2025 00:00:00 GMT

Decoding Brain Somatic Mosaicism with New Single-Cell Copy Number Analysis Methods Zhao, Yifan Copy number variants (CNVs) represent a significant but understudied form of somatic variation in the human brain, with potential implications for neurodevelopment, aging and disease. While single-cell whole-genome sequencing (scWGS) enables genome-wide profiling at single-cell resolution, existing computational methods struggle to accurately detect non-clonal CNVs, limiting our understanding of genomic mosaicism in the brain. In this thesis, I present two novel and complementary computational approaches for high-resolution CNV analysis in single cells. The first, HiScanner, is a CNV detection method that integrates single-cell assay-specific characteristics and introduces innovations in bin size optimization, read depth normalization, and joint segmentation across cells. Through extensive benchmarking experiments, I demonstrate HiScanner’s superior performance compared to existing tools. The second is a validation method that leverages unique molecular patterns from tagmentation-based scWGS, representing the first tool that exploits fragment overlap patterns to corroborate CNV predictions. I then apply these tools to investigate CNVs in three biological contexts: tumor evolution in paired initial and recurrent meningiomas, age-related genomic changes in neurotypical human brains, and developmental patterns in fetal and postnatal brain tissues. By analyzing both scWGS and multimodal single-cell data (paired RNA-seq and ATAC-seq), I characterize cell-type-specific CNV patterns and their potential functional implications. This work establishes a robust framework for studying somatic CNVs at single-cell resolution and provides insights into genomic instability in brain development, aging, and disease.

Origin and Correlates of Viral Rebound in SIV-Infected Rhesus Macaques Following Discontinuation of ART

Thu, 01 May 2025 00:00:00 GMT

Origin and Correlates of Viral Rebound in SIV-Infected Rhesus Macaques Following Discontinuation of ART King, Irena V. The earliest events of viral rebound following discontinuation of ART in people living with Human Immunodeficiency Virus-1 remain largely unknown. We investigated detailed reservoir characteristics and viral rebound dynamics in 18 Simian Immunodeficiency Virus-infected rhesus macaques treated with antiretroviral therapy for 70 weeks and then necropsied after a 12-day analytical treatment interruption (ATI). Using molecularly barcoded SIVmac239M, we tracked viral clonotypes following ATI in both peripheral blood and tissues at necropsy. Viral rebound appeared to originate by reactivation of a single or a few barcode clonotypes from a limited number of deep lymph nodes or gastrointestinal tissues, followed by rapid virus replication of this clonotype in peripheral blood and tissues as well as serial reactivation of multiple additional barcode clonotypes from different anatomic sites, resulting in oligoclonal plasma viremia. Daily transcriptomic and proteomics profiling in peripheral blood following ATI identified early upregulation of pathways related to T cell signaling, cytokine responses, and metabolism prior to detectable plasma viremia, presumably reflecting initial viral replication in tissues. Taken together, these data provide a detailed anatomic, virologic, and immunologic characterization of viral rebound in SIV-infected macaques following ATI, which provides critical information to inform the development of next generation HIV-1 cure strategies.

Multiscale Modeling of Genome Organization: Bridging Polymer Physics, Molecular Dynamics, and AI

Thu, 01 May 2025 00:00:00 GMT

Multiscale Modeling of Genome Organization: Bridging Polymer Physics, Molecular Dynamics, and AI Lao, Zhuohan The human genome is intricately organized within the nucleus, and its spatial arrangement plays a critical role in gene regulation, cellular function, and disease. Recent advances in high- throughput experiments have unveiled the heterogeneous and dynamic nature of chromatin organization at single-cell resolution. However, computational tools that can both simulate and predict such complex structures are still limited. In this thesis, we develop and apply computational frameworks to investigate nuclear genome organization at high spatial and temporal resolution. Our approaches integrate biophysical modeling and generative artificial intelligence to address complementary aspects of nuclear architecture. In Chapter 1, we provide an overview of the hierarchical organization of the genome and discuss emerging principles that govern chromatin folding, nuclear compartmentalization, and their functional implications. We introduce data-driven, physics-based, and generative artificial intelligence modeling approaches, highlighting the need for interpretable and efficient models capable of capturing the structural diversity of the nucleus across individual cells. In Chapter 2, we present OpenNucleome, a high-resolution molecular dynamics framework for simulating the entire human nucleus at 100-kilobase resolution. OpenNucleome incorpo- rates explicit representations of chromosomes, nuclear bodies, and the nuclear lamina, and faithfully reproduces experimental data from Hi-C, TSA-seq, DamID, and DNA-MERFISH. The developed software is fully open-source and GPU-accelerated, enabling large-scale simu- lations and mechanistic explorations. In Chapter 3, we explore the impact of genome organization on various biological phe- nomena within the cell nucleus—focusing on telomere and telomere condensate dynamics, and nuclear deformation—using OpenNucleome. Our results demonstrate that the three- dimensional genome architecture plays a pivotal role in governing the dynamics of genomic loci such as telomeres, influencing the kinetics and outcomes of droplet coarsening. More- over, specific interactions between the genome and nuclear bodies form robustly across cells, providing strong support for a nuclear zoning model of genome function. In Chapter 4, we introduce ChromoGen, a generative diffusion model that predicts single- cell chromatin conformations de novo from DNA sequence and DNase-seq data. Unlike traditional simulation frameworks, ChromoGen learns from experimental single-cell 3D structures to generate physically realistic, region- and cell type-specific ensembles. ChromoGen achieves high agreement with both experimental Dip-C and Hi-C data while maintaining computational efficiency, enabling rapid exploration of chromatin heterogeneity across the genome and cell types. Together, these two frameworks—OpenNucleome and ChromoGen—provide powerful and complementary tools for understanding genome structure and function at the single-cell level, bridging physics-based modeling and deep generative artificial intelligence modeling.

On single-cell immune dynamics of chronic HIV infection and treatment in rhesus macaque models

Thu, 01 May 2025 00:00:00 GMT

On single-cell immune dynamics of chronic HIV infection and treatment in rhesus macaque models Quinn, Sarah Lynne Human Immunodeficiency Virus (HIV) continues to be an overwhelming challenge in both global health and immunology. With no cure available, 30 million people worldwide rely on antiretroviral therapy (ART) to prevent transmission and disease progression. However, individuals on ART are at elevated risk for numerous comorbidities and are susceptible to continued disease progression should treatment be stopped or interrupted. Addressing the challenges resulting from treatment and lack of cure requires a deeper understanding of the complex underlying immunology of HIV infection, treatment, and therapeutics. Single-cell RNA sequencing (scRNAseq) is continually advancing our understanding of immune dynamics and when combined with well-characterized rhesus macaque models of HIV, provides an opportunity to profile immune perturbations over extensive time courses in a controlled setting. In this thesis, I present two studies that further our understanding of the host immune response across stages of infection, treatment, and therapeutic intervention, using rhesus macaque models. In the first study (Chapter 2), I comprehensively profiled immune dynamics during untreated infection, ART initiation, and long-term ART, leveraging a longitudinal cohort of SIV-infected (Simian Immunodeficiency Virus) macaques. This work is particularly relevant given the increasing age and average time spend on ART among people living with HIV. scRNAseq revealed key immune shifts during acute and chronic infection, as well as over five years of subsequent ART. I identified cell type composition shifts during prolonged untreated infection and uncovered areas of unresolved immune dysregulation despite long-term ART– most prominently among myeloid gene expression and enrichment. I further link transcriptional changes to intact proviral burden and identified ribosomal pathways as markers infection stage, treatment status, reservoir size. Finally, by evaluating published immune correlates of treatment outcome, I identify nuances in signatures changing and remaining stable with time on ART. In the second study (Chapter 3), I expand on the baseline infection and treatment case by evaluating immune dynamics in response to a post-exposure combination therapeutic (Ad26/MVA + PGT121 + Vesatolimod) previously shown to induce post-ART viral control in most (7/10) treated macaques with Simian Human Immunodeficiency Virus (SHIV). Here I identify features of therapeutic response and implicate a previously defined Antibody-Dependent Cellular Phagocytosis signature as being associated with control. Furthermore, I identify a new cytotoxic transcriptional module in T and NK cells associated with both non-rebounding animals and post-rebound controller animals, suggesting a shared effector program associated with successful virologic control. Supported by a thorough introduction on immunological techniques, questions and applications to HIV studies (Chapter 1), and a discussion of intersectionality and future directions of the field (Chapter 4), this thesis provides a comprehensive analysis of immune dynamics across the lifecycle of viral infection, treatment, and therapeutic intervention in macaque models of HIV. This work demonstrates how the host immune environment influences therapeutic success, laying a foundation for future therapeutic design.

Adsorption and electrostatic potentials at the electrochemical interface

Thu, 01 May 2025 00:00:00 GMT

Adsorption and electrostatic potentials at the electrochemical interface Nowack, Linsey This paper explores adsorption in electrochemical systems. Part I reviews ways to quantify adsorption, the energetic considerations that make adsorption favorable or unfavorable, how to measure adsorption experimentally, and how it has previously been modeled using extensions of the Langmuir isotherm. At the end of Part I, a simple Monte Carlo model (MC) is applied to a very complex carbon dioxide reduction system to study competitive adsorption. This application of Monte Carlo simulations demonstrates the challenges of extracting meaningful parameters from empirically fitting MC simulations to isotherms derived from nanoparticle-enhanced Raman spectra. Part II examines how adsorption influences the electrostatic potential in the electrochemical double layer using molecular dynamics simulations. Adding on to previous work from the Willard group, this chapter calculates how adsorbate polarity and coverage influences two characterizations of Coulombic interactions: the Poisson potential and the Madelung potential. Both potentials, while having different shapes as a function of distance from the electrode surface, exhibit strong sensitivity to water structure. At high coverage, adsorbates decrease the number of interfacial waters, shifts the position of the molecular layers of waters at the interface, and disrupts the water's orientational order. Lastly, cross-sections of the 3D Poisson potential parallel to the electrode surface reveal large heterogeneity in Poisson potential values as a result of adsorbates. This suggests that 1D electrostatic potential profiles are not enough to understand forces in the electrochemical double layer.

The combustion of droplets of heavy liquid fuels

Fri, 01 Jan 1954 00:00:00 GMT

The combustion of droplets of heavy liquid fuels Simpson, Hugh Cameron. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 1954; Vita.; Bibliography: leaves 542-552.

On the use of selected derivatives in the specific characterization of alcohols, phenols, amines and mercaptans

Fri, 01 Jan 1926 00:00:00 GMT

On the use of selected derivatives in the specific characterization of alcohols, phenols, amines and mercaptans Zeng, Zhaolun, 1898-1967. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Chemistry, 1926; Vita.

The chemistry of octahalodirhenate (III).

Sat, 01 Jan 1966 00:00:00 GMT

The chemistry of octahalodirhenate (III). Robinson, William Robert. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1966; Bibliography: leaves 112-116.

On the economics of advertising.

Thu, 01 Jan 1970 00:00:00 GMT

On the economics of advertising. Schmalensee, Richard. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1970; Vita.; Bibliography: leaves 485-496.

An essay on taxation and growth in an enclave economy.

Mon, 01 Jan 1968 00:00:00 GMT

An essay on taxation and growth in an enclave economy. Francis, Alfred Alexander Jaques. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1968; Bibliography: leaves 130-131.

The Hiddenness Argument and The Limits of Doxastic Positioning

Sat, 01 Feb 2025 00:00:00 GMT

The Hiddenness Argument and The Limits of Doxastic Positioning Garcia, Nicole If God exists, how clear or obvious should we expect his existence to be? Particularly if such a God is interested in having a personal relationship with us? The Hiddenness Argument contends that it should be much clearer than it in fact is. If God exists and really wants us to know as much, we should expect to inhabit a very different epistemic situation than we in fact do – one that rules out the possibility of rational nonbelief. The evidence available for God’s existence should be so definitive that it would be impossible for us to fail to believe on good epistemic terms. My dissertation sets out to delegitimize this expectation. While available objections to it challenge its propriety – God may have overriding reasons for disclosing his existence in a way that allows for rational nonbelief – my account challenges its feasibility – whether it is in principle possible to meet. Expecting divine self-disclosure to rule out rational nonbelief assumes that it can rule out rational nonbelief – but can it? By homing in on the nature, mechanics, and limitations of disclosure itself, I show it cannot. Divine self-disclosure is an instance of what I call doxastic positioning: the process of positioning someone to rationally form some belief – in this case, theistic belief. To rule out rational nonbelief, God’s disclosure would need to make theistic belief a universal rational requirement. Given the success conditions of doxastic positioning, this would involve the provision of sufficient evidence as well as the universal possession and appreciability of said evidence. But no matter what evidence God provides, God cannot guarantee on pain of irrationality that humans will possess or be in a position to appreciate the available evidence, leaving rational nonbelief an ever live possibility. —Which is just to say that divine self-disclosure cannot rule out rational nonbelief. The expectation that it would do so is, then, illegitimate and the hiddenness argument depending on it fails.

Proteolethargy is a pathogenic mechanism in chronic disease

Thu, 01 May 2025 00:00:00 GMT

Proteolethargy is a pathogenic mechanism in chronic disease Moreno, Shannon The pathogenic mechanisms of many diseases are well understood at the molecular level, but there are prevalent syndromes associated with pathogenic signaling, such as diabetes and chronic inflammation, where our understanding is more limited. Here, I present evidence that pathogenic signaling suppresses the mobility of a spectrum of proteins that play essential roles in cellular functions known to be dysregulated in these chronic diseases. The reduced protein mobility, which we call proteolethargy, was linked to cysteine residues in the affected proteins and signaling-related increases in excess reactive oxygen species. Diverse pathogenic stimuli, including hyperglycemia, dyslipidemia, and inflammation, produce similar reduced protein mobility phenotypes. I propose that proteolethargy is an overlooked cellular mechanism that may account for various pathogenic features of diverse chronic diseases.

Neural Sequences Underlying Directed Turning in C. elegans

Thu, 01 May 2025 00:00:00 GMT

Neural Sequences Underlying Directed Turning in C. elegans Kramer, Talya Complex behaviors like navigation rely on sequenced motor outputs that combine to generate effective movement. The brain-wide organization of the circuits that integrate sensory signals to select and execute appropriate motor sequences is not well understood. Here, we characterize the architecture of neural circuits that control C. elegans olfactory navigation. We identify error-correcting turns during navigation and use whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. These turns occur as motor sequences accompanied by neural sequences, in which defined neurons activate in a stereotyped order during each turn. Distinct neurons in this sequence respond to sensory cues, anticipate upcoming turn directions, and drive movement, linking key features of this sensorimotor behavior across time. The neuromodulator tyramine coordinates these sequential brain dynamics. Our results illustrate how neuromodulation can act on a defined neural architecture to generate sequential patterns of activity that link sensory cues to motor actions.

High-Dimensional Statistics for Causal Inference and Panel Data

Thu, 01 May 2025 00:00:00 GMT

High-Dimensional Statistics for Causal Inference and Panel Data Klosin, Sylvia This dissertation develops new econometric tools for causal inference in panel data settings, with a focus on addressing key biases that arise in high-dimensional and dynamic environments. While this dissertation is motivated by the need to flexibly measure the economic impacts of climate change, the methods I develop are much more general. They apply broadly to panel data problems across empirical economics—including in labor, development, and industrial organization—where standard fixed effects estimators may fail. The first chapter identifies a previously overlooked source of bias in fixed effects panel estimators, which I term dynamic bias. This bias arises when dynamic feedback—where past outcomes influence current outcomes—is ignored in the estimating equation. I show that dynamic bias can be severe even when treatments are randomly assigned and that it often exceeds the well-known Nickell bias. To address this, I develop a bias-corrected estimator that is consistent in panels with a fixed number of time periods. I apply this method to estimate the effects of temperature shocks on GDP, where accounting for dynamic feedback reduces estimated damages substantially. The second chapter, coauthored with Max Vilgalys, proposes a flexible estimator for continuous treatment effects using panel data with fixed effects. We extend the double debiased machine learning (DML) framework to this setting and prove consistency and asymptotic normality. In an application to U.S. agriculture, we show that our estimator captures nonlinear effects of temperature on crop yields more accurately than standard linear models, estimating substantially larger damages from extreme heat. The final chapter further generalizes the methodological contribution by introducing a non-parametric estimator of the average dose-response function. Building on recent developments in DML and automatic double machine learning (ADML), I propose a novel debiasing strategy that directly estimates the bias correction term, yielding favorable theoretical properties. Together, these essays provide practical and theoretically grounded tools for applied researchers working with panel data, particularly in settings characterized by high dimensionality, continuous treatments, or dynamic feedback.

Essays in Health Economics

Thu, 01 May 2025 00:00:00 GMT

Essays in Health Economics Moran, Kelsey C. This dissertation comprises three essays in health economics. The first paper studies how imperfect electronic health record (EHR) system compatibility, or interoperability, affects patients. My coauthors, Rebekah Dix and Thi Mai Anh Nguyen, and I find that improved EHR interoperability between hospitals leads to better health outcomes and lower costs for shared patients. We also show that hospitals prefer sending patients to facilities with more compatible EHR systems, causing patient reallocation across providers based on technological factors. Using a model of patient flows, we estimate that eliminating these frictions would generate substantial welfare gains by improving patient outcomes and reducing allocative distortions. The second paper examines how regulatory requirements influence hospital charity care by analyzing the Hill-Burton Act of 1946, which allocated $6 billion to over 3,500 hospitals in exchange for providing free care to uninsured patients. I find that after these obligations expire, hospitals strategically reduce charity care by 30% and decrease admissions of charity-eligible patients by 14%. These patients subsequently shift to neighboring public and non-profit hospitals, where they must pay for care and experience higher mortality rates. The third paper, co-authored with Ari Bronsoler, Joseph Doyle, and John Van Reenen, studies the broad impact of Health Information Exchange (HIE) on patient outcomes. Using a newly compiled database of state HIE laws as instruments for hospital HIE, we find that HIE significantly reduces mortality from infectious diseases and hospital readmission rates for common conditions. With HIE usage increasing by 50 percentage points from 2009 to 2019, we estimate this technology saved approximately 27,000 lives annually through improved care coordination and public health response.

Essays on Firms and Technology in Development Economics

Thu, 01 May 2025 00:00:00 GMT

Essays on Firms and Technology in Development Economics Houeix, Deivy My thesis investigates the relationships between technology and firms in lower-income countries. I explore both the economic impacts of technology on firms—how it affects their economic outcomes and their relationships with stakeholders, both within and across firms—and the determinants of technology adoption: what underlying factors impede or drive the uptake of new technologies? I combine a diverse set of methods, including large-scale field experiments, economic theory, and long-term collaborations with local partners to investigate how small firms—such as taxis, retailers, and others—transform their practices and relationships as they adopt new technologies. My work centers on West Africa, one of the world’s poorest regions, where economic research remains limited. Chapter 1: The first chapter investigates the idea that digital technologies have the potential to increase firm productivity. However, they often come bundled with data observability, which can be a double-edged sword. Observability reduces information frictions and can increase efficiency, but some agents may lose their informational rent and thus resist adoption. I explore this trade-off between observability and adoption through two field experiments conducted over nearly two years. These experiments, guided by contract theory, introduce digital payments to the Senegalese taxi industry in partnership with the country's largest payment company. In the first experiment, I randomize access to digital payments for drivers (employees) and transaction observability to taxi owners (employers). I find that digital payments reduce drivers' cash-related costs by about half but also serve as effective monitoring tools for taxi owners. Transaction observability substantially increases driver effort, contract efficiency, and the duration of owner-driver relationships. However, 50% of drivers—primarily the worst-performing and poorest—decline to adopt digital payments when transactions are observable. The second experiment shows that the adoption rate doubles when drivers are assured that owners will not be able to observe their transactions. I develop a theoretical framework and use the experimental variations to estimate the welfare impacts of policy counterfactuals. I show that removing transaction observability would maintain moral hazard problems but broaden adoption and thus increase overall welfare—an approach ultimately implemented by the payment company. These findings highlight the crucial role of information embedded in digital technologies, as it magnifies gains for adopting firms but can deter initial adoption. Chapter 2: In the second chapter, I conduct a randomized experiment to study the nationwide technology diffusion of a new digital payment technology in Senegal. By leveraging two novel sources of network data—mobile money transactions and anonymized phone contact directories covering the near universe of the adult population in Senegal—I causally identify three sets of adoption spillovers from taxi firms randomized to receive early access to the technology: intra-industry among taxi firms; inter-industry between taxi drivers and other small businesses; and inter-regional spillovers from the capital city to businesses in other urban centers. I show that spillovers go beyond strategic complementarities, reflecting social learning within firms' social networks, driven by social ties and remote interactions. Chapter 3: In the third and final chapter, I explore the fact that search and trust frictions have historically made it hard for small firms in lower-income countries to buy inputs from foreign markets. The growth in smartphone ownership and social media usage has the potential to alleviate these barriers. Informed by a dynamic model of relational contracting, we run a field experiment leveraging these technological tools to provide exogenous variation in (1) search frictions and (2) trust frictions (adverse selection and moral hazard) in a large international import market. In our search treatment, we connect a randomly selected 80% of 1,862 small garment firms in Senegal to new suppliers in Turkey. We then cross-randomize two trust treatments that provide additional information about the types (adverse selection) and incentives (moral hazard) of these new suppliers. Alleviating search frictions is sufficient to increase access to foreign markets: in all treated groups, firms are 26% more likely to have the varieties a mystery shopper requests and the goods sold are 30% more likely to be high quality. However, the trust treatments are necessary for longer-term impact: using both transaction-level mobile payments data and a follow-up survey, we show that these groups are significantly more likely to develop the connections into relationships that persist beyond the study. These new relationships lead to increases in medium-run profit and sales. Finally, we use the treatment effects to estimate the model and evaluate counterfactuals where we set various combinations of the frictions to zero, finding that the largest gains come from eliminating adverse selection.

Essays in Industrial Policy, Misallocation and Production Networks

Thu, 01 May 2025 00:00:00 GMT

Essays in Industrial Policy, Misallocation and Production Networks Garg, Tishara The thesis comprises three chapters studying industrial policy, misallocation and macroeconomic propagation in developing countries. The first chapter studies the impact of placebased industrial policies on equilibrium selection with Indian industrial parks as the empirical context. The second and third chapters study firm networks in Chile and Turkey respectively— using a common theoretical framework, the former studies the incidence of distortions while the latter studies the propagation of a large refugee shock. The first chapter introduces a method to study the impact of policy events on equilibrium selection in settings where strong complementarities may lead to multiple equilibria and coordination failures. Many industrial policies are rooted in the idea of coordination failures and big-push’ theories, yet empirical evidence on their effectiveness remains limited, since distinguishing equilibrium shifts from direct changes in fundamentals is challenging. Leveraging tools from industrial organization and algebraic geometry, I develop an approach to study coordination effects without imposing strong assumptions on the distribution or responsiveness of economic fundamentals. The method identifies the ‘types’ of factual and counterfactual equilibria through a three-step procedure: model estimation and inversion, equilibrium enumeration, and type assignment. Types of factual equilibria may be used to examine how events, like urban infrastructure, subsidy drives, or trade liberalization, affect equilibrium selection. Types of counterfactual equilibria further allow decomposition of observed effects into fundamentals- versus coordination-driven. I apply this method to study industrial zones in India. Using a newly assembled dataset, I find that municipalities receiving an industrial zone see a 60% increase in non-farm employment over 15 years, with significant spillovers to non-targeted sectors and municipalities. Combining the methodology with event study designs, I find that industrial zones increase the probability of escaping a low-industrialization equilibrium by 38%, with coordination effects explaining roughly onethird of the observed change in outcomes. The second chapter (joint with David Atkin, Baptiste Bernadac, Dave Donaldson, and Federico Huneeus) combines unique datasets from Chile to quantify the full incidence of distortions for the first time. Economic distortions—such as market power, taxes, credit constraints, etc.—are fundamental in understanding the difference between developing and developed economies. Recent work has documented the pervasive extent of economic distortions and how they lead to substantial misallocation, or aggregate productivity loss. Far less well understood is how these phenomena affect members of society differently. We embed a new dataset which we build by linking workers and owners to firms, firms to each other, firms to consumers, and firms and consumers to the government, inside a general equilibrium model of the Chilean economy. Armed with internal estimates of distortions on exchanges throughout the economy, as well as data on the network of such linkages, we conduct a series of counterfactual simulations that illuminate the incidence of distortions in our model economy. We find that the burden of distortions falls relatively more on the shoulders of the poor, the young and women. The final chapter (joint with Ahmet Gulek) investigates how immigration-induced wage shocks can propagate beyond the regions receiving immigrants through the production network. Using the Syrian refugee crisis in Turkey as a quasi-experiment and the near universe of domestic firm-to-firm transaction data from VAT records, we show that the immigration shock propagates both forward and backward along the supply chain. Firms in non-host regions who directly or indirectly buy from host regions demand more labor. Firms who sell to host regions weakly increase their sales. Estimates imply an elasticity of substitution between labor and intermediate goods of 0.76 and an elasticity of substitution of nearly 1 between intermediates. Counterfactual analyses show that the spillover effects on non-host regions are economically meaningful when the host regions are central nodes of the domestic trade network. For example, a 1% increase in labor supply in Istanbul decreases real wages in Istanbul by 0.56% and increases real wages in the average non-host city by 0.38%.

The influence of nutrient availability on tumor metabolism

Sat, 01 Feb 2025 00:00:00 GMT

The influence of nutrient availability on tumor metabolism Abbott, Keene Louis Tumor growth and progression are profoundly influenced by nutrient availability in the tumor microenvironment (TME). Nutrient accessibility not only shapes cancer metabolism but also affects therapeutic responses, genetic dependencies, and metastatic behavior. This dissertation explores how nutrient availability modulates these cancer phenotypes. First, we examined how environmental nutrient levels influence the efficacy of drugs targeting metabolic enzymes, showing that their effectiveness varies under different nutrient conditions. We also found that the nutrient composition of the TME in solid tumors is primarily determined by the tissue of origin rather than by the tumor itself. By contrast, leukemia cells actively reshape their nutrient environment. Furthermore, we assessed the impact of physiological nutrient conditions on genetic dependencies, identifying numerous genes whose essentiality is dictated by nutrient levels and uncovering potential new therapeutic targets in leukemia. Finally, we established that single nutrients do not dictate metastatic site preference. Instead, metastatic growth is driven by a complex interplay among multiple nutrients in the microenvironment and the intrinsic properties of cancer cells. These findings provide critical insights into how the nutrient environment influences tumor metabolism.

Healthy Behavior: Essays in Health and Behavioral Economics

Thu, 01 May 2025 00:00:00 GMT

Healthy Behavior: Essays in Health and Behavioral Economics Shreekumar, Advik These essays examine beliefs and decision-making in health settings, emphasizing the role of attention, information, and technology in shaping behavior. The first essay studies human error in chest x-ray interpretation, a common and consequential medical task. It casts radiologists as facing a classical decision-theory problem, derives a novel martingale test for optimal behavior, and implements this test through a prudent application of machine learning to anonymized health records from the Beth Israel Deaconess Medical Center. I find that 58 percent of radiologists make predictable mistakes when assessing cardiac health on chest x-rays. Roughly two thirds of errors are explainable as individual radiologists making inconsistent decisions, and one third reflect the possibility that algorithms detect novel or complex signals. The second essay studies app-based mindfulness meditation, which has grown popular due to claims about its effects on mental well-being, productivity, and decision making. We assess these claims an experiment with 2,384 US adults, randomizing access and usage incentives for a popular mindfulness app. App access improves an index of anxiety, depression, and stress at two weeks and four weeks, with persistent effects three months later. It also improves earnings on a focused proofreading task by 2 percent. The third essay studies a tradeoff governments face when making recommendations in an evolving crisis. We investigate the effect of taking an early position on how much people believe later recommendations, using an online experiment with 1,900 US respondents in early April 2020. We present participants with CDC projection about coronavirus death counts and randomize exposure to information that highlights how the President previously downplayed the threat. When the President’s inconsistency is salient, participants are less likely to revise their beliefs about death counts from the CDC projection. This aligns with a model of signal extraction from government communication, and has implications for changing guidelines in other settings. JEL Codes: D91, I12, C8

Large-Scale Optimization using Reinforcement Learning, Dynamic Programming, and Column Generation

Thu, 01 May 2025 00:00:00 GMT

Large-Scale Optimization using Reinforcement Learning, Dynamic Programming, and Column Generation Paskov, Alexander Spassimirov One of the most enduring challenges in large-scale optimization is determining how to push the boundaries of scalability without compromising on performance or rigor. For decades, the exponential advances in computational power offered a straightforward solution: bigger problems could simply be tackled by bigger machines. However, in recent years, it has become increasingly apparent that pure computational force alone can no longer keep pace with the ever-growing complexity and scale of real-world applications. Additionally, despite the remarkable success of general-purpose methods for linear and integer optimization, these methods often struggle when confronted with domains that involve intricate dynamics, massive dimensionality, or a need for fine-grained sequential decisions. The simple question thus arises: can we design new optimization methods that scale more appropriately? In this thesis, we propose using dynamic programming, reinforcement learning, and column generation as a practical way to address this need across a variety of settings. We begin by developing and refining our methodology within the context of reinforcement learning and dynamic programming. We then move on to the application of column generation, and finally show how these techniques can be combined to supercharge fundamental machine learning methods with large-scale optimality.

Mechanisms of phage detection by bacterial innate immune proteins

Sat, 01 Feb 2025 00:00:00 GMT

Mechanisms of phage detection by bacterial innate immune proteins Zhang, Tong Bacteria are under constant threat from their viral predators, known as bacteriophages (or phages). As a result, bacteria have evolved diverse immune mechanisms to protect themselves from phage infection, such as restriction modification, CRISPR-Cas, and abortive infection (Abi) systems. Because Abi systems function through killing infected cells to protect the bacterial population, they must stay inactive prior to infection, but rapidly detect phages and promptly trigger an immune response. Although many novel Abi systems have been discovered in recent years, how they detect phage infection remains poorly understood. Here, I demonstrated that CapRel_SJ46, an anti-phage protein from E. coli, senses phage infection by directly binding to the newly synthesized major capsid proteins (MCPs) of certain phages. Binding to the MCPs releases autoinhibition of the CapRel_SJ46 toxin domain, enabling it to pyrophosphorylate tRNAs, which blocks translation to restrict viral infection. Detection of the MCPs is analogous to how eukaryotic innate immune systems detect foreign invaders through conserved pathogen-associated molecular patterns (PAMPs). In addition to the MCPs, I found that CapRel_SJ46 can directly bind to another unrelated and structurally different phage protein, called Gp54. Bas11 phages harbor two trigger proteins, and both are sensed by CapRel_SJ46 during infection, indicating that a bacterial immunity protein can sense more than one phage-encoded trigger. Additionally, I demonstrated that another CapRel homolog, CapRel_Ebc, senses the inhibition of a host cell division protein by the phage-encoded trigger, which is analogous to effector-triggered immunity in eukaryotes, where innate immune proteins sense virulence-associated activities of pathogens rather than directly sensing PAMPs. Lastly, I characterized another Abi system, named RAZR (ring-activated zinc-finger RNase), and showed that RAZR forms a ring-shaped supramolecular complex of over 1 MDa upon sensing a phage-encoded PAMP, leading to activation of its RNase activity to restrict phage infection. This finding highlights the importance of higher-order molecular assembly in bacterial innate immunity. Collectively, my thesis work has provided new insights into the molecular mechanisms by which bacterial innate immune systems detect phage infection.

New Theory and New Practical Methods for Solving Large-Scale Linear and Conic Optimization

Thu, 01 May 2025 00:00:00 GMT

New Theory and New Practical Methods for Solving Large-Scale Linear and Conic Optimization Xiong, Zikai In the last several years there has been a dramatic shift in the way many large-scale linear programs (LPs) are solved in practice, with classic methods (simplex method and interior-point methods) being replaced by the primal-dual hybrid gradient method (PDHG) to solve large-scale LP problems. While PDHG---with heuristic enhancements and GPU implementation---has been very successful in solving large-scale LP problems, its performance can have substantial variance and an intuitive understanding of the drivers of its performance has been lacking. In this context the research in this thesis has three related goals: (i) the development of new theory to explain the performance of PDHG for large-scale LPs, (ii) the development of new practical methods for solving large-scale LP problems based on PDHG, and (iii) the generalization of such new theory and new practical methods to the more general class of conic optimization problems. The thesis is organized as follows. Chapter 1 is an introduction and a unified summary of the thesis research as a whole. Chapter 2 presents computational guarantees for PDHG for solving LP problems based on two instance-dependent natural geometric condition measures, namely the "limiting error ratio" and the "LP sharpness." The connection between these condition measures and other LP condition numbers is also developed. Chapter 3 presents computational guarantees for more general conic optimization problems using the geometry of the primal-dual (sub)level sets. Based on our analysis we propose a central-path Hessian-based rescaling to enhance algorithmic performance by improving the (sub)level set geometry. We present computational results that show the potential of our methodology to improve the performance of PDHG in practice. Chapter 4 presents a closed-form expression of the iteration complexity of PDHG for LP instances with unique optima. The iteration bound has a reciprocal relationship with (i) stability under data perturbation, (ii) proximity to multiple optima, and (iii) LP sharpness. Chapter 5 considers the iteration complexity of LP instances under a sub-Gaussian model of instance generation. In this model we show that PDHG is a polynomial-time algorithm with high probability. This result partially shrinks the gap between theory and practice for PDHG by showing that PDHG can solve "most" LP instances in polynomial time. Finally, Chapter 6 presents a practical PDHG-based large-scale conic optimization solver with GPU enhancements. In this chapter we present computational experiments that show that the solver is more efficient than other first-order methods and commercial solvers for large-scale conic optimization problems.

Informing Public Health Policy Design and Operations with Analytics: Methods and Applications

Thu, 01 May 2025 00:00:00 GMT

Informing Public Health Policy Design and Operations with Analytics: Methods and Applications Zerhouni, El Ghali Ahmed Data-driven approaches hold immense potential for improving public health decision-making in complex, uncertain, and high-risk environments. Yet, there are several key challenges that stand in the way of successfully leveraging large-scale, heterogeneous, and noisy data into timely and actionable policy insights. These challenges are particularly pronounced when conventional modeling tools fall short, for instance, in settings where health risks arise from infection propagation in intricate supply chains, rapidly mutating pathogens, or delayed and fragmented surveillance systems. This thesis introduces a suite of novel methodologies and use cases at the intersection of operations research, epidemiology, and machine learning to address some of these challenges and support more informed, timely, and proactive public health decisions. A central focus of this thesis is the management of health risks related to zoonotic viruses, which are pathogens that emerge in animals and can potentially jump to humans, then further evolve to become transmissible between humans. These viruses pose a growing global health threat. Notably, outbreaks of zoonotic viruses frequently emerge in live animal markets in developing countries, even when infection rates in the upstream farms supplying these markets remain consistently low. Motivated by this empirical observation, the first chapter of this thesis develops an innovative epidemiological model called the Transmission, Interaction, and Persistence (TIP) model. This model integrates stochastic supply chain dynamics and environmental transmission mechanisms, and sheds light on how market-level factors amplify the risk of infection outbreaks. It yields actionable insights regarding the potential effectiveness of risk mitigation strategies such as frequent market sanitation and supply consolidation. Since March 2020, the world has experienced multiple waves of infections caused by the SARS-CoV-2 virus. Similar to past pandemics, SARS-CoV-2 has spread in waves, each driven by different genetic variants of the virus. Public health agencies have often struggled to predict in advance which variants would drive a new wave of infections. The second chapter of this thesis introduces an AI-enabled early warning system for emerging viral variants. The newly developed predictive model incorporates genetic and epidemiological features and is trained and tested on over 9 million sequenced SARS-CoV-2 variants across 30 countries. It accurately predicts whether each new variant will drive a significant wave of infections within the following 3 months. There is ample biological and empirical evidence regarding the roles of mutating variants and population immunity in driving infection waves of respiratory viruses. Motivated by this, the third chapter of the thesis develops the first epidemiological model, called the Immunity-Variants-Epidemic (IV-Epidemic) model, that explicitly captures circulating variants and the evolving population immunity profile to more accurately reflect the long-term trajectory of variant-driven pandemics. It incorporates variant evolution and population immunity dynamics, and is able to replicate the observed multi-wave infection patterns without requiring ad hoc recalibration. The fourth chapter of the thesis focuses on post-marketing pharmacovigilance, which is key to drug safety regulatory work. It presents PR1SM (Patients Really are 1st in Signal Management), an AI-based framework for identifying potential drug safety signals using post-marketing surveillance data. By structuring adverse event reports into parallel time series at multiple levels of clinical aggregation and adjusting for exposure trends, PR1SM complements standard disproportionality methods to detect safety signals earlier and with greater sensitivity in both real-world and synthetic settings. Collectively, the chapters of this thesis demonstrate how operations research can be combined with domain-specific methods in biology, epidemiology, and pharmacovigilance to inform data-driven public health strategies. The proposed analytical frameworks offer interpretable, scalable, and policy-relevant tools to create more resilient public health systems.

Regulation and activity of the 5-methylcytosine DNA glycosylase ROS1 contributes to DNA methylation patterning across development

Sat, 01 Feb 2025 00:00:00 GMT

Regulation and activity of the 5-methylcytosine DNA glycosylase ROS1 contributes to DNA methylation patterning across development Hemenway, Elizabeth A. DNA methylation patterning is a consequence of opposing activities of DNA methyltransferases and DNA demethylases. A 5-methylcytosine DNA glycosylase, ROS1, removes DNA methylation from the Arabidopsis genome. In flowering plants, two distinct female gametes, the egg cell and the central cell, are fertilized, producing what will become the embryo and the endosperm of the seed. In Arabidopsis, a 5-methylcytosine DNA glycosylase, DME, demethylates regions in the central cell genome, leading to methylation differences between maternally- and paternally-inherited endosperm genomes after fertilization. DME is required for endosperm gene imprinting. Homologues of DME include ROS1, DML2 and DML3. It is unknown whether any of these DNA glycosylases are required for endosperm methylation patterning. We show that ROS1 prevents hypermethylation of paternally-inherited alleles in the endosperm at regions that lack maternal or paternal-allele methylation in wild-type. Thus, ROS1 promotes epigenetic symmetry between genomes in the endosperm by preventing paternal genome hypermethylation. We investigated dynamics of DNA methylation at the edges of transposable elements, where ROS1 is known to prevent spreading of DNA methylation into neighboring regions of the genome. We found that DNA methylation spreading in ros1 mutant is unidirectional, which has implications for the field’s understanding of the mechanism of ROS1 activity at TEs as well as the mechanism of methylation establishment at TEs. We have investigated the regulation of ROS1 expression by interaction of ROS1 and the RdDM pathway at the ROS1 promoter. Using a previously characterized deletion in the ROS1 promoter, we investigated the consequences of ROS1 regulation across the genome in the presence of a wild-type RdDM pathway. Finally, I discuss the implications of the work I have done in understanding the role of ROS1 across plant development and the mechanisms by which DNA methylation is patterned in plants, and propose future directions related to these findings.

Conflict between bacteriophages and a mobile genetic element in bacterial immunity

Sat, 01 Feb 2025 00:00:00 GMT

Conflict between bacteriophages and a mobile genetic element in bacterial immunity Loyo, Christian L. Bacteriophages (phages) are the most abundant biological entities on the planet. They are ubiquitous and numerous across the many environments bacteria are found. To combat phage predation, bacteria have evolved numerous immune strategies, so-called anti-phage defense systems. Likewise, phages encode counter-defenses that prevent the function of anti-phage defense systems. Many anti-phage defense systems are found within mobile genetic elements, like plasmids, temperate bacteriophages, and integrative and conjugative elements (ICEs). In this thesis, I show how an ICE in the bacterium Bacillus subtilis, called ICEBs1, protects populations of cells from phage predation by phages in the SPβ family. ICEBs1 has a phage defense system, spbK, which, upon phage infection, causes cell death prior to generating phage progeny. This mechanism of phage defense is considered abortive infection and protects populations of cells via altruistic cell death of infected cells. I show that during SpbK-mediated abortive infection, cells experience NAD⁺ depletion dependent on the Toll-interleukin-1 receptor (TIR) domain of SpbK. Depletion of NAD⁺ likely starves both the cell and infecting phage of energy, killing the cell and preventing the generation of phage progeny. I found that SpbK recognizes phage infection by recognizing and binding to the phage portal protein, YonE, through an interaction between the N-terminus of SpbK and the clip domain of YonE. Furthermore, I show that a gene in the SPβ-like phage Φ3T, nip, was necessary and sufficient to prevent SpbK-mediated anti-phage defense. I found that Nip binds to the TIR domain of SpbK and inhibits NADase activity to prevent abortive infection and enable viable phage production. These findings highlight the conflicts that occur between mobile genetic elements and the co-evolutionary arms race between bacteria and phages.

Essays on the Effects of Immigration on Labor Markets

Thu, 01 May 2025 00:00:00 GMT

Essays on the Effects of Immigration on Labor Markets Gulek, Ahmet This thesis consists of three chapters on the effects of immigration on labor markets. The first chapter studies the effects of an informal labor supply shock on the host regions, the second chapter investigates the spillover effects on non-host regions through the production network, and the third chapter provides a method that the first two chapters rely on. The first chapter studies the effects of Syrian refugees, who are denied work permits and thus can only work informally, on Turkish firms and workers. Using travel distance as an instrument for refugee location, I show that low-skill natives lose both informal and formal salaried jobs. I document two mechanisms: formal firms reduce their formal labor demand and new firms do not enter the formal economy. Estimates imply an elasticity of substitution of 10 between formal and informal workers. Counterfactual exercises predict that granting refugees work permits would have created up to 120,000 formal jobs in the economy through higher informal wages. The second chapter, co-written with Tishara Garg, investigates how immigration-induced wage shocks can propagate beyond the regions receiving immigrants through the production network. Using the Syrian refugee crisis in Turkey as a quasi-experiment and the near universe of domestic firm-to-firm transaction data from VAT records, we show that the immigration shock propagates both forward and backward along the supply chain. Firms in non-host regions who directly or indirectly buy from host regions demand more labor. Firms who sell to host regions weakly increase their sales. Estimates imply an elasticity of substitution between labor and intermediate goods of 0.76 and an elasticity of substitution of nearly 1 between intermediates. Counterfactual analyses show that the spillover effects on non-host regions are economically meaningful when the host regions are central nodes of the domestic trade network. For example, a 1% increase in labor supply in Istanbul decreases real wages in Istanbul by 0.56% and increases real wages in the average non-host city by 0.38%. The third chapter, co-written with Jaume Vives-i-Bastida, proposes a Synthetic Instrumental Variables (SIV) estimator for panel data that combines the strengths of instrumental variables and synthetic controls to address unmeasured confounding. We derive conditions under which SIV is consistent and asymptotically normal, even when the standard IV estimator is not. Motivated by the finite sample properties of our estimator, we introduce an ensemble estimator that simultaneously addresses multiple sources of bias and provide a permutation-based inference procedure. We demonstrate the effectiveness of our methods through a calibrated simulation exercise, two shift-share empirical applications, and an application in digital economics that includes both observational data and data from a randomized control trial. In our primary empirical application, we examine the impact of the Syrian refugee crisis on Turkish labor markets. Here, the SIV estimator reveals significant effects that the standard IV does not capture. Similarly, in our digital economics application, the SIV estimator successfully recovers the experimental estimates, whereas the standard IV does not.

Essays in Political Economy

Thu, 01 May 2025 00:00:00 GMT

Essays in Political Economy Sapiro-Gheiler, Eitan In this thesis, I describe three approaches to political communication and decision-making. Chapter 1, ``Persuasion with Ambiguous Receiver Preferences,'' studies an informed Sender who knows only the average threshold belief needed to persuade a Receiver and wishes to safeguard against unfavorable distributions of individual preferences. Chapter 2, ``Discovery through Trial Balloons,'' examines how correlation between different projects affects information disclosure by a principal who designs a bundle of projects that an agent can then choose to approve. Chapter 3, ``Strategic Opinion-Writing on Appellate Courts,'' describes how and why the partisan composition of quasi-random panels of judges on the U.S. Federal Courts of Appeals affects consensus-building. I describe each chapter in more detail below. The first chapter, ``Persuasion with Ambiguous Receiver Preferences,'' describes a Bayesian persuasion problem where Receiver has a private belief cutoff for Sender’s preferred action and Sender has maxmin preferences over all Receiver type distributions with known mean and bounds. This problem can be represented as a zero-sum game where Sender chooses a mean-preserving contraction of the prior over states and adversarial Nature chooses a Receiver type distribution. I formalize the connection between maxmin persuasion and similar games used to model political spending, all-pay auctions, and competitive persuasion. In both a standard binary-state setting and a new continuous-state setting, Sender optimally linearizes the prior distribution over states to create a distribution of posterior means that is uniform on a known interval with an atom at the lower bound of its support. The second chapter, ``Discovery through Trial Balloons,'' presents a model of a principal and an agent who face symmetric uncertainty about the agent's value for two correlated projects. The principal chooses which project values to publicly discover and makes a proposal to the agent, who accepts if and only if the expected sum of values is positive. I characterize optimal discovery for various principal preferences: maximizing the probability of the grand bundle, of having at least one project approved, and of a weighted combination of projects. My results show when discovering ex-ante disfavored projects may be optimal; these conclusions rationalize the inclusion of controversial policies in omnibus bills and the presence of moonshot projects in organizations. The third chapter, ``Strategic Opinion-Writing on Appellate Courts,'' studies consensus and decision-making by powerful judges on the U.S. Federal Courts of Appeals. Using quasi-random three-judge panels on these courts from 1970\textendash 2013 I document a novel pattern in dissenting opinions: compared to party-unanimous panels, party-mixed panels cause all judges to dissent more often, and at equal rates. This result is incompatible with classical models of judicial politics and is unique to partisanship. To explain my results, I introduce a theoretical framework where judges' favored coalitions are more homogeneous along both partisan and non-partisan dimensions. Using judge metadata, I find suggestive evidence for the model's result that polarization increases dissents by judges of panel-minority law school or gender. With state-of-the-art machine learning tools from natural language processing, I generalize beyond dissents, showing that those same features drive differences in opinion text even when rulings are unanimous.

Uncovering the biogenesis pathways for human mitochondrial alpha-helical outer membrane proteins using genome-wide approaches

Thu, 01 May 2025 00:00:00 GMT

Uncovering the biogenesis pathways for human mitochondrial alpha-helical outer membrane proteins using genome-wide approaches Muthukumar, Gayathri A. Mitochondria are critical double-membraned organelles that act as biosynthetic and bioenergetic cellular factories, with the outer membrane providing an interface with the rest of the cell. In humans, the outer mitochondrial membrane (OMM) contains ~110 different proteins which are encoded in the nuclear genome, synthesized in the cytosol and must be targeted to the membrane. OMM proteins are defined by the secondary structure of their transmembrane domains (TMDs), as two classes – beta-barrel proteins, evolutionarily derived from the outer membranes of gram-negative bacteria, and alpha-helical proteins, an evolutionarily more recent class. Beta-barrel proteins are first translocated into the mitochondrial intermembrane space (IMS) via the translocase of the outer membrane (TOM) and subsequently inserted by the sorting and assembly machinery (SAM) complex. Comparatively, alpha-helical OMM protein biogenesis is poorly understood. Alpha-helical proteins are classified as signal-anchored (a single N-terminal anchored TMD), tail-anchored (a single C-terminal anchored TMD) and polytopic (multiple TMDs), by the number and orientation of their TMDs with respect to the membrane. While the novel OMM insertase MTCH2 was discovered using a genome-wide CRISPRi screen for alpha-helical tailanchored substrates (Guna et. al, 2022), the broader biogenesis and targeting pathways for all biophysically diverse alpha-helical proteins remained unexplored. Critically, the mechanisms of cytosolic chaperoning and targeting for all alpha-helical OMM proteins were unknown. This thesis presents a large-scale investigation that systematically delineates alphahelical biogenesis pathways, from cytosolic chaperoning to membrane insertion to quality control of unassembled or mis-localized TMDs. Genome-wide CRISPRi screens in human cells for varied signal-anchored and polytopic substrates revealed novel cytosolic chaperones, targeting and quality control factors. Arrayed follow-up genetic screens against a large and biophysically more varied panel of substrates revealed that alpha-helical proteins are triaged in the cytosol by TMD number and topology, thus defining a set of ‘rules’ for biogenesis. Cell biological and in vitro biochemistry experiments further discovered a new role for the ribosome-bound chaperone NAC in regulating polytopic protein biogenesis and characterized a novel signal-anchored targeting factor TTC1 that chaperones TMDs using a conserved C-terminal hydrophobic groove. Cumulatively, this work both defines the pathways for biogenesis and quality control of alphahelical OMM proteins and identifies mechanisms by which mitochondrial protein composition and thereby function can be tuned through manipulation of mitochondrial membrane protein biogenesis machinery in diverse pathophysiological conditions.

Development and use of advanced nuclear diagnostics and neural networks to diagnose 3D morphology and power balance in inertial fusion implosions at OMEGA and NIF

Thu, 01 May 2025 00:00:00 GMT

Development and use of advanced nuclear diagnostics and neural networks to diagnose 3D morphology and power balance in inertial fusion implosions at OMEGA and NIF Kunimune, Justin H. Inertial confinement fusion (ICF) is one of several ways to perform nuclear fusion in the laboratory, and is thus appealing as a potential future energy source. Achieving high gain at ICF facilities like the National Ignition Facility (NIF) and OMEGA requires new ways of measuring implosion conditions such as the shape of the shell at minimum-volume and the power balance in the hot-spot. This dissertation describes several novel instruments and analysis techniques to measure these. First is a method to combine information from existing diagnostics that probe asymmetries, such as the neutron imaging system, the real-time neutron activation detectors, and the neutron time-of-flight spectrometers. Our technique uses a forward-fit to a simplified physics model to produce a single self-consistent 3D picture of the implosion. Markov chain Monte Carlo is used to provide robust uncertainty quantification. Second is a knock-on deuteron imager to measure deuterons elastically scattered out of the shell by fusion neutrons. This diagnostic would enable a full 3D reconstruction of both the hot-spot and shell geometry. Analysis procedures were developed for this diagnostic, and commissioning experiments were carried out to validate the procedures and associated hardware, providing improved capabilities for imaging OMEGA implosions. Third is a time-resolved neutron spectrometer called MRSt which would record a time-resolved neutron spectrum. Extensive modelling of the MRSt’s response and analysis procedures has been carried out, with which it has been predicted that the system as designed will meet the top-level physics requirements needed for novel insights. A path forward for implementing this spectrometer has been identified. These projects represent significant advancements in our abilities to diagnose ICF implosions, which will improve our understanding of degradations and failure modes in ICF implosions and lead to higher gain overall. This will hopefully one day enable nuclear fusion energy as a clean energy source.

Essays on Institution and Innovation

Thu, 01 May 2025 00:00:00 GMT

Essays on Institution and Innovation Zhou, Jie This dissertation explores the complex interplay between institutions and innovation across three distinct contexts: digital protectionism, academic governance, and language policy. The first essay examines whether protectionist policies can foster domestic innovation in the digital economy, focusing on China’s Great Firewall (GFW)—the world’s most extensive system of internet censorship. Leveraging the quasi-random timing of foreign app blockages, I find that Chinese substitute apps experienced a 30% increase in user base following foreign app bans. Using novel data extracted from compiled app code, I show that in-house technological development at these firms rose by 14% two years after blockage. This innovation diffused broadly, as both Chinese and foreign apps subsequently adopted more Chinese-origin technologies. I further document that expanded access to user data—enabled by increased data requests and third-party sharing—was a key driver. Quasi-random introductions of new data access types causally boosted in-house development, and firms receiving shared user data also intensified innovation. These findings suggest that digital protectionism, under certain conditions, can catalyze domestic technological growth. The second essay investigates how powerful institutional actors shape academic research and innovation in China. Using data on publications from researchers at 109 top Chinese universities and leadership transitions within these institutions, I apply natural language processing (NLP) techniques to assess alignment between faculty and leader research agendas. Faculty shift their research toward that of incoming leaders—particularly those appointed by the Communist Party—immediately after leadership transitions. This influence is stronger in fields with histories of political control or academic repression. While some alignment may reflect coordination, I find significant costs to research quality: transitions to low-productivity leaders lead to sharp increases in topic similarity and declines in citation impact, especially for research most closely aligned with new leadership. These results highlight the tension between centralized control and research autonomy in high-stakes innovation environments. The third essay explores how language policy affects national identity formation, analyzing Taiwan’s Chinese language unification campaign. Exploiting variation in individuals’ age-based ability to learn Mandarin and their linguistic distance from it, I implement a difference-in-differences design to identify the policy’s long-term effects. I find that cohorts more affected by the policy became more fluent in Mandarin but were less likely to identify as Taiwanese or support self-determination. The intergenerational disruption of native language transmission plays a key role, with the identity impact comparable to 11% of the effect of losing a parent. The policy also increased consumption of state-controlled media among treated cohorts. These findings underscore how language policies can reshape political identity and social cohesion. Together, these essays show that institutions—through mechanisms of control, exclusion, and cultural shaping—play a pivotal role in determining the direction, diffusion, and societal implications of innovation. JEL code: O33, O38, L86, I23, Z13, C23

Machine Learning for Causal Estimation

Thu, 01 May 2025 00:00:00 GMT

Machine Learning for Causal Estimation Quintas-Martínez, Víctor M. The intersection of causal inference and machine learning (ML) has given rise to powerful tools for tackling complex empirical questions, especially in high-dimensional or highly nonlinear settings where traditional methods often fall short. This thesis develops and analyzes novel ML-based methods for estimating causal effects, with a focus on flexibility, robustness, and valid statistical inference. The first chapter addresses the challenge of regularization and model selection bias that arises when ML is used to estimate nuisance parameters. We propose a new framework for automatic debiased machine learning (DML), which we term Riesz regression. This approach constructs debiased estimating equations without requiring explicit characterizations of the debiasing terms, allowing for seamless integration with any ML algorithm. We extend the framework to generalized regressions, including high-dimensional generalized linear models (GLMs). To illustrate its practical value, we apply Riesz regression to a study of discrimination in lending, showing how neural networks can be leveraged for automatic debiasing. Monte Carlo simulations demonstrate that our method frequently outperforms conventional inverse propensity weighting approaches. The second chapter introduces a new method for causal change attribution, which quantifies how different causal mechanisms contribute to shifts in the distribution of an outcome variable over time or across groups. Building on a given causal model, our approach combines regression and re-weighting to identify and estimate the relevant counterfactual quantities. Our methodology is multiply robust, meaning it remains valid even when some components of the model are misspecified. We establish consistency and asymptotic normality. Moreover, we show how our algorithm can be embedded into popular attribution frameworks such as Shapley values, which then inherit its statistical guarantees. Simulation studies confirm the excellent performance of our method, and we demonstrate its utility through an applied case study. The third chapter tackles a common challenge in applied work: estimating and conducting inference on many related causal parameters, such as causal effects of many treatments or on multiple outcomes. We derive uniform error bounds and construct valid simultaneous confidence bands for collections of average treatment effects (ATEs) estimated via DML. Our framework accommodates both finite sets and continua of functionals, and leverages strong Gaussian approximation results to account for dependence across estimates. This enables rigorous simultaneous inference with control over familywise error rates. Together, these contributions advance the state of the art in machine learning for causal estimation by unifying flexible modeling with rigorous inferential theory. The methods developed are broadly applicable to problems in economics, public policy, healthcare, and beyond, where understanding causal relationships in complex, data-rich environments is essential. This thesis emphasizes practical applicability while maintaining strong theoretical guarantees, equipping researchers with tools to make credible, data-driven causal claims. JEL: C14, C21, C45

Essays on Information Economics

Thu, 01 May 2025 00:00:00 GMT

Essays on Information Economics Veiel, Rafael This thesis contains 5 chapters. Every chapter deals with the question of how information affects equilibrium behavior in strategic problems. Chapter 1 is my job market paper "Limits of Global Games.'' It considers the impact of information on equilibrium multiplicity in two-player games of strategic complementarities. Games with strategic complementarities often exhibit multiple equilibria. In a global game, players privately observe a noisy signal of the underlying payoff matrix. As the noise diminishes, a unique equilibrium is selected in almost all binary-action games with strategic complementarities - a property known as "limit uniqueness.'' This chapter describes the limits of that approach in two-player games, as we move beyond two actions. Unlike binary-action games, limit uniqueness is not an intrinsic feature of all games with strategic complementarities. When the noise is symmetric, we demonstrate that limit uniqueness holds if and only if the payoffs exhibit a generalized ordinal potential property. Moreover, we provide an example illustrating how this condition can be easily violated. Chapter 2 is co-authored with Olivier Gossner and is titled "Strategic Type Spaces.'' We provide a strategic foundation for information: in any given game with incomplete information we define strategic quotients as information representations that are sufficient for players to compute best-responses to other players. We prove 1/ existence and essential uniqueness of a minimal strategic quotient called the Strategic Type Space (STS) in which a type is given by an interim correlated rationalizability hierarchy together with the set of beliefs over other players' types and nature that rationalize this hierarchy 2/ that this minimal STS is a quotient of the universal type space and 3/ that the minimal STS has a recursive structure that is captured by a finite automaton. Chapter 3 is also co-authored with Olivier Gossner and is titled "Information Design for Rationalizability.'' We study (interim correlated) rationalizability in games with incomplete information. For each given game, we show that a simple and finitely parameterized class of information structures is sufficient to generate every outcome distribution induced by general common prior information structures. In this parameterized family, players observe signals of two kinds: A finite signal and a common state with additive, idiosyncratic noise. We characterize the set of rationalizable outcomes of a given game as a convex polyhedron. Chapter 4 is co-authored with Stephen Morris and Dirk Bergemann and is titled "A Strategic Topology on Information Structures.'' Two information structures are said to be close if, with high probability, there is approximate common knowledge that interim beliefs are close under the two information structures. We define an "almost common knowledge topology'' reflecting this notion of closeness. We show that it is the coarsest topology generating continuity of equilibrium outcomes. We show that finite information structures are dense in the almost common knowledge topology and thus it is without loss to restrict attention to finite information structures in information design problems. Finally, chapter 5 is a short note describing an information aggregation mechanism that can be used by players before playing a game of strategic complementarities under incomplete information. In such a game, players may have an incentive to share overly optimistic information with other players, thus inducing them to play higher actions. In this mechanism, players trade a token before playing the game. Players who want to communicate good news must purchase this worthless token and burn resources. The note shows that players only need to observe the market clearing price that arises from the token trades to aggregate their private information. Each element in a player's private information set is encoded as a prime in the prime factorization of the market clearing price. The element that is contained in every player's information set is identified as the prime with the highest multiplicity. JEL Classification Codes: C72, D82

Essays on the Economics of Private and Social Insurance

Thu, 01 May 2025 00:00:00 GMT

Essays on the Economics of Private and Social Insurance Solomon, Adam The first chapter, joint with Sylvia Klosin, studies the 'scope' of insurance. Distinct risks are typically insured separately. A single 'aggregate' contract that pays more when many shocks occur simultaneously, but less when positive shocks offset negative shocks, is utility-increasing absent moral hazard. However, an aggregate contract discourages diversification, leading to a novel insurance-incentive trade-off. We study the US Federal Crop Insurance Program (FCIP), where farmers can choose the `scope' of their policy - whether to insure each field separately, or all fields of the crop as an aggregate unit. Starting in 2009, the FCIP introduced a large subsidy increase for aggregate insurance. We show that farms that moved to aggregate insurance reduced crop diversity and irrigation, farmed less and conserved more land, and insured price risk --- all reducing the diversification of their risks. This increased the variability of farm yield by 14%, raising the fiscal cost of aggregate insurance by about $1.5 billion per year. We derive and estimate a formula for the optimal contract scope. We find that an aggregate policy is never welfare maximizing, but that the optimal policy lies partway between separate and aggregate. More generally, we discuss scope's widespread relevance in insurance design. The second chapter, proceeds from the fact that increasing climate risk has caused insurance in many locations to become unaffordable or unavailable. I study a novel policy response in Australian home insurance: government provided, mandatory, actuarially fair, reinsurance for cyclone damage. In this scheme, the government reinsures the cyclone risk, while the private market covers the remaining idiosyncratic risk. I find that public reinsurance leads to a 21% decrease in home insurance premiums and an 11% increase in the probability of insurance being offered at all. In terms of mechanisms, I rule out subsidization and show that the ambiguity of the risk has a minimal impact on premiums and insurance offerings. Instead, the entirety of the increase in insurance offered, and much of the decrease in premiums, comes from reducing the implicit costs associated with insuring spatially correlated risk. Increased competition due to insurer entry explains the remaining premium reductions. This isolates the cause of market dysfunction - correlated risk - and suggests that public reinsurance is a cost-effective policy to rehabilitate insurance markets for catastrophic climate risks. The third chapter, studies bundling in insurance contracts. Every insurance contract bundles risks, and explicit bundling discounts are common. I show theoretically that bundling arises in a competitive market whenever correlation between risk types enables insurer 'cream-skimming': willingness-to-pay for insurance against one risk must be negatively correlated with expected costs from the other risk. I analyze long-term care insurance, in which both-spouse bundles are discounted by 20-35%. I show that cream-skimming incentives are sufficient to explain these discounts, and model-predicted equilibrium bundling discounts closely match empirical discounts. I rule out standard economies-of-scale and differential contract lapsation as alternate explanations of the offered discounts. Counterfactually, banning bundling would raise welfare by 10% by correcting separate-market unraveling, while mandatory family bundling would reduce welfare by 15% by exacerbating advantageous selection.

Essays in Development Economics and Trade

Thu, 01 May 2025 00:00:00 GMT

Essays in Development Economics and Trade Wiles, Edward This thesis comprises three chapters. The first chapter, written with Deivy Houeix, studies search and trust frictions, which have historically made it hard for small firms in lower-income countries to buy inputs from foreign markets. The growth in smartphone ownership and social media usage has the potential to alleviate these barriers. Informed by a dynamic model of relational contracting, we run a field experiment leveraging these technological tools to provide exogenous variation in (1) search frictions and (2) trust frictions (adverse selection and moral hazard) in a large international import market. In our search treatment, we connect a randomly selected 80% of 1,862 small garment firms in Senegal to new suppliers in Turkey. We then cross-randomize two trust treatments that provide additional information about the types (adverse selection) and incentives (moral hazard) of these new suppliers. Alleviating search frictions is sufficient to increase access to foreign markets: in all treated groups, firms are 26% more likely to have the varieties a mystery shopper requests and the goods sold are 30% more likely to be high quality. However, the trust treatments are necessary for longer-term impact: using both transaction-level mobile payments data and a follow-up survey, we show that these groups are significantly more likely to develop the connections into relationships that persist beyond the study. These new relationships lead to increases in medium-run profit and sales. Finally, we use the treatment effects to estimate the model and evaluate counterfactuals where we set various combinations of the frictions to zero, finding that the largest gains come from eliminating adverse selection. The second chapter, written with Habib Ansari and Dave Donaldson, is motivated by a modern revolution in spatial economic modeling that aims to answer quantitative counterfactual questions by using models that feature micro-level heterogeneity. This heterogeneity is then often assumed to come from particular parametric families---such as Frechet in Eaton and Kortum's (2002) Ricardian model. While these parametric choices greatly enhance the tractability of model simulations, it is unknown how sensitive the answers to counterfactual questions are to these assumptions of convenience because there are infinitely many alternative distributions of heterogeneity to be evaluated. We overcome this challenge by building a general trade model that leverages recent advances in the robustness literature. Our method calculates sharp bounds on the values of model counterfactuals that could obtain---while still exactly matching all aggregate trade data points, a gravity-like moment condition, and satisfying equilibrium constraints---under all possible distributions of underlying heterogeneity that lie within a given divergence from a chosen reference distribution. Applying this method to the Eaton and Kortum (2002) model, we find that the gains from trade in these models could be several times larger or smaller than they appear to be under standard benchmark distributions, even if heterogeneity is drawn from a distribution that is at least as similar to Frechet as are the types of parametric alternatives that are commonly explored in sensitivity analysis. The third chapter, written with Tishara Garg, studies regional integration, a major issue both across and within countries. Yet, integration can take many forms, ranging from lowering tariffs to lowering administrative frictions. We provide evidence on the gains to removing administrative frictions using rich microdata on firm-to-firm trade to study a major fiscal integration reform in India. Using an event-study style regression derived from a gravity model, we estimate that the reform increased interstate trade by around 15% on average. We plug this estimate into the model and use it to calculate the aggregate and distributional welfare gains. We find that all but a handful of districts saw welfare gains, with an aggregate welfare increase of around 1%.

Exploring Anisotropic Noise and Fast Gates with Superconducting Qubits

Sat, 01 Feb 2025 00:00:00 GMT

Exploring Anisotropic Noise and Fast Gates with Superconducting Qubits Rower, David A. Rapid recent progress in the engineering of quantum systems across multiple platforms has enabled quantum science at never-before-seen precision and scale, and may yield useful quantum technology. However, two major challenges slow such progress: (1) decoherence from interactions between target systems and uncontrolled external degrees of freedom, and (2) errors in the control of target systems, which often arise from physics beyond the models used to design control protocols. We report on three novel results addressing both coherence and control, utilizing superconducting qubits. Our first result is the characterization of superconducting qubit flux noise, a primary source of decoherence, under the influence of weak, in-plane magnetic fields. We reveal two trends which serve as a novel experimental benchmark for microscopic theories of flux noise: (1) a 1/f to approximately Lorentzian transition in the noise power spectral density below 1 Hz, and (2) noise suppression above 1 MHz. Our second result is the suppression of coherent qubit-control errors induced by the counter-rotating component of strong, linearly-polarized drives. We establish two complementary protocols for mitigating such errors, which previously limited the speed of single-qubit gates for low-frequency qubits. The first protocol realizes circularly-polarized drives in circuit quantum electrodynamics. The second protocol---commensurate pulses---uses pulse-timing restrictions to homogenize counter-rotating errors and enable their mitigation with conventional calibration routines. With commensurate pulses, we demonstrate world-class single-qubit gate fidelities reliably exceeding 99.997%. Our third result is the observation of a novel signature in the decoherence dynamics of qubits subject to anisotropic transverse noise. Through injected noise experiments with a fluxonium qubit, we directly observe time-domain state-purity oscillations at twice the qubit frequency arising from the intrinsic qubit Larmor precession. We probe the oscillation dependence on noise anisotropy, lab-frame orientation, and power spectral density. Such oscillations are a result of physics beyond standard qubit-decoherence models within the rotating-wave approximation, and were previously unobserved in experiment.

Essays in International Macroeconomics

Thu, 01 May 2025 00:00:00 GMT

Essays in International Macroeconomics Gertler, Sarah M. How do exchange rates and tariffs shape the economy? Their effects both independently and in regard to each other are puzzling with respect to classical economic models. This dissertation focuses on how macroeconomic factors -- sticky prices (Chapter 1), interest rates (Chapter 2), and scale economies (Chapter 3) -- inform the scope of exchange rates and tariffs. Unveiling these factors helps illuminate the nature of these shocks' influence. Chapter 1: The first chapter revisits the classic relationship between exchange rate pass-through, how exchange rates influence prices, and expenditure-switching, the resulting substitution between home and foreign goods. Expenditure-switching is the main channel through which exchange rates transmit to the real economy. Conventional wisdom holds that this channel's strength is increasing in exchange rate pass-through into prices: assuming the import demand elasticity is independent of pass-through, larger effects of exchange rates on prices yield larger substitution of spending between domestic and foreign goods. In this paper, I show that this conventional wisdom does not hold. Using confidential US micro-data and a panel-data local projection technique, I show that quantity-exchange rate elasticities are similar across high and low pass-through environments. In essence, low pass-through is subject to a larger import demand elasticity than is high pass-through. I then propose an extension of a standard small open economy New Keynesian model by adding a layer of import buying (retail) firms, in which both exporting and importing firms are subject to price rigidities. I show empirically and theoretically that the ``import buyer rigidity" dampens overall adjustment, but less so under low pass-through because in this case the pass-through is more persistent. The model thus accounts for why the quantity-exchange rate elasticities are similar across pricing regimes. I conclude by exploring the implications of this framework for monetary and exchange rate policy, actually finding a stronger expenditure-switching channel under low pass-through. Chapter 2: The second chapter, joint with Victor Orestes, documents how currency markets and trade flows respond to tariffs imposed by and on the US as related to other countries' macrofinancial position. We show that countries which maintain higher interest rates than the US depreciate much more strongly -- to the point of offsetting the tariffs on impact -- than their low-interest counterparts. However, these effects are not as persistent as the tariff shocks. Our results highlight a US hegemonic asymmetry: tariffs imposed on the US have little effect on currency markets, US demand for high-interest countries' goods is relatively elastic, but the latter's demand for US exports is not. Monetary policy can be an effective tool to target the exchange rate fluctuation as it has a similar incidence as tariffs. Finally, we present evidence that the interest rate analysis could draw from trade-network fundamentals. To rationalize our findings, we modify a baseline model of exchange rate determination using the interest rate as a "sufficient statistic" wedge in fundamentals. Our model indicates that the financial market imperfections we observe in data distort the global response to tariff escalation. Chapter 3: The third chapter proposes an answer to the question of why there is complete long-run pass-through of both tariffs and exchange rates in US exports, despite evidence of flexible markups. I develop a methodology to leverage tariffs and exchange rates to uncover the structural drivers of pass-through, the markup elasticity and the marginal cost scale elasticity. I derive and quantify the scale channel of pass-through, which can be decomposed into a bilateral scale and the novel "shock span" scale effect. The shock span channel arises because different correlation patterns across customers enters prices via the scale channel. Because exchange rates are correlated across trading partners, compared to tariffs they have greater capacity for shock-span effects of scale economies. Quantifying the bilateral and shock span components of the scale channel, the paper demonstrates that scale economies can rationalize the discrepancy between markup flexibility and observed pass-through.

Essays on Technology and Trade

Thu, 01 May 2025 00:00:00 GMT

Essays on Technology and Trade Kikuchi, Shinnosuke This thesis consists of essays on technology and trade. In Chapter 1, I study how technology in the 21st century has changed the pattern of trade. I document that skill-abundant countries no longer have a comparative advantage in skill-intensive sectors. While this empirical relationship was strong in the 1980s, it weakened in the 1990s and disappeared by the 2000s. The decline is more pronounced in countries and sectors with higher automation. I find no such heterogeneous effects among countries and sectors more exposed to offshoring. Using a quantitative trade model incorporating automation and offshoring, I confirm that the observed changes in automation can account for the evolution of comparative advantage while observed changes in offshoring cannot. I conclude by revisiting the relationships between globalization, technology, and inequality through this model. Automation increases skill premia in developed countries with high automation and also raises welfare globally, whereas offshoring leads to smaller, more evenly distributed welfare gains. In Chapter 2 (joint with Daniel G. O'Connor), we turn to the geographic consequences of technology and trade by analyzing the role of granularity—the dominance of a few large firms in local labor markets. We propose a new economic geography model featuring granular firms subject to idiosyncratic shocks. We show that average wages increase in the size of the local labor market due to that granularity, and provide a sufficient statistic for the contribution of our mechanism. We further prove that too few firms enter in equilibrium. Using Japanese administrative data on manufacturing, we provide evidence consistent with our mechanism and quantify it. Our mechanism implies that markets with around 2 firms per sector have an elasticity of wages to population of 0.05 and firms capture only 85% of their contribution to production in profits. In large markets like Tokyo, the elasticity is around 0.001, and firm entry is approximately efficient. Enacting optimal place-based industrial policy would increase the number of firms in modest-sized cities by more than 30% and actually decrease the number of firms and people in Tokyo. In Chapter 3 (joint with Sagiri Kitao), we study the distributional consequences of technological and trade-induced polarization—wage and employment losses of middle-class workers relative to low- and high-skill groups. We build a model of overlapping generations who choose consumption, savings, labor supply, and occupations over their life-cycles, and accumulate human capital. We simulate a wage shift observed since the early 1980s and investigate individuals' responses. Polarization improves welfare of young individuals that are high-skilled, while it hurts low-skilled individuals across all ages and especially younger ones. The gain of the high-skilled is larger for generations entering in later periods, who can fully exploit the rising skill premium.

Regulation of Chromatin Landscape on and by the Human Sex Chromosomes

Thu, 01 May 2025 00:00:00 GMT

Regulation of Chromatin Landscape on and by the Human Sex Chromosomes Bokil, Neha Vijay Sex chromosome constitution is the largest and oldest source of genetic variation in the human population. One sex chromosome—the “active X” (Xa)—is present in all individuals. The second sex chromosome differs between sexes; most males have a Y while most females have a second X, which adopts a distinct conformation from Xa and is termed the “inactive X” (Xi). Despite its name, the human Xi expresses ~20% of its genes. Xi-expressed genes and their Y homologs play critical gene regulatory roles. Examining mechanisms and effects of Xi gene expression is essential to understanding these functions. In this thesis, I investigate chromatin landscape across the human Xi to identify features of Xi-expressed and Xi-silent genes; I also interrogate the role of an Xi-expressed gene and its Y homolog in regulating chromatin genome-wide. To examine chromatin state differences between Xi-expressed and Xi-silent genes, we quantified H3K4me3, H3K27me3, and CTCF along Xi by linear modeling in cells of individuals with zero to three Xis. We demonstrate that Xi-expressed genes are enriched for H3K4me3 compared to Xi-silent genes. Moreover, Xi-silent genes near strongly Xi-expressed genes have higher H3K27me3 than other Xi-silent genes. CTCF shields strongly Xi-expressed gene promoters from surrounding heterochromatin. We propose a framework associating combinations of chromatin marks with subcategories of Xi-expressed and Xi-silent genes. A key Xi-expressed gene, KDM6A, encodes an H3K27me3 demethylase—enabling Xi to impact chromatin structure genome-wide. Its Y homolog, UTY, is thought to encode a catalytically dead enzyme. However, we demonstrate that Xi and Y copy number-dependent changes to H3K27me3 across autosomes are strongly correlated. Moreover, KDM6A knockdown results in increased H3K27me3 at similar genomic regions as UTY knockdown. We posit that KDM6A and UTY share demethylase-dependent functions. Deciphering features and genome-wide effects of Xi expression is essential to understanding fundamental mechanisms of gene regulation and the shared and differential roles of the sex chromosomes outside the reproductive tract. This work highlights critical chromatin-level differences between Xi-silent and Xi-expressed genes, the effects of an Xi-expressed gene on chromatin structure genome-wide, and striking similarities between Xi and Y in modulating autosomal chromatin structure and gene expression.

Essays on Econometrics and Policy Evaluation

Thu, 01 May 2025 00:00:00 GMT

Essays on Econometrics and Policy Evaluation Vives-i-Bastida, Jaume This thesis consists of four chapters that study the statistical properties of synthetic control methods and their application to public policy evaluation and the digital economy. The first chapter, co-written with Ahmet Gulek, proposes a Synthetic Instrumental Variables (SIV) estimator for panel data that combines the strengths of instrumental variables and synthetic controls to address unmeasured confounding. We derive conditions under which SIV is consistent and asymptotically normal, even when the standard IV estimator is not. Motivated by the finite sample properties of our estimator, we introduce an ensemble estimator that simultaneously addresses multiple sources of bias and provide a permutation-based inference procedure. We demonstrate the effectiveness of our methods through a calibrated simulation exercise, two shift-share empirical applications, and an application in digital economics that includes both observational data and data from a randomized control trial. In our primary empirical application, we examine the impact of the Syrian refugee crisis on Turkish labor markets. Here, the SIV estimator reveals significant effects that the standard IV does not capture. Similarly, in our digital economics application, the SIV estimator successfully recovers the experimental estimates, whereas the standard IV does not. The second chapter, co-written with Ignacio Martinez, proposes a Bayesian alternative to the synthetic control method and explores the frequentist properties of the method in the context of linear factor models. In this chapter, we characterize the conditions on the factor model primitives (the factor loadings) for which the statistical risk minimizers are synthetic controls (in the simplex). Then, we propose a Bayesian alternative to the synthetic control method that preserves the main features of the standard method and provides a new way of doing valid inference. We explore a Bernstein-von Mises style result to link our Bayesian inference to the frequentist inference. For linear factor model frameworks we show that a maximum likelihood estimator (MLE) of the synthetic control weights can consistently estimate the predictive function of the potential outcomes for the treated unit and that our Bayes estimator is asymptotically close to the MLE in the total variation sense. Through simulations, we show that there is convergence between the Bayesian and frequentist approach even in sparse settings. Finally, we apply the method to re-visit the study of the economic costs of the German re-unification and the Catalan secession movement. The Bayesian synthetic control method is available in the bsynth R-package. The third chapter, recognizes that synthetic control methods often rely on matching pre-treatment characteristics (called predictors) of the treated unit, and that the choice of predictors and how they are weighted plays a key role in the performance and interpretability of synthetic control estimators. This chapter proposes the use of a sparse synthetic control procedure that penalizes the number of predictors used in generating the counterfactual to select the most important predictors. I derive, in a linear factor model framework, a new model selection consistency result and show that the penalized procedure has a faster mean squared error convergence rate. Through a simulation study, I then show that the sparse synthetic control achieves lower bias and has better post-treatment performance than the unpenalized synthetic control. Finally, I apply the method to revisit the study of the passage of Proposition 99 in California in an augmented setting with a large number of predictors available. The fourth chapter, co-written with Alberto Abadie, proposes a set of simple principles to guide empirical practice in synthetic control studies. The proposed principles follow from formal properties of synthetic control estimators, and pertain to the nature, implications, and prevention of over-fitting biases within a synthetic control framework, to the interpretability of the results, and to the availability of validation exercises. We discuss and visually demonstrate the relevance of the proposed principles under a variety of data configurations. JEL: C23, C26, C11, C52.

Energy Flow in Particle Collisions

Tue, 01 Sep 2020 00:00:00 GMT

Energy Flow in Particle Collisions Metodiev, Eric Mario In this thesis, I introduce a new bottom-up approach to quantum field theory and collider physics, beginning from the observable energy flow: the energy distribution produced by particle collisions. First, I establish a metric space for collision events by comparing their energy flows. I unify many ideas spanning multiple decades, such as observables and jets, as simple geometric objects in this new space. Second, I develop a basis of observables by systematically expanding in particle energies and angles, encompassing many existing observables and uncovering new analytic structures. I highlight how the traditional criteria for theoretical calculability emerge as consistency conditions, due to the redundancy of describing an event using particles rather than its energy flow. Finally, I propose a definition of particle type, or flavor, which makes use of only observable information. This definition requires refining the notion of flavor from a per-event label to a statistical category, and I showcase its direct experimental applicability at colliders. Throughout, I synthesize concepts from particle physics with ideas from statistics and computer science to expand the theoretical understanding of particle interactions and enhance the experimental capabilities of collider data analysis techniques.

A Preoptic Neurocircuit that Regulates Blood Glucose Homeostasis

Sat, 01 Feb 2025 00:00:00 GMT

A Preoptic Neurocircuit that Regulates Blood Glucose Homeostasis Roessler, Julian McFadden The preoptic area (POA) is the core thermoregulatory center of all known endothermic species and balances heat generation and cooling in response to environmental stimuli. This delicate balance is executed via a brain-body exchange of sensory information and thermoregulatory output that is intimately connected to the nutritional state of the organism. When faced with food deprivation, certain endotherms engage in torpor, a behavior in which body temperature and metabolic rate are substantially depressed to improve the probability of organismal survival. Induction of torpor is regulated by anteroventral POA (avPOA) Vglut2⁺/Adcyap1⁺ neurons, which are necessary and sufficient to induce this state. How these neurons regulate the metabolic depression observed during torpor remains poorly understood. In this work, we show that activation of avPOA_Vglut2/PACAP neurons results in temperature-independent changes in whole-body changes in fuel usage, from glucose to fatty acids, driven predominantly via insulin signaling defects in skeletal muscle. This metabolic shift is executed via engagement of the hypothalamic-pituitary-adrenal axis, and impairment of this process via silencing of avPOA_Vglut2/PACAP neurons results in a loss of fasting glucose homeostasis. Taken together these results nominate torpor-associated avPOA_Vglut2/PACAP neurons as core regulators of glucose homeostasis, and provide a basis for understanding how endotherms utilize hierarchical control of metabolism to tune energy expenditure and survive extreme periods of energetic deprivation.

Characterizing population-level variation in mRNA splicing and implications for human genetic interpretation

Thu, 01 May 2025 00:00:00 GMT

Characterizing population-level variation in mRNA splicing and implications for human genetic interpretation Jacobs, Hannah N. Alternative splicing is when a single gene sequence gives rise to multiple RNA sequences. DNA mutations in this gene sequence can alter this process, shifting the relative usage of RNA sequences. This relative usage is called percent spliced in (PSI). Sometimes changes in PSI triggers a change in function, happening at the level of a cell, organism, or of fitness. The consequences of splicing variability, and the contribution of genetic variation to this process, remains incompletely characterized. In this thesis, we seek to characterize the splicing events specifically present in a subset of the human population. We use the Genotype-Tissue Expression project (GTEx), which encompasses genomic DNA sequence information and bulk mRNA data from 49 tissues in 838 individuals. In this dataset we implement a 3-component beta-binomal model using RNA-sequencing reads, at a tissue-specifc level, to reliably call splicing events present in a subset of the samples within a tissue. We call these naturally variable exons (NVEs), and identify a total 57,271 unique NVEs in GTEx. We find NVEs in a large portion of the transcriptome, existing in 75% of all protein-coding genes. The beta-binomal model generates a population distribution of each NVE and we leverage that to estimate an NVE frequency at a PSI level of interest. This enables us to compare NVEs by their frequencies. We find that NVEs either tend to be rare in frequency ( ≤ 10%) in the population) or quite high in frequency ( ≥ 90%). We find that NVEs tend to be in 5' untranslated regions at higher frequencies, and tend to be in coding regions at lower frequencies. 60% of NVEs have been previously found to be modulated by genetic variants. We find that proximity to a splice site is one of the most important predictors in predicting if a genetic variant will impact splicing in GTEx, which enables better predictions over existing methods (increase in AUC by 0.39). Surprisingly, we find that NVEs tend to be in genetically constrained genes (depleted of loss-of-function mutations), with the lowest frequency NVEs occurring in the most constrained genes. We find a subset of genetically-modified NVEs that target genes in a manner consistent with inducing nonsense-mediated decay (NMD). We highlight a couple of such variants linked to diseases, such as those associated with heart disease. These findings demonstrate that quantifying the population frequency of splicing events can reveal novel axes of molecular variability, and provide potential insight into the evolution of alternative splicing.

Biochemical Characterization of the DUF3328 Protein in the Biosynthesis of Cyclic Peptide Cyclochlorotine

Thu, 01 May 2025 00:00:00 GMT

Biochemical Characterization of the DUF3328 Protein in the Biosynthesis of Cyclic Peptide Cyclochlorotine Huang, Wentao Cyclic peptide natural products are valuable sources for medicine. They exhibit significant biological and chemical diversity. Cyclochlorotine, a fungal cyclic pentapeptide produced by Talaromyces islandicus, possesses unique structural modifications, including dichlorination and hydroxylation, yet the enzymatic basis for these transformations remains poorly understood. This study biochemically characterizes the Domains of Unknown Function 3328 (DUF3328) protein family and investigates its role in cyclochlorotine biosynthesis. Through transcriptomic sequencing and CRISPR/Cas9 knockout experiments, I revealed that CctP2 is essential for chlorination and CctR is required for hydroxylation. Computational sequence and structural analyses using AlphaFold suggested that DUF3328 proteins contain a conserved HxxHC(x)nHxxHC motif, a putative metal-binding site. Structural modeling further indicated that DUF3328 proteins form disulfide-linked homodimers, an unusual feature among biosynthetic enzymes. To elucidate their biochemical roles, I purified CctR and CctP2 from Sf9 insect cells, overcoming challenges posed by their membrane association and intrinsic disorder. In vitro assays demonstrated that CctR is a copper-dependent enzyme that hydroxylates cyclochlorotine, and dimerization is essential for the activity. Mechanistic studies using isotopic labeling confirmed dioxygen as the oxygen source. Copper redox cycling was found to be essential, with Cu(I) required for the catalysis. This work establishes DUF3328 proteins as a new class of copper-dependent enzymes involved in fungal secondary metabolism. The discovery of their catalytic mechanisms expands our understanding of enzymology and provides a foundation for future enzyme characterization in this family. More broadly, this study highlights the power of computational tools such as AlphaFold in guiding the functional characterization of previously uncharacterized protein families.

Essays on Spatial Economics

Thu, 01 May 2025 00:00:00 GMT

Essays on Spatial Economics O'Connor, Daniel This thesis comprises three chapters, each studying optimal policy in a model of spatial economics. The first chapter considers the policy problem of a country looking to influence the geopolitical actions of another country. The second chapter considers how a central government should design place-based transfers to fight local recessions. And the third chapter considers how granularity affects the geography of economic activity and what that might mean for optimal place-based policy. In the first chapter (joint with John Sturm Becko), we suppose a country anticipates that it may use trade as a point of leverage in future geopolitical conflicts. How should it develop domestic industries and international trading relationships today in order to strengthen its hand tomorrow? Domestically, we show that the country should abstain from peacetime industrial policies if it can credibly threaten trade taxes as geopolitical punishments during conflict, but not otherwise. Internationally, its peacetime trade policy should promote the accumulation of foreign capital that makes foreign prices---not foreign welfare---more sensitive to trade during conflict. We apply these insights to provide the first quantitative exploration of the US's optimal policies for building geopolitical power vis-à-vis China. The optimal policy promotes US-China trade on both the import and export margins. In the second chapter, I note that many regions in the US experience depressed labor demand and high unemployment, even when the rest of the United States does not. How should the US government respond? In this chapter, I characterize optimal place-based transfers in a dynamic economic geography model with nominal wage rigidity and compare them to observed government transfers. I show that transfers not only have a stimulus effect—by boosting local demand—but also a migration effect—by encouraging local residents to stay. Analytically, I provide optimal transfer formulas that capture this trade-off and show, perhaps surprisingly, that the optimal transfer to a distressed region may be a tax due to the migration effect. All else equal, transfers should be larger in the short-run and when there are distressed regions nearby. Quantitatively, I find that observed transfers are both too small in the short-run and too large in the medium-run, achieving just over half of the gains from the fully optimal response to idiosyncratic local shocks. I conclude by exploring how the US government could have responded to the China trade shock in the 2000s. In the third chapter (joint with Shinnosuke Kikuchi), we ask how does the fact that individual firms dominate labor markets affect the geography of economic activity? And what does it mean for the efficiency of firm entry? To answer these questions, we propose a new economic geography model featuring granular firms subject to idiosyncratic shocks. We show that average wages increase in the size of the local labor market due to that granularity, and provide a sufficient statistic for the contribution of our mechanism. We further prove that too few firms enter in equilibrium. Using Japanese administrative data on manufacturing, we provide evidence consistent with our mechanism and quantify it. Our mechanism implies that markets with around 2 firms per sector have an elasticity of wages to population of 0.05 and firms capture only 85% of their contribution to production in profits. In large markets like Tokyo, the elasticity is around 0.001, and firm entry is approximately efficient. Enacting optimal place-based industrial policy would increase the number of firms in modest-sized cities by more than 30% and actually decrease the number of firms and people in Tokyo. JEL Codes: F1, E3, R1.

Development of Novel Technologies to Investigate DNA Double-Strand Break Repair Uncovers a Role for the ATM Kinase in Error-Free NHEJ with Implications for Neurodegenerative Diseases

Thu, 01 May 2025 00:00:00 GMT

Development of Novel Technologies to Investigate DNA Double-Strand Break Repair Uncovers a Role for the ATM Kinase in Error-Free NHEJ with Implications for Neurodegenerative Diseases Kruswick, Alex J. DNA double strand breaks (DSBs) are considered to be the most lethal genotoxic lesion because they can result in chromosomal translocations or result in a major loss of genetic information if repaired incorrectly. To preserve genomic integrity, mammalian cells have evolved a set of complimentary and redundant repair pathways that faithfully repair DSBs. Consequently, eukaryotic cells utilize an evolutionary conserved set of protein kinase signaling pathways that recognize and respond to DNA damage by pausing cell cycle progression and recruiting DNA repair machinery to ultimately determine the fidelity of DSB repair. Mutations and/or acquired defects that compromise the function of DNA damage response (DDR) pathways result in enhanced mutagenesis and underlie the development and progression of cancer and neurodegenerative conditions. How cells chose which DSB repair pathways to use when fixing a DSB in order to maximize repair fidelity is incompletely understood. To better understand how cells decide which repair pathway to use when fixing DSBs and to specifically investigate protein kinase signaling that coordinates DSB repair pathway selection, we developed a set of multicolor fluorescent reporter systems, named DSB-Spectrum and DSB-Prism. DSB-Prism is uniquely designed to report on the choice between DSB repair via error-free non-homologous end joining (EF-NHEJ), mutagenic end joining (mut-EJ), alternative end joining (alt-EJ), homologous recombination (HR), and single strand annealing (SSA) at a single break created within individual cells by CRISPR-Cas9. We demonstrate that DSB-Prism robustly reveals patterns of DSB repair pathway compensation following chemical inhibition or genetic perturbation of DDR repair factors. We report that the majority, but not all, EF-NHEJ repair requires DNA-PKcs. We observed that DNA-PKcs kinase activity is essential for its function in EF-NHEJ repair, while autophosphorylation of DNA-PKcs on the previously mapped ABCDE phosphorylation site cluster plays only a minor role in this process primarily through the Ku80 DNA-PKcs long range synaptic complex. We utilized DSB-Prism to uncover a novel role for the ATM kinase in promoting EF-NHEJ repair at highly transcribed genes. We show that ATM promotes EF-NHEJ repair via two genetically distinct pathways independently of DNA-PKcs kinase signaling. First, ATM promotes EF-NHEJ through a phosphorylation-dependent interaction between 53BP1 and RIF1 independently of the Shieldin and CST complexes, which we propose serves to physically hold DSB ends together in a redundant manner with the core NHEJ-mediated end synapsis machinery. Second, we propose that ATM promotes EF-NHEJ via promoting R-loop resolution by both SETX and ERCC6L2. We show that the role of ATM in promoting EF-NHEJ is largely independent of MRN-dependent ATM activation, and completely independent of ROS-dependent ATM activation. We discover a novel N-terminal set of positively charged residues that we propose directly interact with the negatively charged DNA phosphate backbone adjacent to DSBs in order to activate ATM. These N-terminal positively charged residues, in combination with MRN, promote binding of ATM to chromatin but are particularly important for ATM’s function in promoting EF-NHEJ repair within the DSB-Prism reporter. Finally, we characterized a cohort of ATM patient mutations and observe that the ability of ATM mutants to promote EF-NHEJ perfectly correlates with patient clinical A-T disease severity. We propose that this loss of EF-NHEJ repair is a major mechanistic cause of Purkinje cell death and cerebellar neurodegeneration observed in A-T patients.

Understanding the Mechanisms that Determine the Edge Electron Density Profile in Tokamaks

Thu, 01 May 2025 00:00:00 GMT

Understanding the Mechanisms that Determine the Edge Electron Density Profile in Tokamaks Miller Hernández, Marco Andrés The interaction between the physics of plasma turbulence and that of atomic neutral dynamics, intrinsic to the tokamak edge, makes prediction of edge profiles difficult. It is unclear to what extent neutral ionization, as opposed to particle transport, is responsible for the build up of edge density gradients. To this end, this thesis combines electron and neutral measurements across the edge region with high-fidelity simulations of neutrals to study these processes in high density and high magnetic field plasmas on Alcator C-Mod. This is enabled by measurements of Lyman-α (Lyₐ) emission made by the LYMID camera, as well as measurements of electron density, nₑ, and electron temperature, Tₑ, by the edge Thomson scattering (ETS) system. These result in a large database of inferred neutral density, n₀ and ionization source, S_ion, as well as radial particle flux, Γ_D, and effective diffusivity, D_eff, for stationary periods. For selected discharges, these are used to impose additional constraints to simulations of neutral dynamics in the plasma edge using SOLPS-ITER. This methodology is used to examine stiffness in the edge gradients forming the so-called “pedestal" in the high-confinement mode (H-mode) in response to increased ionization. This phenomenon is found to be associated with changes to local particle transport, and is observed to be correlated with a local parameter governing the influence of turbulence from interchange instabilities as opposed to that resulting from drift-waves. Reaching the threshold in this parameter may be avoided through improved particle control and is found to also be highly dependent on the 2D distribution of neutrals in the unconfined plasma region. The competition between interchange modes and the drift-wave is probed on Alcator C-Mod through validation of a semi-empirical model for tokamak operational boundaries. The separatrix operational space (SepOS) model [Eich & Manz, Nuclear Fusion (2021)] predicts boundaries for the L-H transition, the L-mode density limit, and the ideal MHD ballooning limit in terms of plasma quantities evaluated using separatrix parameters for a wide range of Alcator C-Mod plasmas. These boundaries are expressed in terms of dimensionless quantities borrowed from electromagnetic fluid drift turbulence (EMFDT) theory. The combined workflow of ETS and LYMID also allows for evaluation of quantities associated with plasma transport in connection with the plasma operational space. Experimental evidence of changes to particle transport near the boundaries is provided for the first time. Organization of Γ_D at the separatrix is observed in both H-modes and low-confinement modes (L-mode) for key dimensionless parameters. The model is also used to elicit the physics of high confinement regimes free of Type-I edge localized modes (ELMs). Databases of the transition to the improved-confinement (I-mode) and that between the Type-I ELMy H-mode and the Enhanced Dα (EDA) H-mode are studied using the SepOS framework. An empirical model for particle transport in the EDA H-mode to explain pedestal saturation in this regime is developed and tested. The findings are then leveraged for modeling of next-generation devices, with priority on core-edge integration and improved power handling. high confinement regimes free of Type-I edge localized modes (ELMs). Databases of the transition to the improved-confinement (I-mode) and that between the Type-I ELMy H-mode and the Enhanced Dₐ (EDA) H-mode are studied using the SepOS framework. An empirical model for particle transport in the EDA H-mode to explain pedestal saturation in this regime is developed and tested. The findings are then leveraged for modeling of next-generation devices, with priority on core-edge integration and improved power handling.

Essays on Economic Growth and Innovation

Thu, 01 May 2025 00:00:00 GMT

Essays on Economic Growth and Innovation Lensman, Todd A foundational observation by Robert Solow holds that long-run economic growth is primarily driven by the innovation and adoption of new technologies (Solow, 1957). This set of essays provides new theory and evidence to explain how firms choose which technologies to innovate and adopt. A point of emphasis, particularly in the first two chapters, is that complementarities across firms play an important role in determining the rate and direction of technological change. These complementarities arise as firms build shared knowledge by innovating (Chapter 1) and from joint consumption of new products (Chapter 2). They provide a new channel through which market structure and property rights affect long-run technological change. Chapter 1. The first chapter is motivated by the observation that the direction of innovation shapes both current technologies and future innovation opportunities, as firms acquire expertise and create public knowledge through discovery. But how do firms choose which technologies to develop? Do they ever fail to exploit new technological paradigms? I build a new model of innovation and firm dynamics to study a novel link between market structure, the direction of innovation, and economic growth: Expertise in a current technology gives incumbents a comparative advantage at innovating it relative to entrants, who instead favor a new technology with higher growth potential. Each firm’s innovation decisions influence others through knowledge spillovers, so the initial market structure can affect the long-run direction of innovation. Concentrating R&D resources in a small number of firms allows faster accumulation of expertise, raising growth when all firms innovate the same technology. But it can lower growth when firms face a technology choice, amplifying the influence of incumbents and potentially delaying or preventing the emergence of the new technology. I provide empirical evidence for the theory using data on firm patenting and R&D expenditures. I also show that it explains the historical development of mRNA vaccines, and I explore its implications for the highly concentrated innovation of artificial intelligence. Chapter 2. In the second chapter, joint with Rebekah Dix, we observe that innovations often combine several components to achieve outcomes greater than the “sum of the parts.” We argue that such combination innovations can introduce an understudied inefficiency—a positive market expansion externality that benefits the owners of the components. We demonstrate the importance of this externality in the market for pharmaceutical cancer treatments, where drug combination therapies have proven highly effective. Using data on clinical trial investments, we document several facts consistent with inefficiently low private innovation: firms are less likely than publicly funded researchers to trial combinations, firms are less likely to trial combinations including other firms’ drugs than those including their own drugs, and firms often wait to trial combinations including other firms’ drugs until those drugs experience generic entry. Using microdata on drug prices and utilization, we quantify the externalities that arise from new combinations and find that the market expansion externality often dominates the standard negative business stealing externality, suggesting too little innovation in combination therapies. As a result, firms may have incentives to free ride off others’ innovation, which we analyze with a dynamic structural model of innovation decisions. We use the model to design cost-effective policies that advance combination innovation. Redirecting publicly funded innovation toward combinations with high predicted market expansion or consumer surplus spillovers minimizes crowd out of private investments, increasing the rate of combination innovation and total welfare while remaining budget neutral. Chapter 3. The final chapter, joint with Daron Acemoglu, considers incentives to adopt transformative technologies that promise to accelerate productivity growth across many sectors but also present new risks from potential misuse. We develop a multi-sector technology adoption model to study the optimal regulation of transformative technologies when society can learn about these risks over time. Socially optimal adoption is gradual and typically convex. If social damages are large and proportional to the new technology’s productivity, a higher growth rate paradoxically leads to slower optimal adoption. Equilibrium adoption is inefficient when firms do not internalize all social damages, and sector-independent regulation is helpful but generally not sufficient to restore optimality.

Essays in Industrial Organization

Thu, 01 May 2025 00:00:00 GMT

Essays in Industrial Organization Dix, Rebekah A. This thesis comprises three essays on industrial organization. The first chapter, joint with Todd Lensman, studies the innovation of cancer drug combination therapies. Innovations often combine several components to achieve outcomes greater than the “sum of the parts.” We argue that such combination innovations can introduce an understudied inefficiency—a positive market expansion externality that benefits the owners of the components. We demonstrate the importance of this externality in the market for pharmaceutical cancer treatments, where drug combination therapies have proven highly effective. Using data on clinical trial investments, we document several facts consistent with inefficiently low private innovation: firms are less likely than publicly funded researchers to trial combinations, firms are less likely to trial combinations including other firms’ drugs than those including their own drugs, and firms often wait to trial combinations including other firms’ drugs until those drugs experience generic entry. Using microdata on drug prices and utilization, we quantify the externalities that arise from new combinations and find that the market expansion externality often dominates the standard negative business stealing externality, suggesting too little innovation in combination therapies. As a result, firms may have incentives to free ride off others’ innovation, which we analyze with a dynamic structural model of innovation decisions. We use the model to design cost-effective policies that advance combination innovation. Redirecting publicly funded innovation toward combinations with high predicted market expansion or consumer surplus spillovers minimizes crowd out of private investments, increasing the rate of combination innovation and total welfare while remaining budget neutral. The second chapter, joint with Kelsey Moran and Thi Mai Anh Nguyen, studies the interoperability of electronic health record systems. Interoperability—the ability of different systems to work together—is an increasingly vital component of product markets. We study the impact of interoperability frictions in the context of US hospital Electronic Health Record (EHR) systems. While use of EHR systems is widespread, interoperability of these systems remains low, particularly across those produced by different EHR vendors. We examine how interoperability affects patients by considering both a direct, technological effect of influencing health information exchange and an allocative effect of shifting the flow of patients across providers. Using an event study design in which interoperability between hospital pairs changes when one changes EHR vendors, we find evidence for both channels. When two hospitals switch to having the same EHR vendor, charges and readmissions rates for patients who are transferred and referred between them decrease by 4% and 11%, respectively. In addition, these hospitals now share 8% more inpatient transfers and 9-10% more referrals. This change in patient flows further affects patient outcomes: patient health improves when their sending hospitals switch to EHR vendors used by higher-quality hospitals in the market and worsens when the opposite occurs. To quantify the welfare gain from reducing interoperability frictions, we estimate a demand model of how patients and providers trade-off interoperability with other receiving hospital characteristics when choosing where to send patients. The model is identified by changes in patient flows following changes in hospital EHR vendors and interoperability levels. We show that eliminating all interoperability frictions would redirect 7.5% of patients to different hospitals and increase joint hospital-patient welfare by 21%, the equivalent of a 57-kilometer reduction in travel distance. The third chapter, joint with Roi Orzach, studies the relationship between the fares of direct and connecting flights. Airlines operate complicated flight networks, often utilizing hub-and-spoke systems to efficiently route connecting travelers and optimize costs. Despite the prevalence of connecting travelers—accounting for approximately one-third of passenger itineraries—most analyses of the welfare effects of changes in competition focus on nonstop routes. We show that when firms face capacity constraints or adjustment costs, a price decrease on a direct route may incentivize firms to decrease prices on indirect routes using this route as a leg. We document that this pass-through is positive using the price effects of low-cost carrier entry and airline mergers: connecting fares decrease after low-cost carrier entry on one of the legs and increase after a merger of carriers that competed on one of the legs. Our findings demonstrate that ignoring these network effects leads to significantly underestimating changes in consumer surplus—by up to 115%—in response to changes in competition. Thus, considering full airline networks is essential to accurately estimating the impact of changes in competition on consumers.

Essays in Environmental and Supply Chain Topics in Finance

Thu, 01 May 2025 00:00:00 GMT

Essays in Environmental and Supply Chain Topics in Finance Zhang, Henry H. This thesis comprises three essays in finance. The first two essays study how liquidity provision by the financial sector affects firms’ production decisions in response to shocks. The third essay studies the real and financial impacts of regulatory enforcement in an environmental setting. The first chapter (joint with Victor Orestes and Thiago Christiano Silva) shows that firms experience large increases in sales and purchases after receiving cheaper liquidity. We focus on factoring, defined as the supplier-initiated sale of receivables. In Brazil, receivables funds (FIDCs) securitize receivables for institutional investors. By assembling a novel transaction-level dataset of factoring with other credit operations for all registered firms and FIDCs, we construct a shift-share instrument for factoring financing supply based on FIDC flows. We then use a novel combination of electronic payments, trade credit, and employer-employee matched data to estimate the impacts. A flow-induced increase in receivables demand reduces firms’ factoring interest rate. In response, firms demand more permanent labor and less temporary labor. In our model, these effects arise from factoring’s purpose of reducing cash inflow volatility, helping firms match inflows to outflows, which firms otherwise achieve at an efficiency cost through substitution across labor types.. The second chapter (joint with Victor Orestes and Thiago Christiano Silva) uses transaction-level data on payments, credit, and insurance to examine how Brazilian farmers responded to the severe frost of July 2021, a shock that affected coffee, a perennial crop whose plants are a major component of farm value. The frost shock reduced both output and the pledgeable value of farmers’ collateral. We find that insured farmers increased investment in the years following the shock, while uninsured farmers reduced investment and borrowing. We show how this pattern is consistent with models of imperfect pledgeability of a firm’s collateral, where constrained firms neither insure (ex-ante) nor fully recover from a shock (ex-post). Limited commitment endogenously generates under-insurance through the combination of upfront payment of the insurance premium with the tightening of borrowing constraints post-shock due to the decrease in total collateral. We discuss two equilibrium implications of this mechanism: the inefficacy of emergency credit lines in targeting liquidity constrained firms and the amplification of output volatility from the rising risk of extreme weather shocks. The third chapter (joint with Ananya Kotia and Utkarsh Saxena) studies the aggregate impacts of court-ordered iron ore mining bans in India. The local sectoral ban is a command-and-control (CAC) policy that is commonly applied to natural resource settings, usually when the regulator has a signal of widespread non-compliance. The Supreme Court of India imposed bans on iron ore mining and outbound iron ore trade in two states in response to reports that mines operated under fake environmental permits and underpaid mining royalties. Using firm-level industrial survey data, mine-level output data, we decompose the bans’ effects into trade, production networks, and local labor demand channels. Our results indicate persistent declines in employment, capital stock, and borrowing by iron-consuming plants, despite the temporary duration of the ban. These findings highlight the economic spillovers caused by CAC policies, especially in industries that are upstream in the supply chain.

Flexibility in Platform Operations

Thu, 01 May 2025 00:00:00 GMT

Flexibility in Platform Operations Zhao, Jiayu This thesis studies how modern service platforms, through algorithmic and market design, leverage agents’ flexibility to enhance operational efficiency. The last decade has witnessed the booming growth of such platforms in ride-hailing (e.g., Uber), e-commerce (e.g., Amazon), and hospitality (e.g., OpenTable). A central operational challenge in these systems lies in the heterogeneity across both supply and demand. For example, Uber cannot match a rider and a driver who are far apart in time or location. To address such challenges, platforms increasingly rely on flexibility levers—interventions that encourage market participants to be more accommodating in when or how they interact with the platform. For instance, Uber’s ``wait and save'' option offers a discount to riders who are willing to wait longer, making it easier to find compatible matches. Motivated by the growing use of such flexibility incentives, this thesis examines how flexibility can be structured, coordinated, and optimized in modern platforms. It focuses on two central dimensions of flexibility: (1) how flexibility levers interact across a platform’s ecosystem and (2) how flexibility decisions can be optimized to improve operational performance. Part I of this thesis examines the interactions and implications of platforms' flexibility decisions. Decisions around flexibility on platforms influence both (i) horizontal dynamics across market sides and (ii) vertical dynamics in a supply chain. Chapter 2 investigates the horizontal interaction between demand-side and supply-side flexibility incentives. While such incentives are common on both the demand (e.g., "wait and save" feature at Uber) and the supply side (Ride streak bonuses at Uber) of platforms, they have been treated in isolation in the literature and in practice. Chapter 2 initiates the study of two-sided flexibility in platforms: by modeling how these incentives influence the likelihood of compatibility between agents and the resulting matching size, we study whether and when platforms should invest in flexibility across both market sides. Moreover, we identify that platforms may realize significant efficiency gains by incorporating the horizontal interplay of flexibility when designing different incentives. In an orthogonal direction, Chapter 3 investigates the vertical supply chain implications of ride-hailing platforms' flexibility decisions. When dual-sourcing autonomous vehicles (AVs) and flexible human drivers with self-scheduling capacity, platforms (e.g., Uber's operations in Phoenix and Austin) make dispatch prioritization decisions to fulfill demand through a hybrid fleet. These decisions affect the incentives of AV suppliers and human drivers, and the self-scheduling nature of gig workers introduces novel supply chain challenges. We study how these challenges can hinder successful AV deployments and provide contracting solutions to overcome them. Part II of the thesis focuses on optimizing specific operational levers for flexibility. The digitization of modern platforms allows for algorithms that provide better customization and timing to harness flexibility. For instance, booking platforms can adjust their admission control decisions in real-time by considering customers' heterogeneous probabilities of being no-shows (i.e., not requiring service) and their compensation requirements for overbooking. In Chapter 4 we analyze an online resource allocation problem that allows overbooking and propose a policy that improves the additive profit loss guarantee (compared to a clairvoyant) in T periods from an order of square-root-T in the literature to a bounded constant. A related application appears in e-commerce, where retailers seek to use promotional discounts to align customer demand with their inventory position. Chapter 4 investigates how platforms can leverage an "opaque selling" strategy to dynamically time these discounts to influence purchase behavior and balance inventory. We propose a class of dynamic inventory-balancing algorithms that adapt opaque selling to real-time inventory states, achieving order-optimal fulfillment costs. This chapter demonstrates how demand-side flexibility can be operationalized through pricing levers for better inventory management.

Essays on Environmental Regulation

Thu, 01 May 2025 00:00:00 GMT

Essays on Environmental Regulation Aspelund, Karl Milutin I focus on three challenges that often confront regulators in designing environmental regulations around the world: equity-efficiency tradeoffs, incomplete information, and significant ecologic and economic uncertainty across time and space. First, I analyze the efficiency and distributional consequences of trade restrictions in environmental permit markets, I study common trade restrictions—segmentation and production requirements—in Iceland’s fisheries permit market, showing how they increase employment and compress the income distribution at an efficiency cost. Second, in the U.S. Conservation Reserve Program, Anna Russo and I find that auction mechanisms designed to incentivize land conservation suffer from widespread non-additionality due to adverse selection in land use. A redesigned scoring system that accounts for counterfactual land outcomes improves welfare. Third, using a stylized model calibrated to the Atlantic scallop fishery, Aaron Berman and I evaluate the use of output, input, and quantity-based regulations over time when a resource exhibits ecological and economic uncertainty across large areas of space. We show that, for a given ultimate sustainability goal, output taxes maximize value but exacerbate inequality and ecological risk, while input limits can strike a balance between flexibility, equity, and robustness. JEL Classification: L51, Q22, Q28

Dynamics of Group Decision-Making

Thu, 01 May 2025 00:00:00 GMT

Dynamics of Group Decision-Making Orzach, Roi This thesis comprises three chapters, all focused on Microeconomic Theory, specifically the dynamics of decision-making. The first explores how the desire for conformity results in long-run misperceptions due to uninformative decision-making. The second chapter studies multi-project collaborative experimentation. The final chapter analyzes whether decentralized organizations should utilize sequential or concurrent decision-making. The first chapter notes that in many settings, individuals imitate their peers’ public decisions for one or both of two reasons: to adapt to a common fundamental state, and to conform to their peers’ preferences. In this model, the fundamental state and peers’ preferences are unknown, and the players learn these random variables by observing others’ decisions. With each additional decision, the public beliefs about these unknowns become more precise. This increased precision endogenously increases the desire to conform and can result in decisions that are uninformative about a player’s preferences or perceptions of the fundamental state. When this occurs, social learning about peers’ preferences and fundamentals ceases prematurely, resulting in inefficient decisions. In line with findings from social psychology, I show that interventions aimed at correcting misperceptions of peers’ preferences may lead to more efficient decision-making in settings where interventions aimed at correcting misperceptions of the fundamental state may have no effect. The second chapter (joint with Charles Angelucci) analyzes collaborative experimentation across multiple independent domains. Each domain contains infinitely many potential projects with asymmetric benefits. In each period and domain, two players can idle, jointly explore a new project, or jointly exploit a known one, with voluntary transfers. For intermediate discount factors, treating domains as independent during experimentation is suboptimal. The optimal experimentation policy exhibits common features of collaborative experimentation: lengthy exploration, temporary project exploitation, recall of past projects, and inefficient initial or terminal idling within certain domains. We connect these findings to research on buyer-supplier dynamics and persistent productivity differences. The final chapter examines how the timing of decision-making shapes the allocation of decision rights within an organization. Here, I analyze concurrent versus sequential decision-making in a model where two units first communicate and then make decisions, attempting to both adapt to their local conditions and coordinate with their partner. Sequential decision-making improves overall information sharing compared to concurrent decisionmaking. However, first movers also have an incentive to over-adapt to their state, knowing second movers will conform to their decision. A surplus-maximizing headquarters prefers sequential decision-making to concurrent if and only if (i) the two units’ local conditions have sufficiently different volatilities and (ii) their need to coordinate is sufficiently asymmetric or low. Finally, sequential decision-making is shown to be optimal even when allowing for additional governance structures involving the reallocation of decision rights across the units and the headquarters and is shown to render some commonly-analyzed forms of decentralization sub-optimal. JEL Classification Numbers: C72, D83, D90

Storage and capacity rights markets in the natural gas industry

Fri, 01 Jan 1999 00:00:00 GMT

Storage and capacity rights markets in the natural gas industry Paz-Galindo, Luis A. (Luis Andrés) Thesis: Ph. D., Massachusetts Institute of Technology, Technology, Management, and Policy Program, 1999; Includes bibliographical references (p. 165-169).

Adsorption physics of metals partially coated by metallic films

Tue, 01 Jan 1963 00:00:00 GMT

Adsorption physics of metals partially coated by metallic films Levine, Jules David. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nuclear Engineering, 1963; Vita.; Includes bibliographical references (leaves 123-129).

Fluorocarbon synthesis in a high-intensity carbon arc

Tue, 01 Jan 1963 00:00:00 GMT

Fluorocarbon synthesis in a high-intensity carbon arc Bronfin, Barry Robert. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 1963; Includes bibliographical references (leaves 95-103).

Machine verification of mathematical proof

Tue, 01 Jan 1963 00:00:00 GMT

Machine verification of mathematical proof Abraham, Paul W. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Mathematics, 1963; Vita.; Includes bibliographical references (leaves 208-210).

Studies of an x-ray selected sample of cataclysmic variables

Wed, 01 Jan 1992 00:00:00 GMT

Studies of an x-ray selected sample of cataclysmic variables Silber, Andrew D. (Andrew David) Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1992; Includes bibliographical references (p. 253-254).

A bid-rent analysis of housing market discrimination.

Tue, 01 Jan 1974 00:00:00 GMT

A bid-rent analysis of housing market discrimination. Galster, George Charles. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1974; Vita.; Bibliography: leaves 273-283.

An economic analysis of the cobalt industry.

Thu, 01 Jan 1970 00:00:00 GMT

An economic analysis of the cobalt industry. Burrows, James Christian. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1970; Vita.; Bibliography: leaves 400-405.

Private interests and international conflict : a case study of US intervention in the Congo

Sun, 01 Jan 1989 00:00:00 GMT

Private interests and international conflict : a case study of US intervention in the Congo Gibbs, David Neil. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Political Science, 1989; Includes bibliographical references (leaves 404-427).

Quantum Yang-Mills on the two-sphere

Sun, 01 Jan 1989 00:00:00 GMT

Quantum Yang-Mills on the two-sphere Fine, Dana Stanley. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1989; Includes bibliographical references (p. 40).

The making of industrial policy : ad hoc corporatism and cable and satellite technology in West Germany

Sun, 01 Jan 1989 00:00:00 GMT

The making of industrial policy : ad hoc corporatism and cable and satellite technology in West Germany McKnight, Lee W. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Political Science, 1989; Vita. M.I.T. copy lacks leaves 15 and 385.; Includes bibliographical references (leaves 378-403).

Three essays on the theory of contracts

Fri, 01 Jan 1988 00:00:00 GMT

Three essays on the theory of contracts Hermalin, Benjamin E. (Benjamin Edward) Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1988; Includes bibliographical references.

Reflectivity studies of semimetals under pressure.

Mon, 01 Jan 1979 00:00:00 GMT

Reflectivity studies of semimetals under pressure. Mendez Perez, Emilio Eugenio. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1979; Vita.; Includes bibliographical references.

Cross-Frontal Exchange at the US Northeast Shelfbreak

Thu, 01 May 2025 00:00:00 GMT

Cross-Frontal Exchange at the US Northeast Shelfbreak Taenzer, Lukas L. Exchange across the semipermeable US Northeast shelfbreak front is a potential driver of irreversible change to the continental shelf waters, its productive ecosystem, and economically valuable fisheries. However, cross-frontal exchange is difficult to observe directly because it is highly intermittent, non-linear, and driven by both internal frontal instability and external forcing. In this thesis, I quantify eddy-driven exchange across the US Northeast shelfbreak front and its impact on the coastal ocean, starting on seasonal timescales and moving toward individual synoptic events. For this task, I take advantage of unprecedented multi-year observations from the Ocean Observatories Initiative (OOI) Coastal Pioneer Array (2014-2022). On seasonal timescales, the buoyancy-driven shelfbreak front is persistently trapped at the shelfbreak, which supports theoretical predictions of shelfbreak frontogenesis (Chapter 2). However, exchange across the shelfbreak front leads to a significant increase in salinity on the continental shelf between spring and fall. A volume budget of the subsurface continental shelf 'cold pool', habitat of the valuable benthic ecosystem, quantifies the contribution of eddy-driven advection to the observed salinity increase and explains the seasonal cycle of watermass variability on the shelf (Chapter 3). However, the multi-year averaged cold pool watermass budget does not capture the intermittency of cross-shelfbreak eddy-fluxes on synoptic timescales. Thus, I demonstrate how individual mooring timeseries can be used to capture the statistical distribution of eddy-driven exchange by assessing cross-shelfbreak eddy-covariance fluxes of salt and heat (Chapter 4). Mean eddy-covariance fluxes align well with previous residual estimates of cross-shelfbreak exchange to close coastal watermass budgets, and just 10-20% of statistically anomalous events are responsible for half the multi-year mean flux. To characterize rapid changes in continental shelf watermass properties over short timescales, I investigate the decline of seasonal stratification due to individual weather events and identify signatures of cross-shelfbreak exchange in wind-driven destratification (Chapter 5). Altogether, this thesis extends our understanding of the characteristics, timing, and magnitude of eddy-driven exchange across the US Northeast shelfbreak front on varying timescales. This information can help to inform how large-scale, long-term trends will impact the US East Coast coastal ocean and its marine ecosystem.

High-throughput tools for decoding T cell receptor specificity

Thu, 01 May 2025 00:00:00 GMT

High-throughput tools for decoding T cell receptor specificity Gaglione, Stephanie A. T cells play a central role in adaptive immunity by recognizing specific antigens through their T cell receptors (TCRs). These receptors bind to peptides presented by major histocompatibility complex (pMHC) proteins, driving immune responses in cancer, infection, and autoimmunity. Understanding how TCRs recognize antigens is crucial for developing cancer immunotherapies and identifying therapeutic targets in autoimmunity, infectious disease, and allergy. However, large-scale mapping of TCR-antigen interactions remains a challenge due to the vast diversity of both TCRs and antigens, as well as the limitations in current screening technologies in cost, throughput, and accessibility. This work presents two advances in large-scale TCR-antigen screening. The first aim introduces a scalable and cost-effective platform for synthesizing tens of thousands of TCRs from sequence data to create synthetic TCR libraries. We integrate this approach with a high-throughput antigen discovery platform that leverages pMHC-pseudotyped viruses to identify TCR-pMHC pairs. Using this system, we screen 3,808 vitiligo patient-derived TCRs against 101 antigens, and synthesize 30,810 TCRs from patients with pancreatic ductal adenocarcinoma (PDAC). By streamlining TCR assembly and antigen screening, this pipeline has the potential to advance immunotherapy, accelerate vaccine design, and deepen our understanding of TCR recognition. The second aim presents a new method that couples pMHC-displaying virus-like particles with yeast display, enabling efficient screening of millions of TCR variants against ~100 pMHCs at once. Yeast display is a powerful tool for studying TCR-antigen interactions but is constrained by its reliance on recombinant protein production. Our approach overcomes this limitation by replacing recombinant protein with barcoded lentiviral particles, allowing large-scale, multiplexed screening of TCR libraries. By overcoming key technical barriers, these tools significantly expand our ability to study TCR specificity and engineer new antigen-specific therapeutics.

A relative trace formula approach to the stable trace formula on the unitary group

Thu, 01 May 2025 00:00:00 GMT

A relative trace formula approach to the stable trace formula on the unitary group Lu, Weixiao We develop a relative trace formula on GLₙ which can be compared to the stable trace formula on the unitary group. Locally, we prove the fundamental lemma and transfer. We also proof a character identity based on the transfer. Globally, we develop a (simple) relative trace formula and compared it to the (simple) stable trace formula on the unitary group.

The influence of topography on ice-ocean interactions in coastal Antarctica

Thu, 01 May 2025 00:00:00 GMT

The influence of topography on ice-ocean interactions in coastal Antarctica Gaul, Alan Interactions between various water masses and ice shelves along the Antarctic coastline impact the global climate and sea level. This thesis focuses on how geometric features such as troughs and fast ice affect cross-shelf exchange in dense water formation regions of the Antarctic continental shelf. In Chapter 2, we use an idealized, eddy-resolving model to examine how an outflow of Dense Shelf Water (DSW) drives an inflow of warmer Circumpolar Deep Water (CDW) in a narrow, prograde trough. We find that the trough organizes mesoscale, cyclonic eddies in the dense outflow into a chain pattern. These cyclones then, as an efficient group, entrain filaments of CDW towards the coast. In Chapter 3, we use the same model to investigate buoyancy-driven cross-shelf exchange in a wide, retrograde trough. We find that the dynamics of the CDW intrusion change near the shelf-break. Here, the DSW outflow excites Topographic Vorticity Waves which interact with the DSW outflow to drive onshore intrusions of CDW. Onshore of the shelf-break, CDW intrudes further poleward due to a mean flow driven by eddy rectification. In Chapter 4, we switch to a realistic model of Prydz Bay, East Antarctica, to test the impact of local icebergs on cross-shelf exchange, dense water formation, and ice shelf basal melt rates. We find that removing the Cape Darnley Ice Barrier increases CDW intrusions and decreases dense water formation due to changes to the sea ice cover and wind-driven circulation. Conversely, removing the tabular Iceberg D-15 has little impact on heat transport and only slightly decreases dense water formation. Thus, the location of grounded icebergs greatly influences their impact on regional hydrography and ice shelf melt. In all, this thesis uses numerical models to examine the dynamics of cross-shelf exchange in coastal Antarctica. Understanding these dynamics is imperative for projecting how the Antarctic margins will impact the globe in a changing climate.

Analysis of Steel Decarbonization Strategies and Supply Chain Integration

Thu, 01 May 2025 00:00:00 GMT

Analysis of Steel Decarbonization Strategies and Supply Chain Integration Johnson, Sydney Rose Industrial decarbonization is an obstacle as the global community focuses on climate mitigation. Steel production is responsible for 7% of global emissions and faces unique challenges in reducing emissions in the ironmaking process. Models are developed to assess the emission and cost characteristics of current and emerging steel decarbonization strategies at a plant and sector level. Case studies are performed in India, the second-largest steel producer, and the United States, the fourth-largest steel producer, to highlight differences in strategies. In addition, a model of hydrogen-based steel production and a corresponding hydrogen network is created to assess supply chain needs. From this analysis, we can identify key cost and logistic barriers to technology implementation and the impact on the steel industry in respective locations. Finally, the future of the steel industry is assessed from a strategic standpoint while considering challenges to commercialization and policy mechanisms.

Aligning Machine Learning and Robust Decision-Making

Thu, 01 May 2025 00:00:00 GMT

Aligning Machine Learning and Robust Decision-Making Cristian, Rares C. Machine learning (ML) has become increasingly ubiquitous across many applications worldwide, ranging from areas like supply chain to personalized pricing, recommendations, and more. These predictive models are often used as tools to inform operations and decision-making, with the potential to revolutionize decision-making. The main key question this thesis aims to address is: How can we make ML methods aware of their downstream impact on the full decision-making process? As a result, we focus on developing methods to align AI with real-world objectives in order to make efficient, safe, and robust systems. This thesis is split into three chapters focusing on different aspects of this problem. In Chapter I of this thesis we address the heavy computational complexity of existing methods. We present a meta-optimization machine learning framework to learn fast approximations to general convex problems. We further apply this within an end-to-end learning framework which trains ML models with an optimization-based loss function to minimize the decision cost directly. This meta-optimization approach allows us to tackle problem sizes that were intractable using approaches from the previous literature. Furthermore, this work establishes analytically that this learning approach guarantees fast convergence to nearly-optimal solutions. Through this chapter it is shown that the proposed approach consistently scales better in terms of runtime as problem size increases, being 2 to 10 times faster for various problems while retaining nearly the same accuracy. In Chapter II we focus on the robustness problem, to make decisions that protect against worst-case scenarios as well as to noise in the data. Traditional robust optimization methods tackle this issue by creating uncertainty sets for each observation, aiming to minimize costs in worst-case scenarios. However, these methods assume the worst-case scenario happens at every observation, which can be too pessimistic. We propose a new approach that avoids constructing uncertainty sets and links uncertainties across the entire feature space. This allows for robust decision-making without assuming worst-case scenarios at every observation. Our approach integrates robustness with a concept of learning stability, proving that algorithms with a stability property inherently produce robust solutions without explicitly solving the robust optimization problem. This chapter finally tests the framework on a variety of problems such as portfolio optimization using historical stock data, inventory allocation and electricity generation using real-world data, showing significant improvement in terms of robustness and competitive results in terms of the average error relative to existing literature. Finally in Chapter III we consider the endogenous setting where decisions we take affect outcomes, like pricing and assortment optimization where decisions (like price) affect demand. In the end-to-end spirit, this research introduces an approach to jointly predict and optimize in this setting which learns a prediction aligned with expected cost. We further introduce a robust optimization decision-making method that can account for uncertainty in ML models --- specifically by constructing uncertainty sets over the space of ML models and optimize actions to protect against worst-case predictions. We further prove guarantees that our method can capture near-optimal decisions with high probability as a function of data. We also introduce a new class of two-stage stochastic optimization problems to the end-to-end learning framework that can now be addressed through our framework. Here, the first stage is an information-gathering problem to decide which random variable to ``poll'' and gain information about before making a second-stage decision based off of it. We present several computational experiments for pricing and inventory assortment/recommendation problems. We compare against existing methods in bandits and offline reinforcement learning, showing our approach has consistent improved performance over these.

Reducing the Compositional and Structural Degeneracy of Planetary Interiors

Thu, 01 May 2025 00:00:00 GMT

Reducing the Compositional and Structural Degeneracy of Planetary Interiors Lin, Zifan The interior conditions of planets are highly uncertain, because two types of intrinsic degeneracies – compositional degeneracy and structural degeneracy – prevent precise characterization. In this thesis, I develop a planetary interior code package, CORGI, incorporating state-of-the-art physical properties of planet-forming materials. Using CORGI, I eliminate unmixed interior scenarios for Uranus, rule out the fossil-compressed formation hypothesis for high-density exoplanets, and establish a link between formation history and atmospheric composition for hypothetical Earth-like white dwarf (WD) exoplanets, reducing interior degeneracy for these planets. However, I also identify a novel carbon-rich interior composition for sub-Neptunes, introducing an additional degeneracy to this already ambiguous category. It is heatedly debated that whether Uranus is a distinct-layer “ice giant” with greater than 70 wt% ice or a “rock giant” with compositional gradients and roughly equal amounts of ice and rock. Gravity field measurements from spacecraft, which directly probe interior mass distribution, are expected to resolve this debate. However, I show that the degeneracy will persist even with future Uranus Orbiter and Probe (UOP) mission, but the level of degeneracy can be reduced. My models indicate that only highly mixed interiors – either those with smooth density gradients or those with substantial light elements in the mantle and heavy elements in the atmosphere – are consistent with previous Voyager 2 measurements. Additionally, I demonstrate that the UOP can distinguish between high- and low-atmospheric metallicity scenarios and constrain the J6 harmonic, and potentially J8, if placed in close-in polar orbits, informing the mission and orbit design of UOP. For exoplanets with no solar system counterparts, interior models are essential for understanding their composition, structure, formation, and evolution. I apply CORGI to a category of high-density planets that are consistent with greater than 50% core mass fraction, substantially higher than that of the Earth (33%). By combining planetary interior modeling with photoevaporation modeling, I investigate into one of the hypotheses – the fossil-compressed hypothesis – for the origin of high-density planets. My models revealed that most high-density planets are highly improbable to be fossil-compressed cores, because most or even all of the iron-silicate core is molten during the evolution, while the fossil-compressed hypothesis requires a solid core. Kolmogorov–Smirnov test statistics show that this result is robust for planets with both hydrogen-dominated and steam envelopes. Planetary interior models sometimes reveal new degeneracies rather than resolving them. By combining interior, atmospheric chemistry, and transmission spectra models, I identify a new possible interior composition for sub-Neptunes: carbon-rich composition. I posit that sub-Neptunes formed between the “soot line” – a condensation line for refractory organic carbon – and the water snow line would have high bulk C/O ratios and a substantial carbon layer. Interior models revealed that such carbon-rich compositions are consistent with the masses and radii of sub-Neptunes, given appropriate atmospheric metallicity. Atmospheric chemistry and transmission spectra models found that the spectral features predicted for carbon-rich sub-Neptunes are compatible with observations by the Hubble Space Telescope and the James Webb Space Telescope. Finally, I explore the connection between post-main-sequence evolutions and the atmosphere and interior conditions of hypothetical Earth-like planets orbiting WDs. I showed that first-generation WD planets that have experienced significant atmospheric loss and second-generation WD planets that are formed in WD debris disks under a more clement radiation environment can be distinguished by the presence of a hydrogen-dominated atmosphere. Additionally, the interior conditions of second-generation WD planets can be inferred from WD pollution observations.

Principal Slip Zones in Nature and Experiments and Their Role in the Earthquake Cycle

Thu, 01 May 2025 00:00:00 GMT

Principal Slip Zones in Nature and Experiments and Their Role in the Earthquake Cycle Ortega-Arroyo, Daniel Earthquakes generally do not occur in intact rocks but rather within extremely narrow (≤10 cm) principal slip zones found along fault zones. These slip zones are relatively weaker than the surrounding wall rocks suggesting they play an important through the earthquake cycle. This thesis explores the microphysical processes occurring along principal slip zones and examines their influence in fault behavior from various perspectives and scales. Chapter 2 examines slickensides from three different fault systems, using laser profilometry to measure fault surface roughness and detailed microstructural analyses to identify the processes leading to these structures. Chapter 3 presents stick-slip experiments aimed at understanding the energy flow during earthquakes, quantifying the complete energy budget of individual events through a combination of microstructural analyses, novel magnetic field imaging, ultrasonic probing and numerical modelling. Chapter 4 involves Differential Scanning Calorimetry (DSC) measurements on ball-milled granite powders to investigate how extreme grain size reduction affects earthquake processes. Lastly, Chapter 5 presents DSC measurements of pseudotachylites aimed at constraining the thermal history of past slip-events. Results from this thesis highlight that the strain path significantly influences how the energy flows during the earthquake cycle, underscoring the importance of microstructural evolution in determining bulk sample behavior.

A Novel Machine Learning Approach to Robust Optimization: Theory and Applications

Thu, 01 May 2025 00:00:00 GMT

A Novel Machine Learning Approach to Robust Optimization: Theory and Applications Boucher, Benjamin The increasing availability of data offers modelers unprecedented opportunities to improve decision-making. In particular, we can leverage machine learning based approaches to estimate parameters of optimization models, enabling more informed decisions. However, these models often inherit uncertainty from the data they are trained on, leading to unreliable decisions when deployed at face value. This body of work develops robust optimization frameworks that take into account these uncertainties, by bridging theory and practice to mitigate decision-making risk. This thesis is organized into three chapters. In Chapter 2, we present a robust scheduling approach tailored to hospital operations, where post-surgery recovery times are uncertain and right-skewed. Our method captures the underlying distribution of patients' length of stay by taking into account their surgery type, and without necessitating detailed patient-level features. Applied to the Bone and Joint Institute of Hartford Hospital’s elective surgery scheduling problem, our approach reduces the monthly peak census---freeing up valuable hospital beds and improving system flexibility in the face of emergencies. In Chapter 3, we introduce a general methodology for constructing uncertainty sets informed by the loss functions of machine learning models. These sets are designed to protect against prediction errors in estimated optimization parameters. Extending guarantees from the robust optimization literature, we derive strong guarantees on the probability of violation. Synthetic computational experiments show that our method requires uncertainty sets with radii up to one order of magnitude smaller than those of other approaches. Lastly, in Chapter 4, we apply robust optimization to the domain of recommendation systems, where user and item interaction data are often noisy or adversarially perturbed. We can improve model robustness by modifying the training loss to defend against worst-case inaccuracies in user preference data. Because our approach adds only a single trainable parameter to the optimization model, its runtime impact is negligible. To evaluate the effectiveness of our method, we apply our modified loss function to a suite of recommendation systems from the literature and show consistent improvements in the performance of these methods on synthetic and benchmark datasets, as well as diminished ranking sensitivity.

Transition Metal Heterogeneous Catalysis Towards Applications in Sustainable Energy: Leveraging Rational Design Principles for Activity, Stability, and Stereoselectivity

Thu, 01 May 2025 00:00:00 GMT

Transition Metal Heterogeneous Catalysis Towards Applications in Sustainable Energy: Leveraging Rational Design Principles for Activity, Stability, and Stereoselectivity McCormack, Kaylee Lynn As global demand grows for renewable energy storage and conversion technologies, novel methods of storing energy and providing portable power are a necessity to accommodate variability in energy resources. Heterogeneous catalysis is a fundamental driver in the development of direct liquid fuel cells, water electrolyzers, and other sustainable energy storage applications including liquid organic hydrogen carriers (LOHCs). The methanol oxidation reaction (MOR) is a multistep reaction comprised of methanol dehydrogenation leading to CO adsorbed on the catalyst surface, followed by CO oxidation. Incorporation of an oxophilic material that facilitates the formation of OH groups on the surface is highly effective for improving CO oxidation and MOR performances. Thus in addition to the enhanced MOR activity through incorporation of the carbide core beneath the monolayers of Pt, the performance of these catalysts is expected to increase further by adding Ru atoms to the Pt shell, resulting in an overall 10 times enhancement in mass activity compared to commercial DMFC catalysts. Metal hydroxide organic frameworks (MHOFs) comprise edge-sharing metal hydroxide octahedra layers interconnected by carboxylate linkers which utilize pi-pi stacking to impart additional stability for electrochemical applications including the oxygen evolution reaction (OER). However, we discovered that there are definitive limits to this stability. This work explored the underlying processes causing loss of MHOF-specific motifs, which lead to phase transformations from MHOF to the Ni oxyhydroxide-like phase during OER, providing insight into the phase stability of these types of materials in base. During extended electrochemical OER cycling, linkers leach from the MHOF structure, exposing more electrochemically active Ni sites, thereby increasing the geometric OER activity. The linker leaching was observed to be accelerated by Ni²⁺ to Ni³⁺/⁴⁺ oxidation, which leads to a phase transformation from MHOF to NiOOH₂₋ₓ structure. A phase transformation mechanism is proposed where mono-μ-oxo bridge motifs found only in the MHOF structure convert to di-μ-oxo bridge motifs in the Ni oxyhydroxide-like phase. MHOFs with the weaker pi-pi interaction L1 linker underwent full transformation to this Ni oxyhydroxide-like phase. Meanwhile, the MHOFs with the stronger pi-pi interaction L4 linker showed transformations to Ni oxyhydroxide-like phases only at near surface regions, where the MHOF can remain as a less active core, thereby identifying NiOOH₂₋ₓ as the OER active phase, but highlights the potential of stability these MHOF materials for alkaline water oxidation. Finally, MHOFs present unique opportunities as sacrificial templates for thermocatalysis, with adjustable metal centers, structural robustness, and heteroatom incorporation through linker selection. In this thesis I present a model for using MHOFs and analogous MOFs to generate catalysts with unique catalytic properties which differentiate them from typical Ni hydrogenation catalysts. The MHOF-based catalysts perform similarly to other Ni-based catalysts in naphthalene and tetralin to decalin conversion rates per active site, however with a notable stereoselectivity toward cis-decalin across compared to the other Ni catalysts. This work highlights Ni-MHOFs as precursors for transition metal catalysts that emulate the stereoselectivity of NM catalysts, thereby reducing energy requirements in LOHC dehydrogenation.

Dynamic Regulation of Metabolic Flux Using Orthogonal Quorum Sensing

Thu, 01 May 2025 00:00:00 GMT

Dynamic Regulation of Metabolic Flux Using Orthogonal Quorum Sensing Ream, Michael James Dynamic regulation allows engineers to direct metabolic flux and cellular resources towards target pathways, improving production of value-added chemicals. One dynamic regulation strategy is quorum sensing (QS), a cell-to-cell communication that allows populations of cells to function as a collective. By applying QS to engineered pathways, the diversion of metabolic resources can be coupled to the population of the culture, thereby ensuring sufficient growth is achieved. These circuits can then be layered to allow for fine-tuned control of the cell. Previous research has focused on QS systems that utilize acyl homoserine lactones (AHL) as signaling molecules. These systems are well characterized, but pairing them in layered systems is difficult due to similarities in signals, which can cause unintended switching of the opposing control system. Here, we identified orthogonal AHL systems for an independently-controlled, multi-layered regulation circuit, which was then applied to increase the production of the valuable natural products of naringenin and bisnoryangonin in Escherichia coli. To our knowledge, the resulting regulations led to the highest extracellular titers at the flask scale with a final naringenin titer of 1251.2 +/- 59.6 mg/L and a bisnoryangonin titer of 597.7 +/- 18.3 mg/L in naringenin equivalence. In a parallel effort to obtain orthogonal QS-based regulations, we focused on expanding the available QS systems for the model organism E. coli. Specifically, the Gram-positive QS systems of Agr from Staphylococcus aureus and Com from Bacillus subtilis were implemented and subsequently improved for functionality in E. coli. These systems have tight control of expression, which was demonstrated by dynamic downregulation of the aromatic amino acid pathways via CRISPRi. The efficacy of these systems in synthetic biology was further illustrated by using T7 RNA polymerase to amplify the expression output of an Agr-controlled circuit. Overall, this work developed and applied QS-based regulation systems to improve microbial production of value-added chemicals in E. coli.

Design of Single-Chain Polymer Nanoparticles to Mimic Globular Proteins

Thu, 01 May 2025 00:00:00 GMT

Design of Single-Chain Polymer Nanoparticles to Mimic Globular Proteins Jin, Tianyi While globular proteins exhibit an impressive range of precise functionalities, their sensitivity to environmental changes has motivated scientists to pursue two complementary strategies: (1) engineering and designing proteins directly or indirectly, and (2) exploring synthetic alternatives with higher stability. Single-chain polymer nanoparticles (SCNPs) based on random heteropolymers (RHPs) have emerged as a promising platform as both protein stabilizers and mimetics. However, theoretical understanding of the origins underlying their functional versatility has lagged behind experimental advances. Unlike natural proteins, which rely on well-defined sequences and threedimensional structures, RHPs achieve their functions through sequence and structure ensembles. In this thesis, I use multiscale molecular simulation techniques to uncover the molecular origins of the versatile, protein-mimetic functions of RHPs. This work is motivated by recent experimental findings showing that four-component methacrylate-based (MMA-based) RHPs can function as catalysts, proton channels, and chaperonins. By comparing the behaviors of MMA-based RHPs with that of globular proteins, I provide fundamental physicochemical insights and design principles for SCNPs as protein mimetics and stabilizers. I highlight the significance of chemical polarity and nuances in materials design. In Part I, I study the self-assembly and dynamics of MMA-based RHPs in both melt and solution. I show that MMA-based RHPs collapse into compact globular structures with dynamical heterogeneity and slow dynamics due to glassy backbone. Properties including compactness, monomer hydration, and potential to stabilize membrane protein are largely insensitive to sequence, but strongly depend on composition. At the core of their behavior lies a phenomenon known as hydration frustration, where polar groups become dehydrated and hydrophobic groups remain hydrated. This is a key feature observed in globular proteins. This effect arises from a negative Flory–Huggins interaction parameter (𝜒) between methyl methacrylate and polyethylene glycol in MMA-based RHPs. Guided by these insights, I design a biodegradable, polyester-based RHP that exhibits similar properties in silico. I further map the potential energy landscape of these RHPs through microsecond simulations. In Part II, I study the adsorption and stabilization behaviors of MMA-based RHPs on both synthetic and biological surfaces. I show that adsorption onto graphene and non-specific binding to 𝛽-barrel membrane proteins are primarily through side-chain interactions, with limited backbone reconfiguration. The transition from a globular to a wrapped morphology is hindered by internal friction arising from the deformation of the glassy backbone. I demonstrate that population-based stabilization of 𝛽-barrel proteins is mediated through loop-specific contacts that reduce fluctuations in flexible regions. The findings of this thesis provide a comprehensive framework for understanding and designing synthetic protein mimetics and stabilizers. MMA-based RHPs present a promising alternative to natural proteins, offering greater resilience, improved cost-effectiveness, and enhanced scalability. The structural and functional parallels between RHPs and globular proteins suggest that the principles uncovered here may generalize across a broad class of biomimetic and bio-synthetic hybrid systems. This work lays the foundation for the rational design of SCNPs for emerging applications

The Fourier-Bessel Series and Hard Edge Limits

Thu, 01 May 2025 00:00:00 GMT

The Fourier-Bessel Series and Hard Edge Limits Lerner-Brecher, Matthew The universality classes defined by the Airy and Bessel kernels are two of the most fundamental in random matrices and growth models more generally. Broadly speaking, one often encounters the Airy kernel when studying models where the relevant eigenvalues or particles are unbounded, and the Bessel kernel when examining their constrained counterparts. In this thesis, we analyze two recent problems where the relevant expressions involve a variant of the Airy functions known as the Fourier-Airy series. In both cases, we find that the constrained versions have natural analogues expressible in terms of the Fourier-Bessel series echoing the relationship between the Airy and Bessel kernels. In the first part, we study the hard edge limit of a multilevel extension of the Laguerre β-ensemble at zero temperature. In particular, we show that asymptotically the ensemble is given by Gaussians with covariance matrix expressible in terms of the Fourier-Bessel series. These Gaussians also have an explicit representation as the partition functions of additive polymers arising from a random walk on roots of the Bessel functions. Our approach builds off of techniques introduced by Gorin and Kleptsyn [1] and is rooted in using the theory of dual and associated polynomials to diagonalize transition matrices relating levels of the ensemble. Like the corresponding soft edge limit in the Hermite case studied by Gorin and Kleptsyn, the object we introduce should represent a new universality class for zero temperature random matrices. In the second part, we introduce a new diffusion process which arises as the n → ∞ limit of a Bessel process of dimension d ≥ 2 conditioned upon remaining bounded below one until time n. In addition to being interesting in its own right, we argue that the resulting diffusion process is a natural hard edge counterpart to the Ferrari-Spohn diffusion of [2].

Quantitative embeddings with applications

Thu, 01 May 2025 00:00:00 GMT

Quantitative embeddings with applications Portnoy, Elia In this thesis, we discuss quantitative embeddings that generalize a theorem of Kolmogorov and Barzdin. The theorem says that any bounded degree graph with V vertices can be mapped into a 3-dimensional ball of radius sqrt(V), so that at most a constant number of edges intersect any unit ball. In one generalization we describe how much freedom we have in placing the vertices of the graph, and in the other we prove a similar result for simplicial complexes of any dimension. We also discuss applications of these quantitative embeddings to a problem in metric geometry related to the isoperimetric inequality and a problem about constructing local quantum error-correcting codes.

Co-rank 1 Arithmetic Siegel--Weil

Thu, 01 May 2025 00:00:00 GMT

Co-rank 1 Arithmetic Siegel--Weil Chen, Ryan C. We prove the arithmetic Siegel–Weil formula in co-rank 1, for Kudla–Rapoport special cycles on exotic smooth integral models of unitary Shimura varieties of arbitrarily large even arithmetic dimension. We also propose a construction for arithmetic special cycle classes associated to possibly singular matrices of arbitrary co-rank. Our arithmetic Siegel–Weil formula implies that degrees of Kudla–Rapoport arithmetic special 1-cycles are encoded in near-central first derivatives of unitary Eisenstein series Fourier coefficients. The key input is a new limiting method at all places. On the analytic side, the limit relates local Whittaker functions on different groups. On the geometric side at nonsplit non-Archimedean places, the limit relates degrees of 0-cycles on Rapoport–Zink spaces and local contributions to heights of 1-cycles in mixed characteristic.

Carbon flow and food web structure in the mesopelagic zone of the North Atlantic Ocean

Thu, 01 May 2025 00:00:00 GMT

Carbon flow and food web structure in the mesopelagic zone of the North Atlantic Ocean Gardner, Kayla Grace Mesopelagic ecosystems are vital habitats that link the euphotic zone and the deep ocean through food web interactions and carbon flow pathways. In this dissertation, I use carbon compound-specific stable carbon isotope analysis of amino acids (CSIA-AA) and DNA gut metabarcoding methodologies to provide a broad ecological outlook on mesopelagic carbon flow coupled with finer scale taxonomic details. In Chapter 2, I analyze the diets of seven abundant mesopelagic fish species by combining the integrative power of CSIA-AA with the instantaneous, taxonomic aspects of DNA gut metabarcoding. Three primary diet types were identified: copepod-based, fish-based, and generalist. Additionally, carbon sources were variable across the two years, but cyanobacteria were consistently an important carbon source - evidence that mesopelagic fish are essential exporters in weaker biological pump systems. Finally, this chapter includes cyanobacteria CSIA-AA signature data that was previously missing from the literature. In Chapter 3, I augment the CSIA-AA data by adding genus-level zooplankton data and samples from the winter season. Zooplankton were more dispersed among all the end members than fish, particularly in the winter. Fish, however, still relied the most on cyanobacteria-sourced carbon. This chapter supplies the first zooplankton carbon CSIA-AA data set at such a fine taxonomic resolution. In Chapter 4, I examine the effect of phytoplankton community structure on fish and zooplankton carbon sources by sampling before and during diatom a diatom bloom. Zooplankton, and to a lesser extent fish, showed a shift to diatom-based carbon sources during the bloom. As a whole, this dissertation advances our knowledge of mesopelagic food webs by providing a baseline carbon-CSIA-AA data set for key zooplankton and fish species across several seasons that will inform ecological models to understand how the mesopelagic will react to anthropogenic pressure.

Geometrically-informed methods of wave-based imaging

Thu, 01 May 2025 00:00:00 GMT

Geometrically-informed methods of wave-based imaging Greer, Sarah Yvonne In this thesis, we are interested in understanding and advancing wave-based imaging techniques defined by the adjoint-state method. Wave-based imaging uses wavefield data from receivers on the boundary of a domain to produce an image of the underlying structure in the domain of interest. These images are defined by the imaging condition, derived from the first-order adjoint-state method, which corresponds to the gradient and maps recorded data to their reflection points in the domain. In the first part, we introduce a nonlinear modification to the standard imaging condition that can produce images with resolutions greater than that ordinarily expected using the standard imaging condition. We show that the phase of the integrand of the imaging condition, in the Fourier domain, has a special significance in some settings that can be exploited to derive a super-resolved modification of the imaging condition. Whereas standard imaging techniques can resolve features of a length scale of λ, our technique allows for resolution level R ă λ, where the super-resolution factor (SRF) is typically λ{R. We show that, in the presence of noise, R „ σ. In the second part, we investigate the Hessian operator, which arises from the second-order adjoint-state method, in the context of full-waveform inversion, a non-linear least-squares problem for estimating material properties within the domain of interest. We analyze the contributions of reflected and transmitted waves to the linearized Hessian operator, demonstrating that reflected waves generally produce a high-rank component with well-distributed eigenvalues, while transmitted waves contribute to a low-rank component with poorly distributed eigenvalues. This decomposition of the Hessian, motivated by the underlying physical system, provides insights that can be used to improve inversion strategies. The advancements in both parts of this thesis leverage the underlying structure and geometry of the domain of interest, providing the foundation for the zero-phase imaging condition in the first part and informing the decomposition of the Hessian operator in the second part.

Geometry and analysis of Ricci curvature and mean curvature flows

Thu, 01 May 2025 00:00:00 GMT

Geometry and analysis of Ricci curvature and mean curvature flows Zhao, Xinrui In this thesis, we study the geometry and analysis of spaces with Ricci curvature bounded below from the following three perspectives and the asymptotically conical singularities of mean curvature flows in the following two perspectives. For the spaces with Ricci curvature bounded below, firstly we study the unique continuation problem on RCD spaces, which is a long-standing open problem, with little known even in the setting of Alexandrov spaces. Together with Qin Deng, we proved that on RCD(K,2) spaces both harmonic functions and caloric functions satisfy weak unique continuation properties. Furthermore we constructed counter-examples showing that strong unique continuation in general fails for harmonic and caloric functions on RCD(K,N) spaces where N is greater or equal to 4. Secondly, we consider constructing a canonical diffeomorphism between the n-sphere and a n-dimensional space with Ricci curvature bounded from below by n-1 which is close to the n-sphere in the Gromov-Hausdorff sense. Together with Bing Wang we proved that the first (n+1)-eigenfunctions of Laplacian provides a bi-Holder diffeomorphism and we further give a counter-example showing that the bi-Holder estimate is sharp and cannot be improved to a bi-Lipschitz estimate. Thirdly, we study the Margulis Lemma on RCD spaces. Together with Qin Deng, Jaime Santos-Rodríguez and Sergio Zamora, we extend the Margulis Lemma for manifolds with lower Ricci curvature bounds to the RCD setting. As one of our main tools, we obtain improved regularity estimates for Regular Lagrangian flows on these spaces. For the asymptotically conical singularities of mean curvature flows, firstly together with Tang-Kai Lee, we proved asymptotically conical self-shrinkers as tangent flows of MCFs are unique, generalizing the result in the case of hypersurface proven by Chodosh-Schulze. Secondly, together with Tang-Kai Lee we prove that given any asymptotically conical shrinker, there exists an embedded closed hypersurface such that the mean curvature flow starting from it develops a type I singularity at time 1 at the origin modeled on the given shrinker.

Movement and Trophic Ecology of Large Pelagic Fishes Connecting Surface Waters with the Ocean's Twilight Zone

Thu, 01 May 2025 00:00:00 GMT

Movement and Trophic Ecology of Large Pelagic Fishes Connecting Surface Waters with the Ocean's Twilight Zone Willis, Ciara Sinead Roche The ocean’s twilight zone is a vast area of the global ocean that lies between the sunlit surface waters and perpetually dark midnight zones, covering depths from ~200 to 1000 meters. Recent work in the twilight (or mesopelagic) zone has revealed unexpected biomass and diversity that may not only challenge scientific understanding of ocean systems but also provide new and largely untapped resources for fisheries harvest. The extent to which commercially valuable, highly migratory top predators such as tuna and swordfish rely on mesopelagic biomass for forage has not previously been quantified but is thought to be substantial. Pressure from emerging industrial fisheries in the twilight zone makes determining the linkages between mesopelagic prey and migratory predators of pressing concern for sound management in keeping with the precautionary principle. Ocean predators are further hypothesized to dive into the deep ocean for a range of motives beyond forage, including for navigation on their long migrations. In this thesis, I begin by using compound-specific stable isotope analysis to trace the flow of carbon through pelagic ecosystems in the northwest Atlantic to three predators: bigeye tuna (Thunnus obesus), swordfish (Xiphias gladius), and yellowfin tuna (Thunnus albacares). I confirm the presumed high reliance of these predators on mesopelagic prey using a Bayesian mixing model approach that estimated 50-60% of their temperate carbon is sourced from mesopelagic food webs. Next, I take a larger view of epi- and mesopelagic food webs by sampling simultaneously across a pelagic food web from bottom to top at one point in time and space in the northwest Atlantic Ocean. I trace the movement of carbon and nitrogen from particulate organic matter, through mid-level consumers, up to top predators using compound-specific stable isotope analysis of amino acids. Nitrogen stable isotope analyses is also used to calculate trophic positions, providing a more detailed view of pelagic food web structure and function. To complement these trophic studies, I conduct a movement analysis of vertical habitat use by swordfish focused on their intermittent extreme dives. I explore possible motivations for these dives, including forage, predator avoidance, and navigation. Qualitative investigation of dive geometry, as well as quantitative logistic models of the physical and biological environment, indicate that navigation is the most likely motive. Finally, I consider the implications of predator reliance on mesopelagic forage in a fisheries economics context. Using my earlier diet sourcing results, I adapt a bioeconomic model with a new predator-prey dynamic to evaluate the effects of potential mesopelagic fisheries on their predators with bigeye tuna as the representative predator. Model results highlight the importance of recognizing predator-prey interactions in management of mesopelagic fisheries and demonstrate the sensitivity of equilibrium economic and ecological conditions for the tuna stock under different price and cost scenarios. Overall, these studies emphasize the importance of the deep ocean to marine predators and suggest that a new mesopelagic fishery could be economically viable in-and-of itself but may have significant negative impacts on existing tuna and swordfish fisheries due to reduced forage.

From Theory to Practice: Improving Causal Conclusions from Healthcare Data

Thu, 01 May 2025 00:00:00 GMT

From Theory to Practice: Improving Causal Conclusions from Healthcare Data Cobzaru, Raluca-Ioana Causal inference in biomedical, epidemiological, and health policy research often relies on observational data, such as electronic health records (EHRs), patient registries, or insurance claims, to compensate for the inaccessibility of randomized controlled trials. However, causal inference from observational data depends on strong, often unverifiable assumptions, including exchangeability, parallel trends, and correct model specification. Violations of these assumptions can bias treatment effect estimates, making it essential to assess the sensitivity of causal conclusions--particularly in healthcare applications, where properly interpreting causal relationships and delivering reliable insights is critical for guiding clinical practice and informing system-wide decisions. This thesis contributes to the theoretical and empirical analysis of causal methods under realistic data limitations, with a focus on covariate selection and adjustment, proximal inference for unobserved confounding, and applications of modern estimation techniques to healthcare-relevant settings. In Chapter 2, we investigate the performance and robustness of state-of-the-art machine learning estimators for causal inference when covariate selection for statistical adjustment is performed in a realistically suboptimal manner. Although nonparametric doubly robust methods are asymptotically unbiased, they can perform poorly in finite samples due to slow convergence of nuisance function estimates. Through an extensive simulation study, built upon previous research on statin use and atherosclerotic cardiovascular disease (ASCVD) incidence, we examine how including extraneous covariates---a likely risk when researchers over-adjust to mitigate concerns about unmeasured confounding---may degrade estimator performance. These findings highlight the importance of incorporating domain knowledge to guide covariate selection, even when using flexible data-adaptive methods. In Chapter 3, we explore proximal causal inference, a novel framework designed to address unobserved confounding by leveraging negative control exposures and outcomes to recover the true causal effects. While this approach offers an alternative to the exchangeability assumption, it relies on identification conditions for the proxy variables set and model specifications that remain empirically untestable. We derive closed-form bias expressions under a linear structural equation model to quantify the impact of violating these assumptions and propose a practical bias adjustment strategy using data from an observational ICU study. These results provide a foundation for formal sensitivity analysis and offer insight into the real-world utility of proximal methods. Finally, in Chapter 4 we evaluate the impact of the Meaningful Use Incentive Program on hospital performance, using modern causal methods in a multi-period difference-in-differences (DiD) design. We apply a staggered DiD estimation framework, along with a sensitivity analysis of dynamic treatment effect estimates under potential violations of the parallel trends assumption, across a wide range of quality, safety, and process of care measures. By accounting for treatment timing variation, allowing for heterogeneous effects over a longer follow-up period, and testing for violations of identifying assumptions, our study offers a more rigorous and comprehensive assessment of the causal impact of health information technology (IT) policies introduced by the Meaningful Use program. Our findings help reconcile mixed findings in the literature and inform the design of future hospital incentive programs that aim to promote advanced use of EHRs.

Mechanistic insights into how collective effects mediate the T cell response

Thu, 01 May 2025 00:00:00 GMT

Mechanistic insights into how collective effects mediate the T cell response Yin, Rose T cells play an important role in the adaptive immune system by providing robust responses to foreign pathogens while avoiding widespread autoimmunity. Although many specific microscopic factors are thought to contribute to this self/non-self discrimination, based on a theoretical paper and experiments, a generalized mechanistic framework has emerged over the past decade to describe the remarkable robustness of self/non-self discrimination in spite of the presence of autoimmune T cells in every host. This quorum threshold mechanism states that a threshold number of T cells (a quorum) must be activated by a foreign antigen in a local area for an immune response to ensue. In my thesis, I use analytical and computational models to show how this mechanism enables a response against foreign pathogens while tolerating exposure to self-tissue, and how it increases robustness against perturbations such as changed self-antigen presentation or increased epitope spreading due to inflammation. However, under persistent or severe infections, these models also show that the risk of autoimmunity increases through enhanced sampling of rare epitopes and activation of cross-reactive T cells. These results provide a potential explanation for why persistent infections often trigger autoimmune diseases. To further understand the emergence of the quorum threshold, I developed a population dynamics model. Our results show that steady states corresponding to an effective or ineffective immune response are separated by a threshold dependent on both activated T cell population concentration and concentration of a growth factor (IL2) that is secreted by T cells and absorbed by cells that dampen the immune response. Notably, the threshold’s existence proves robust across randomized parameters, highlighting its fundamental role in regulating T cell responses.

Uniqueness of p-local truncated Brown-Peterson spectra

Thu, 01 May 2025 00:00:00 GMT

Uniqueness of p-local truncated Brown-Peterson spectra Lee, David Jongwon When p is an odd prime, we prove that the Fp-cohomology of BP⟨n⟩ as a module over the Steenrod algebra determines the p-local spectrum BP⟨n⟩. In particular, we prove that the p-local spectrum BP⟨n⟩ only depends on its p-completion BP⟨n⟩p̂. As a corollary, this proves that the p-local homotopy type of BP⟨n⟩ does not depend on the ideal by which we take the quotient of BP. In the course of the argument, we show that there is a vanishing line for odd degree classes in the Adams spectral sequence for endomorphisms of BP⟨n⟩. We also prove that there are enough endomorphisms of BP⟨n⟩ in a suitable sense. When p = 2, we obtain the results for n ≤ 3.

Characterizing microearthquakes and shallow structure with dense array and optical fibers

Thu, 01 May 2025 00:00:00 GMT

Characterizing microearthquakes and shallow structure with dense array and optical fibers Chang, Hilary Source properties of small earthquakes, such as source dimension and stress drop, help us to constrain source physics and assess seismic hazards. Small events carry information about the stress state in the subsurface. They also help us predict the behavior of larger earthquakes. However, the source properties of small earthquakes (magnitude less than 3) are poorly constrained because of trade-offs with other wave propagation effects. The trade-offs with attenuation can cause the apparent stress drop to vary, resulting in an apparent breakdown of earthquake self-similarity. To date, researchers are still trying to understand the uncertainty in source parameter measurements and to improve their accuracy. In the first part of the thesis, I use a dense array in Oklahoma to investigate the influence of site effects on source parameter modeling. By analyzing ground motions, subsurface velocity structure, and attenuation, I show how these factors relate to site effects, and how source parameter estimations vary under different modeling assumptions. To avoid large site-effect-related biases and uncertainties when modeling source parameters, I suggest (1) assuming a realistic attenuation model, (2) using selected stations on hard rocks instead of using many stations with unknown site conditions, and (3) constraining variables in the model during the inversion to avoid parameter trade-offs. In the second part of the thesis, I explore the use of fiber-optic cables in several seismic applications. Distributed Acoustic Sensing (DAS) turns optical fibers into dense receiver arrays. These fiber-optic cables have the advantage of being resilient and easier to maintain compared to mechanical sensors. The cable provides a dense array that helps us separate source and wave propagation effects for different purposes. Here, I use cables in wells in geothermal reservoirs and a telecom cable on the MIT campus. The applications include structure monitoring and imaging, seismic hazard assessment, and earthquake source characterization. DAS measures strain and requires special considerations to fit into conventional seismic methods built on particle motions. Deconvolution-based methods help deal with the DAS instrument response. The gauge length adds a velocity-dependent amplitude response that we need to consider when modeling the DAS spectrum. I provide workflows for conducting seismic imaging surveys using telecom cable and downhole DAS for temporal monitoring and source parameter analysis. The cables can reach places that were difficult to reach in the past. With careful processing, DAS can be a promising tool for structure monitoring, urban seismic hazard assessment, and microearthquake source analysis.

New Regimes for Topology Optimization in Photonics

Thu, 01 May 2025 00:00:00 GMT

New Regimes for Topology Optimization in Photonics Chen, Mo Inverse design is a powerful methodology to obtain non-trivial and non-intuitive photonic structures of unprecedented performance. Topology optimization is a particular class of inverse design method that has been increasingly popular in photonics. Numerous topology optimization tools and frameworks have been developed and often yield satisfying results for various engineering problems. This work explores the subtleties involved in the development and application of topology optimization, and presents new regimes for photonic design, where the key to finding the right solutions lies in posing the right questions. To begin with, we first review the current frameworks for photonic topology optimization. We point out that, as new algorithms emerge, the lack of standardized validation methods presents a challenge for further advancements. To address this, we provide a comprehensive suite of test problems along with a length-scale metric for comparing designs across different algorithms, aiming to facilitate the development and validation of future inverse design approaches. However, a functioning inverse design algorithm alone is not sufficient to guarantee satisfying designs. We present two case studies highlighting the importance of careful formulation for achieving mathematical robustness and tractability that is crucial to the success of optimization. The first case examines the inverse design of 3D-printable metalenses with complementary dispersion for terahertz imaging. It illustrates a physical dichotomy between achieving two distinct dispersion behaviors in a thin structure. We demonstrate that a key aspect in making such design tractable is carefully balancing the trade-offs between focal quality and scanning rate in the optimization problem formulation. The second case focuses on the inverse design of multiresonance filters via quasi-normal mode theory. Traditional filter design approaches have various limitations, and directly applying topology optimization leads to numerically stiff formulations. We propose a new practical high-order-filter design method based on a minimal set of analytical design criteria derived from quasi-normal mode theory. We illustrate our approach by designing 3rd and 4th-order elliptic and Chebyshev dielectric filters.

Progress on the Interplay of Machine Learning and Optimization

Thu, 01 May 2025 00:00:00 GMT

Progress on the Interplay of Machine Learning and Optimization Lin, Zhen Machine learning and optimization have been playing significant roles in the world. Despite the remarkable advancements in these fields, various crucial problems remain unsolved. In this thesis, we address some of these problems by exploring the interplay of machine learning and optimization. In the first part of this thesis, we utilize optimization tools to address two practically important and critical topics in machine learning: interpretability of machine learning models, and improving data for prediction. In Chapter 2 and 3, we focus on improving the interpretability of machine learning models. In particular, Chapter 2 presents an efficient algorithm for training high-quality Nonlinear Oblique Classification Trees using gradient descent. We demonstrate on real-world datasets this is an effective approach. In Chapter 3, we develop an optimization approach to train low depth (up to depth 8) classification trees with hyperplanes to closely approximate neural networks. We also incorporate sparsity in the hyperplanes of the trees. In this way, we contribute in increasing the interpretability of neural networks. Computational results on real-world datasets with different sizes of neural networks show the effectiveness of our algorithm. In Chapter 4, we propose an integer optimization method to improve class-imbalanced data. Our method undersamples the majority class and performs better than existing methods on real-world imbalanced datasets. In the second part of the thesis, we explore the direction of applying machine learning to optimization. In Chapter 5, we show that optimization methods can significantly benefit from a machine learning treatment. We develop a model-based trust-region method for derivative-free optimization problems under noise. Our method, which uses robust and sparse regression to build models of functions, is much more robust and has higher scalability than existing methods.

Integrated Modeling Approaches to Quantify Vehicle-to-Grid Services in an Evolving Power Sector

Thu, 01 May 2025 00:00:00 GMT

Integrated Modeling Approaches to Quantify Vehicle-to-Grid Services in an Evolving Power Sector Owens, James The U.S. transportation sector emitted 27% of nationwide greenhouse gas (GHG) emissions in 2020. In addition to cleaner fuels and more efficient powertrains, vehicle electrification is poised to be a key driver of sector decarbonization. However, fleet electrification poses an unprecedented coupling of the transportation sector and electric grid. Electric vehicle charging and other new loads, if not sufficiently managed, are anticipated to add significant strain to the grid. In light of these challenges, vehicle-to-grid (V2G) has been proposed as a form of flexible load and decentralized energy storage. Within a V2G framework, grid-connected electric vehicles provide services to power grids, for example by shifting when they charge or discharging their batteries to the grid when power demand is high. Conceptually, V2G can reduce the costs of intermittency, facilitate renewables growth, and provide storage services to the grid. While V2G continues to evolve and gain market traction, there remain several aspects of the technology, both operational and economic, that stand to be better understood and improved upon to best facilitate widespread adoption. For instance, EVs can theoretically displace stationary energy storage, but to what extent? What are demand side implications for the grid? For early technology adopters, particularly commercial fleets, how do travel needs and network tariffs affect V2G revenues? How can one practically simulate V2G and other service outcomes and do the potential revenues justify initial investment? This thesis addresses such questions and concerns through the development and application of methods that (1) quantify the technology's ultimate value proposition at the systems level, and (2) enable risk-informed market participation and financial analysis.

Artificial Intelligence for System Medicine: Methods and Applications

Thu, 01 May 2025 00:00:00 GMT

Artificial Intelligence for System Medicine: Methods and Applications Ma, Yu Modern medicine is facing a fundamental shift with the increasing availability of large-scale electronic health data and artificial intelligence-based technologies. In particular, integration across different patient characteristics to optimize, learn, and plan simultaneously across multiple medical tasks of interest, or what we call system medicine, provides opportunities for clinical and operational systems to improve disease diagnosis, operational efficiency, and, most importantly, clinical understanding. This thesis aims to develop and validate novel methods using artificial intelligence and optimization to address challenges faced in this domain. We introduce general-purpose artificial intelligence frameworks in Part 1. First, we introduce Holistic Artificial Intelligence in Medicine (HAIM), an integrated pipeline that combines multimodal data ranging from tabular, time-series, vision, and language into a single framework for downstream task learning. We then develop Multimodal Multitask Machine Learning for Healthcare (M3H), an end-to-end, many-to-many framework that joins the learning of multimodal data with a diverse set of medical and machine learning problem tasks. This work proposed a novel attention mechanism as well as a new explainability metric that extends previous works on the evaluation of input space contributions (features) to the output space (outcomes). These works are actively being incorporated to improve diagnosis in cardiovascular and oncology studies using ECG and multi-omics data. We then address real-world adoption concerns to design responsible machine learning models using optimization in Part 2. We first introduce robust regression under averaged uncertainty, which yields exact, closed-form, and analytical solutions that recover ridge regression. We show insight into how the geometric properties of the uncertainty set are closely linked to the regularization strength of the equivalent ridge regression. We then proposed an adaptive, data-driven approach for personalized breast cancer screening scheduling, which integrates an ML-based survival prediction model and a stochastic optimization formulation that balances screening delay and screening frequency. Finally, we apply predictive and prescriptive analytic methods to improve general medical outcomes in Part 3 and Part 4, respectively. These studies range from oncology, trauma, cardiovascular, and logistics planning. In Part 3, we aim to develop models that can most accurately learn the outcome. We show that predictive methods across different machine learning methodologies, including deep neural networks for computer vision tasks, tree-based models (including Optimal Classification Trees and gradient boosted trees), can significantly improve over either existing benchmarks or achieve comparable performances with manual physician practice. In Part 4, we delve into prescriptive analytics, which focuses on assigning the optimal treatment or other clinical decision to achieve the best outcome. We apply the interpretable Optimal Policy Trees methodology across oncology and trauma settings and observe improved medical outcomes (i.e., mortality rate).

Elucidation of gene clusters underlying withanolide biosynthesis in ashwagandha

Thu, 01 May 2025 00:00:00 GMT

Elucidation of gene clusters underlying withanolide biosynthesis in ashwagandha Reynolds, Erin E. Withanolides are medicinally important steroidal lactones produced by Withania somnifera (ashwagandha) amongst other Solanaceae family plants, known for their anti-inflammatory, anti-cancer, and adaptogenic properties. However, the biosynthetic pathway to withanolides is largely unknown, preventing scale-up and hindering pharmaceutical applications. In this thesis, we report a chromosome-scale assembly of the W. somnifera genome, which we use for biosynthetic gene cluster mining. We identify two biosynthetic gene clusters likely involved in withanolide biosynthesis and explore some aspects of their evolution. The identified clusters are among the largest identified in plants to date and they exhibit an unusual tissue-specific subcluster structure. Next, we characterize the genes in the identified biosynthetic gene clusters using heterologous expression in yeast and tobacco, in conjunction with in vitro enzyme assays. We discover two cytochromes P450 (CYP87G1 and CYP749B2) and a short-chain dehydrogenase (SDH2) responsible for formation of the lactone ring on the sterol side chain, a key chemical feature of withanolides. Two additional P450s (CYP88C7 and CYP88C10) and a sulfotransferase (SULF1) generate the characteristic A-ring structure of withanolides, featuring a C₁ ketone and C₂-C₃ unsaturation. The discovery of SULF1 as a core withanolide pathway enzyme challenges the conventional view of sulfotransferases as tailoring enzymes and suggests a wider role for this enzyme family in plant secondary metabolism. This work opens new avenues for the sustainable production of withanolides through biomanufacturing and for drug development leveraging the withanolide scaffold.

Leveraging System-Level Analyses and Techno-Economic Modeling to Inform the Viability of Electrochemically-Mediated CO₂ Separation

Thu, 01 May 2025 00:00:00 GMT

Leveraging System-Level Analyses and Techno-Economic Modeling to Inform the Viability of Electrochemically-Mediated CO₂ Separation Ripley-Kenyon, Katelyn M. Replacing fossil fuels with renewable energy and removing carbon dioxide (CO₂) via carbon capture, utilization, and storage (CCUS) are essential strategies for addressing the climate crisis and achieving net-zero emissions by 2050. While renewable energy is predicted to supply more than 60% of net electricity generation in the United States by mid-century, it is also predicted that coal and natural gas plants will remain operational in the near term to meet growing energy demands. This, coupled with the persistence of hard-to-decarbonize processes, requires point-source capture technologies to mitigate remaining CO₂ emissions. State-of-the-art CO₂ separation systems are typically based on low efficiency, temperature-swing cycles that exploit the natural affinity of alkanolamines for CO₂ at ambient conditions. Alternatively, electrochemical capture systems may enable CO₂ removal from flue gas streams at higher energetic efficiencies while also offering more modular and scalable designs. However, direct comparisons between the thermochemical and electrochemical approaches are scant, likely due to the nascency of the latter. In this thesis, I develop modeling frameworks that enable system-level comparisons of two types of electrochemical CO₂ capture (eCCC) technologies and the incumbent thermochemical, amine-based capture technologies. I first start by developing a reactive absorption model to predict the absorption column sizes required in “4–stage” eCCC systems (i.e., comprising of an electrochemical reactor, absorption column, and flash tank). I use the model to inform operating conditions and molecular properties that will allow these processes to utilize columns that are comparable in size to those presently deployed in thermochemical systems. While this helps address capital cost comparisons, to couple these effects with operating costs I next combine the absorption column model with an electrochemical cell model to predict the levelized cost of capture (LCOC) of the capture platforms at a coal pilot plant facility. This techno-economic model allows for thorough investigation of the property sets, operating conditions, and target cost factors that will lead to conditions where the electrochemical systems can compete economically with amine scrubbing systems. Next, this in-house model is used to probe the effects of scale and flue gas composition on the overall LCOC to provide commentary on the conditions and costs likely for operation at commercial-scale plants. Finally, I apply my knowledge of decarbonization efforts to inform realistic pathways for decarbonizing cement production facilities in the near-term. Ultimately, the goal of this thesis is to lay the foundation for quantitative comparisons between different technologies available for point-source capture applications while also offering models that can be used to investigate the viability of promising molecules and electrolytes in eCCC.

Uniqueness problems in mean curvature flow

Thu, 01 May 2025 00:00:00 GMT

Uniqueness problems in mean curvature flow Lee, Tang-Kai We investigate uniqueness phenomena in mean curvature flow, focusing on two central problems: the behavior of the flow near singularities and the extension of the flow beyond singular times. These questions have significant applications in geometry, topology, and analysis. For the first problem, with Jingze Zhu, we formulate a canonical way to study the limit model near a singularity of a generic closed mean curvature flow of surfaces. Using this framework, we establish a uniqueness result for singularity models. As a consequence, we resolve a uniqueness problem for gradient flow lines in ordinary differential equation theory, related to a question posed by Thom and Arnold, and revisited by Colding–Minicozzi. For the second problem, with Alec Payne, we examine the level set flow as a weak formulation that ensures long-time existence and uniqueness of mean curvature flow past singularities. This approach, however, can lead to fattening, a phenomenon reflecting genuine non-uniqueness of the extended flow. While genuine uniqueness cannot always be expected, we address this challenge by establishing an intersection principle for comparing two intersecting flows. We prove that level set flows satisfy this principle in the absence of non-uniqueness. Finally, with Larry Guth, we explore a problem concerning homotopy classes of maps between spheres. Recent progress on this problem relies on delicate analysis of high codimensional graphical mean curvature flow. We use a direct method to refine a homotopy criterion for maps between low-dimensional spheres.

Quantum Steenrod operations and Fukaya categories

Thu, 01 May 2025 00:00:00 GMT

Quantum Steenrod operations and Fukaya categories Chen, Zihong The recent introduction of mod p equivariant operations to symplectic Gromov-Witten theory has fueled exciting developments in the field. In this thesis, we develop new tools for understanding these operations and explore an application to the quantum connection. In one direction, we construct certain operations on the equivariant Hochschild (co)homology of a general A∞-category. We show that when applied to the Fukaya category of a nondegenerated closed monotone symplectic manifold, this construction can be identified with the quantum Steenrod operations via Ganatra’s cyclic open-closed maps. A key ingredient in this identification is a new homotopy theoretic framework for studying various equivariant open-closed maps at once, using a combination of cyclic categories, edgewise subdivision and Abouzaid-Groman-Varolgunes’ operadic Floer theory. In another direction, we utilize quantum Steenrod operations, and Lee’s observation that it is related to the p-curvature of the quantum connection, to study singularities of the quantum connection in characteristic 0, and prove the exponential type conjecture for all closed monotone symplectic manifolds.

Organic influences on hydrated magnesium carbonate mineral formation

Thu, 01 May 2025 00:00:00 GMT

Organic influences on hydrated magnesium carbonate mineral formation Baldes, Matthew J. Carbonate minerals retain organic compounds and preserve textural and chemical evidence of microbial activity early in the geologic record of Earth. For this reason, magnesium carbonates thought to be associated with lacustrine deposits in Jezero Crater are an important target of the Mars Sample Return Mission. The presence of hydrated magnesium carbonates in the deposits suggests that these minerals experienced minimal postdepositional alteration and may have the potential to preserve biosignatures from a habitable early martian environment. Microbial influences on calcium carbonate precipitation are well documented, but magnesium carbonates have received considerably less attention as a result of their relative scarcity in terrestrial deposits. The few modern lacustrine environments where hydrated magnesium carbonate minerals form have been proposed as analogs for Jezero Crater. Precipitation often occurs in association with microbial communities in these alkaline lake systems, but little is known about the potential for hydrated magnesium carbonates to preserve biosignatures, especially in depositional environments analogous to the carbonate sediments and coatings identified by the Perseverance Rover in Jezero Crater. This thesis explores organic influences on hydrated magnesium carbonate precipitation and the potential for these minerals to retain evidence of microbial activity. I begin by culturing cyanobacterial biofilms in solutions that replicate natural lacustrine environments where hydrated magnesium carbonate precipitation occurs. I designed experiments to isolate the role of cyanobacterial extracellular polymeric substances (EPS) in mediating the mineralogy of hydrated magnesium carbonate precipitates and rate of amorphous magnesium carbonate (AMC) maturation. I also compared the precipitates that formed in association with cyanobacterial biofilms to those formed under inorganic conditions to determine if hydrated magnesium carbonates preserve biosignatures. The results from these experiments demonstrate that cyanobacterial EPS promotes the early stabilization of the hydrated magnesium carbonate mineral dypingite and that biologically associated precipitates encapsulate cells and retain organic compounds detectable with Raman spectroscopy. I complement this laboratory work by seeking to identify similar spectroscopic and textural evidence of microbial activity in a range of carbonate deposits from Lake Salda, Türkiye including sands, crusts on coarse siliciclastic sediments, and alteration veins in serpentinized ultramafic bedrock. Analyses of these samples revealed that hydromagnesite sands and crusts have a higher potential to preserve biosignatures than dolomite veins in a system analogous to Jezero Crater.

A probabilistic perspective on graph coloring

Thu, 01 May 2025 00:00:00 GMT

A probabilistic perspective on graph coloring Mani, Nitya Graph coloring is perhaps the most fundamental, deeply-studied, and well-known area in graph theory, with many of the most basic questions in the field still widely open. Graph coloring questions often have wide ranging applications across fields as diverse as statistical physics, theoretical computer science, route planning, disease spread, cybersecurity, circuit design, and network science more broadly. This thesis studies graph coloring from a probabilist’s perspective, focusing on graph coloring problems that share an underlying theme: given an exponentially large family of objects derived from a graph vertex-coloring, can we understand what a typical or random object in this large family looks like without manually searching through exponentially many alternatives? The majority of this thesis is centered around two basic graph coloring problems, each of which has been heavily studied and comes with a rich history and many applications. We begin this thesis by establishing a fourth moment phenomenon for the number of monochromatic copies of any fixed subgraph in a given graph sequence (when given at least eight colors). We also study, and in many special cases, characterize, failures of a fourth moment phenomenon to hold in the two-color regime. We then continue to our second major topic of study. We essentially resolve a folklore conjecture about the uniform distribution of proper colorings of a bounded-degree tree. As a consequence, we are able to make significant progress towards a longstanding conjecture in the statistical physics community and one of the oldest and most basic still-open questions in the field of approximate counting and sampling. We also disprove the efficacy of a particular, popular approach to tackling this pair of conjectures. Finally, we conclude the thesis by taking a different approach to studying typical samples from exponentially large families, applying the graph container method to study two coloring-adjacent questions: upper bounding the number of error correcting codes and understanding the structure of typical unit-distance avoiding sets in R².

Towards Bridging and Governing Decentralized Communities

Thu, 01 May 2025 00:00:00 GMT

Towards Bridging and Governing Decentralized Communities Saldías Fuentes, Belén Carolina "Unless the spaces in a building are arranged in a sequence which corresponds to their degrees of privacy the visits made by strangers, friends, guests, clients, family, will always be a little awkward." (Alexander, 1977) — Unlike physical spaces, where we can move seamlessly between different environments with varying degrees of privacy, much of our online experience occurs in noisy, crowded, and imposed public areas. This can undermine meaningful engagement, deepen social divides, and exacerbate anxiety, polarization, and distrust arising from unnecessary friction and misunderstandings. Moreover, while different communities with distinct values and norms often share these same public venues, they are typically subject to one-size-fits-all policies that fail to address local contexts. Consequently, toxic behavior is policed at the platform level rather than by the communities themselves, leading to oversimplified governance solutions that favor some communities while silencing others. Fortunately, emerging strategies in decentralized protocols and networks have begun to change this dynamic. Decentralized systems designed for local governance can empower communities to create more nuanced and context-sensitive rules. However, these approaches remain largely inaccessible to non-technical users and risk creating a "paradox of decentralization," wherein isolated servers or communities potentially deepen echo chambers. This thesis contends that by placing community governance and user agency at the center of online platforms—and by leveraging advances in large language models (LLMs)—we can build healthier digital spaces that foster pro-social interactions while respecting individual groups' autonomy. To explore these possibilities, this dissertation examines how intentional design principles can promote constructive communication in decentralized contexts. First, it presents a large-scale historical Reddit dataset encompassing over 230K removed posts across more than 19K mission-defined communities—that captures a diverse range of speech, community norms, and moderation approaches. By analyzing over 60K community rules, I propose an empirically grounded norms schema and reveal how the purpose statement correlates with pro-social behavior reflected in community-centered discourse. Building on these insights, the dissertation next tackles the challenge of shifting from centralized, top-down moderation to distributed, community-specific content governance. While centralized methods provide highly generalizable moderation powered by advanced AI, they hinder specificity and community-specific definitions of behavior, limiting community and user participation in shaping how their content is moderated and ranked. I prototype and evaluate tools for (i) explainable, decentralized content moderation—where interpretable models illuminate why a post is flagged or removed—and (ii) surfacing unspoken differences in the definitions and understanding of seemingly similar norms across communities. These prototypes show how LLMs can assist by clarifying value mismatches, supporting local decision-making, and enabling communities to mediate misunderstandings across divides. Finally, I consolidate these findings in a real-world social network platform called Odessa—a DEcentralized Social Systems App—deliberately designed as a user-friendly, decentralized environment that allows communities to define—and iteratively refine—their own norms, moderation, ranking algorithms, and, more generally, governance strategies. Through system deployment and user experiments, I investigate how participants navigate local governance controls and interact within bridged spaces across communities. Odessa's bridging mechanisms illustrate how communities can preserve distinct values without sacrificing cross-community connections. By open-sourcing Odessa, I provide a framework for researchers and practitioners to test human-AI partnerships in governance and a learning environment for apprentices. The results presented here underscore both the opportunities and challenges in democratizing content moderation, highlighting the pivotal role of transparent AI in promoting trust and mutual understanding. This dissertation makes the case that future social media ecosystems should emphasize bottom-up, community-driven governance aided by interpretable AI tools. By enabling communities to shape their social expectations through purpose and norms, explain decisions through transparent AI and access to human rationales, and forge connections with other communities, we can cultivate online environments where pro-social discourse thrives. In doing so, we move beyond merely "fighting toxicity" toward intentionally designing spaces that support constructive dialogue and genuine community development.

Efficient Optimal and Approximate Algorithms in Optimization Under Uncertainty

Thu, 01 May 2025 00:00:00 GMT

Efficient Optimal and Approximate Algorithms in Optimization Under Uncertainty Gonzalez, Victor Some of the most important and challenging decisions must be made with incomplete information. This lack of information can refer to ignorance regarding events or conditions that have happened or events that have yet to occur. This lack of information can relate to what decisions are available as well as the consequences of these decisions. Optimization under uncertainty has applications in a wide range of settings including hiring (How good is the candidate? Will a better candidate arrive?), disaster relief (Where are people who need help? How long do rescue teams have before they are in a more critical condition?), and manufacturing (How much will we be able to manufacture? What orders should we accept?). These problems can be solved naively by reformulating the problem as a deterministic problem, but this can dramatically increase the size of the problem making the naive reformulation to be computationally expensive to solve. We aim to develop efficient algorithms to solve optimization problems under uncertainty and construct approximate algorithms that quickly approximate the solution in instances that are too large to solve exactly. In Chapter 2, we discuss a secretary problem with generalized decisions. The goal is to “rank” incoming items arriving in an uncertain order. Once an item arrives, it must be assigned a rank before the next item arrives, and this cannot be changed when new items arrive. We exploit the structure of the problem using exact dynamic programming to construct an algorithm that can be used to compute an exact solution. Additionally, we develop heuristics that can be used to construct an approximate solution in larger problems. In Chapter 3, we discuss a search and rescue drone problem. In this problem, we construct routes to maximize the number of people who are in need of rescue in the event of a natural disaster. After constructing a nonlinear mixed-integer program (MIP), we construct simplifying policies that allow us to solve it in real-time allowing for updates as new information is learned. In Chapter 4, we discuss a two-stage stochastic knapsack problem. In manufacturing settings, orders must be accepted or rejected before it is known how much resources will be available to fill those orders. We formalize this problem as a two-stage stochastic knapsack problem. We construct lower bounds based on feasible solutions and upper bounds based on the optimal solutions of a relaxation of the problem. We then use these bounds to construct optimal solutions that beat traditional solvers. We then develop algorithms that construct approximate solutions for larger instances that perform well compared to the optimal solutions.

A Unified Adaptive Robust Optimization Approach to Electricity Markets Under Uncertainty

Thu, 01 May 2025 00:00:00 GMT

A Unified Adaptive Robust Optimization Approach to Electricity Markets Under Uncertainty Koulouras, Angelos Georgio Electricity grid operations in the US rely heavily on two short-term markets: the DayAhead Market (DAM) and the Real-Time Market (RTM). Although the DAM is the cornerstone of electricity markets, it does not adapt to the fast-changing reality in the grid, such as the increased uncertainties due to renewables. Therefore, the existing deterministic market suffers from inherent uncertainties and creates inefficiencies, which require ad hoc and suboptimal solutions, like out-of-market interventions by the grid operators. To address these issues, in this thesis, we advocate for an adaptive mindset in electricity markets and propose a unified and adaptive redesign of the DAM. The proposed market cooptimizes the existing DAM and out-of-market processes, like the Reliability Unit Commitment (RUC), under adaptive robust optimization (ARO). Through ARO, we explicitly procure and price flexibility using adaptive reserve products that provide generation plans contingent on the uncertainty in the RTM. The grid uncertainty is captured through uncertainty sets that contain all the scenarios against which the market operator hedges, while it is priced through new marginal pricing mechanisms. In Chapter 2, we provide marginal pricing for uncertainty in ARO as a technical enabler of the proposed market. We derive locational marginal prices for unit commitment problems with ARO under load and capacity uncertainty and provide guarantees on the participant incentives under worst-case uncertainty. These pricing mechanisms are then used in Chapter 3, which features the redesign of the DAM. Specifically, the proposed DAM eliminates RUC-like processes by introducing deterministic reserve products that were previously procured in a nontransparent way by the market operators. It also hedges against load forecast errors by using adaptive reserve products that reward participant flexibility. The overall design, which is also applied to ISO New England market data, increases the social welfare and reliability in the market and reduces the arbitrage opportunities. Finally, in Chapter 4, we provide data-driven uncertainty calibration methods for the proposed market. We determine the size of the uncertainty set using machine learning models and mixed-integer optimization, leveraging historical data that consist of covariates or features. This method has been successfully applied to wind generation forecasts from a vendor that caters to a large US grid operator.

On the external forcing of Indian Ocean climate variability across timescales

Thu, 01 May 2025 00:00:00 GMT

On the external forcing of Indian Ocean climate variability across timescales Tiger, Benjamin H. It is imperative to understand the dynamics of external climate forcings and the nature of the climate system’s responses for improved predictability. These forcings include low-probability, high-impact events like explosive volcanic eruptions as well as the continued injection of anthropogenic greenhouse gases into the atmosphere. This thesis explores how external forcings affect Indian Ocean climate in the past, present, and future using paleoclimate archives in conjunction with observational and climate model data. Chapter 2 presents a novel geochemical stalagmite record from northern Madagascar which spans the end of the last glacial period. Stable isotope and trace metal proxies indicate drier conditions in response to North Atlantic cooling events, such as Heinrich stadials, and wetter conditions during North Atlantic warming events, such as the Bølling–Allerød. These responses are opposite what would be expected from north-south shifts in the Intertropical Convergence Zone. Instead, we hypothesize that west-east tropical Indian Ocean temperature gradient variability akin to the modern-day Indian Ocean Dipole explains the consistent hydroclimate response to North Atlantic forcing reconstructed by eastern African sites. Chapter 3 explores the effects of volcanic eruptions on interannual Indo-Pacific climate variability using an ensemble of last millennium simulations. Following the largest tropical eruptions, these simulations demonstrate a consistent negative Indian Ocean Dipole response which leads an El Niño. This response scales with eruption intensity and persists for up to 8 years for the strongest events. We also find that Interdecadal Pacific Oscillation phasing at time of eruption preconditions the initial Indian Ocean Dipole response via low frequency thermocline depth modulation. Finally, in Chapter 4 we use marine sedimentary archives in combination with climate simulations to expand on the Atlantic-Indian Ocean teleconnection hypothesized in Chapter 2. The reconstructed west-east surface temperature gradient responds in lockstep to previous instances of Atlantic Meridional Overturning Circulation (AMOC) variability during the last glacial period, such as Heinrich stadials, the Bølling–Allerød, and the Younger Dryas. An analysis of single-forcing simulations featuring meltwater addition to the North Atlantic under glacial and interglacial boundary conditions further demonstrates this inter-basin connectivity. We find that in simulations of high greenhouse gas emission scenarios, uncertainties in future Indian Ocean temperature and precipitation patterns are attributable to uncertainties in the magnitude of future AMOC weakening. This thesis bridges disparate timescales and data sources to gain insight into how the external forcing of the Earth system works at a fundamental level, from geochemical records of abrupt climate transitions during the last ice age to numerical simulations of the Atlantic overturning slowing by the end of the 21st century.

Characterization and Quantification of Solid Electrolyte Interphases for Composition-Functionality Relationships at Lithium Metal Electrodes

Thu, 01 May 2025 00:00:00 GMT

Characterization and Quantification of Solid Electrolyte Interphases for Composition-Functionality Relationships at Lithium Metal Electrodes Steinberg, Katherine Julia Lithium (Li) has the lowest electrochemical reduction potential and density of any metal, making it an exceptionally desirable anode material for batteries and a powerful chemical reductant. However, the reducing nature which makes Li so useful brings challenges: it is thermodynamically unstable in practical liquid electrolytes, driving the formation of a passivation film called the solid electrolyte interphase (SEI). The SEI mediates transport and reactivity at the Li surface, and in practical systems it both consumes active Li directly and leads to spatial heterogeneity in fluxes to and from the lithium surface, resulting in inefficiency in plating and stripping. Together, these effects make the SEI the most important factor determining the efficiency of Li electrochemistry, but its nanoscale, heterogeneous, and reactive nature make it extremely challenging to study experimentally. As a result, existing understanding of the impact of composition and structure on SEI functionality is limited. This thesis aims to enhance conceptual understanding in this space, combining multimodal characterization, the design and application of informative model systems, and the quantification of key phases to reveal mechanistic insights that advance understanding of composition-functionality relationships at Li interfaces. To begin, this work focuses on the role of the SEI in Li-mediated electrochemical ammonia synthesis (LiMEAS), one of the most promising electrochemical pathways for nitrogen fixation. Here, quantification of major side products, multiscale imaging, and spectroscopic analysis were conducted methodically in four model systems, which introduced the presence of nitrogen gas and a proton donor separately. This study revealed that the electrolyte-derived SEI inhibits reactivity between Li and nitrogen, and that the proton donor is needed to disrupt this passivating interphase. Next, focus shifted to Li metal battery anodes. Lithium carbonate has long been considered beneficial in anode SEI, but the field has lacked a mechanistic explanation for its effects. Here, lithium carbonate was studied through the development of two model systems, a model SEI formed by sequentially reacting oxygen and carbon dioxide with metallic lithium, and Li-copper (Cu) half cells saturated with either argon or carbon dioxide. Through electrochemical impedance analysis on the model SEI, lithium carbonate was found to exhibit elevated conductivity compared to other common inorganic SEI materials. Cycling and subsequent titration analysis of Li-Cu cells revealed that carbon dioxide addition led to less inactive lithium formation during cycling, and that this avoided capacity loss was the driver behind increased Coulombic efficiency (CE) in numerous electrolytes. Finally, an analysis was conducted to decipher unresolved materials in a set of techniques for the quantitative analysis of Li anode Coulombic inefficiencies. These techniques directly quantify capacity losses from formation of inactive lithium and several SEI materials, but lack the ability to delineate between residuals lost during cycling or sample processing, and SEI materials not yet resolvable by quantitative techniques. Here, a set of measurements was developed to explicitly measure material losses during sample processing steps. This work confirmed that material losses do not alter broader trends between electrolytes, validating simpler approaches for electrolyte comparisons while also offering a protocol that can be used when quantitative material accounting is of particular importance. Together, these studies illustrate a multimodal approach for deriving mechanistic insights into the relationships between SEI composition and electrode performance.

Higher dimensional fractal uncertainty

Thu, 01 May 2025 00:00:00 GMT

Higher dimensional fractal uncertainty Cohen, Alex We prove that if a fractal set in Rᵈ avoids lines in a certain quantitative sense, which we call line porosity, then it has a fractal uncertainty principle. The main ingredient is a new higher dimensional Beurling and Malliavin multiplier theorem, which allows us to construct band-limited functions that decay rapidly on line porous sets. To prove this theorem, we first explicitly construct certain plurisubharmonic functions on Cᵈ. Then, following Bourgain, we use Hörmander’s L² theory for the ¯∂ equation to construct band-limited functions. The main theorem has since been applied by Kim and Miller to lower bounds for the mass of eigenfunctions on higher dimensional hyperbolic manifolds.

Invertible Functorial Field Theory for Symmetry Breaking and Interactions in Quantum Field Theory

Thu, 01 May 2025 00:00:00 GMT

Invertible Functorial Field Theory for Symmetry Breaking and Interactions in Quantum Field Theory Krulewski, Cameron We apply invertible field theories to study two questions in quantum field theory. Specifically, we study reflection-positive fully-extended invertible field theories on manifolds with twisted spin structures, which are computed as Anderson-dual bordism groups [1, 2]. In high energy physics, invertible field theories represent anomalies of quantum field theories. Our first application is toward ’t Hooft anomaly matching—a method first developed in the 1980s in which one treats anomalies as invariants of theories of interest and uses them to compute how quantum field theories change under physical processes. Specifically, we model three related processes around a form of spontaneous symmetry breaking via a charged order parameter using a twisted Gysin sequence of Anderson-dual bordism groups. We study the Smith maps of Madsen-Tillmann spectra that underlie the sequence, collecting examples and cataloging periodicities. Finally, we compute an extensive set of examples of physical interest and draw physical predictions from the results. In condensed matter physics, invertible field theories model the low energy field theories of symmetry-protected topological phases (SPTs). In this second application, we develop and compute homotopical free-to-interacting maps to compare two classifications of fermionic SPTs: those for free (i.e. non-interacting) models, and more general interacting classifications. These maps contribute to what has been a prolific line of research in the physics literature for the past fifteen years. Generalizing Freed--Hopkins [1], we construct maps from K-theory to twisted spin IFTs using T-duality and twisted versions of the spin orientation of K-theory [3]. We focus on two situations: weak phases [4, 5], which are SPTs protected by discrete translation symmetry, and primed phases [6], which are closely related to the famous tenfold way [7, 8], but which have a very different interacting classification. In the latter case, we demonstrate the dependence of the interacting classification on more than the Morita class of the symmetry algebra.

Computational Tradeoffs and Symmetry in Polynomial Nonnegativity

Thu, 01 May 2025 00:00:00 GMT

Computational Tradeoffs and Symmetry in Polynomial Nonnegativity Harris, Mitchell Understanding when a polynomial is nonnegative on a region is a fundamental problem in applied mathematics. Although exact conditions for nonnegativity are computationally intractable, there has been a surge of recent work giving sufficient conditions for nonnegativity to address its many practical applications. A major trend in this direction has been the use of convex optimization to characterize polynomials that are sums of squares (SOS); nevertheless, this well-studied condition can be computationally intensive for polynomials of moderate degree and dimension. This thesis addresses the challenge of balancing computational cost against the strength of sufficient conditions for nonnegativity. We make progress towards bridging the gap between simple but crude sufficient conditions, and the more powerful but expensive SOS approach. In the first part, we introduce new certificates of nonnegativity that may be used when SOS is too expensive yet cheaper sufficient conditions are too conservative. For this, we leverage different features of the polynomials, including its Bernstein coefficients, a lower-degree interpolant, or its harmonic decomposition. In the second part, we construct coordinate-invariant sufficient conditions for nonnegativity and study the symmetry properties of the space of Gram matrices. By considering it as a representation of GL(n,R) and combining this module structure with classical invariant theory, we construct an explicit equivariant map for nonnegativity certification. We further introduce an alternative approach using equivariant neural networks, analyzing their benefits and limitations.

Affine Springer Fibers and the Kazhdan-Lusztig Map

Thu, 01 May 2025 00:00:00 GMT

Affine Springer Fibers and the Kazhdan-Lusztig Map Chua, Anlong Let G be a connected reductive group with Lie algebra g and Weyl group W. Let P ⊂ G((t)) be a parahoric subgroup with Levi quotient Gₚ. Using the topology of Lie P, Kazhdan and Lusztig define a map from nilpotent orbits in Lie Gₚ to conjugacy classes in W. This thesis proves compatibilities between Kazhdan-Lusztig maps associated to different parahoric subgroups, as well as the Kazhdan-Lusztig map for the Langlands dual. These compatibilities come from studying the W-representation on the cohomology of affine Springer fibers. The main tool is Yun’s Global Springer Theory. We give two applications of these compatibilities. The first is an affine analog of the classical picture relating singular supports of IC sheaves on the flag variety with special nilpotent orbits. The second is a resolution of Lusztig’s conjecture that strata can be described by fibers of (parahoric) Kazhdan-Lusztig maps.

The Changing Role of Reactive Nitrogen in the Troposphere

Thu, 01 May 2025 00:00:00 GMT

The Changing Role of Reactive Nitrogen in the Troposphere Dutta, Ishir Nitrogen is the most abundant molecule in the Earth’s atmosphere and is one of the essential ingredients for life as we know it. Human activities, especially over the last century, have radically perturbed the natural nitrogen cycle, primarily via emissions of reduced nitrogen from the production and use of fertilizer for agriculture and of oxidized nitrogen from the combustion of fossil fuels. Nitrogen oxides play a central role in driving tropospheric chemistry and are a key ingredient of fine particulate matter, acid rain, and ozone. However, despite this long-understood importance of reactive oxidized nitrogen (NOy) species, even modern chemical transport models have struggled to accurately represent their chemistry. This thesis spans three projects that seek to characterize and explain possible sources of this uncertainty. The first project presents a comprehensive budget of reactive oxidized nitrogen in the troposphere using a state-of-the-science chemical transport model, and observational constraints for this budget from remote troposphere flight campaign data. We also provide modeled estimates for the chemical fluxes between key NOy species, finding that species beyond those that have been the foci of previous work play a crucial role in driving overall chemical cycling. In the second project we explore the sensitivity of this NOy budget to uncertain multiphase chemistry, including the photolysis of nitrate aerosol, the reactive uptake of nitrogen dioxide on aerosol surfaces, and the uptake of nitric acid on dust. We find that these processes may have substantial regional or temporal importance, but they have limited effects on the global NOy budget and are insufficient to explain inter-model discrepancies. Finally, we investigate the utility of long-term wet and dry deposition measurements made in the continental United States as a constraint on regional anthropogenic emissions trends of acid rain precursors (nitrogen and sulfur oxides). We find that dry deposition fluxes follow anthropogenic emissions trends, and wet deposition fluxes are likely more representative of total regional emissions (natural and anthropogenic). Taken together, these studies provide novel, holistic constraints on reactive oxidized nitrogen and identify key chemical processes that govern the fate of NOy in the troposphere. As anthropogenic emissions continue to decline and the effects of climate change intensify, these insights and such a framework will be useful in accurately predicting future atmospheric chemistry and composition.

Geometric representation learning for chemical property prediction, structure elucidation, and molecular design

Thu, 01 May 2025 00:00:00 GMT

Geometric representation learning for chemical property prediction, structure elucidation, and molecular design Adams, Keir Alexander Joseph Molecular representation learning has revolutionized computer-aided chemistry by enabling the automatic extraction of arbitrarily complex patterns from datasets of (potentially labeled) molecular structures via deep neural networks. In predictive chemistry, deep learning is increasingly being used to replace expensive physics-based simulations and even experimental measurements of chemical properties. In generative chemistry, deep generative models are powering molecular design and optimization campaigns across chemical industries. Notably, this paradigm shift has been driven by the development of sophisticated representation learning algorithms that encode and decode molecular structures with increasing geometric detail – from minimal SMILES strings to elaborate atomistic structures. Yet, many aspects of molecular structure remain neglected by leading geometric representation learning models. Accordingly, this thesis advances the geometric representation learning of molecular structure to create new opportunities in chemical property prediction, structure elucidation, and molecular design. This thesis begins by highlighting surprising failure modes of graph neural networks when predicting properties dependent on chirality and conformational isomerism. A new stereochemistry-tailored model is then developed to imbue graph networks with tetrahedral chiral expressivity while evading pitfalls plaguing preceding 2D and 3D graph networks. This thesis then examines how the geometric quality of structures encoded by 3D networks impacts their accuracy in property prediction tasks requiring the model to reason about conformational flexibility. Neglecting structural characteristics that are challenging to model is also common in computational chemistry. In nuclear magnetic resonance (NMR) prediction, for example, quantum chemical calculations typically estimate magnetic shieldings from stationary gas-phase geometries – ignoring vibrations and explicit solvent. To advance chemical structure elucidation, this thesis next develops neural surrogates for magnetic shielding calculations that, when integrated with molecular dynamics simulations, provide access to unprecedented accuracy in solvent-sensitive NMR spectra prediction. Finally, this thesis advances de novo molecular design by explicitly representing 3D shapes, electrostatics, and non-covalent interactions in deep generative models for small molecules. A shape-conditioned variational autoencoder is first developed to design chemically diverse molecules that can adopt desired conformational shapes, like ligand binding poses. This strategy is then generalized into a powerful interaction-aware diffusion modeling framework to comprehensively enable bioisosteric replacement in ligand-based drug design.

Theoretical Foundations of Flow-based Methods for Sampling and Generative Modeling

Thu, 01 May 2025 00:00:00 GMT

Theoretical Foundations of Flow-based Methods for Sampling and Generative Modeling Ren, Zhi (Robert) Sampling from an arbitrary probability distribution is a central problem in computational statistics and machine learning. Transportation of measure offers a useful approach to this problem: the idea is to construct a measurable map that pushes forward a relatively simple source distribution to the target probability distribution. One can then simulate from the target distribution by drawing samples from the source distribution and evaluating the transport map. This construction is applicable to both generative modeling and variational inference; when the map is invertible, one can also estimate the density of the target measure by evaluating the density of the pushforward of the source distribution under the inverse transport map. Over the past decade, various parameterizations of such transports have been proposed. Generally speaking, they fall into two categories: the static approach, where the displacement from x to T(x) is represented directly and the dynamic approach that employs evolution of measures by some differential equation over some fictitious time. While many of these models have achieved enormous success in practical applications, their theoretical underpinnings remain largely unexplored. In this thesis, we provide a theoretical foundation for flow-based methods for sampling and generative modeling, and a unified view of both continuous and discrete-time approaches. In the first part of the thesis, we address the approximation theory of flow based methods. In particular, we show how the regularity of the underlying ODE velocity field relates to the regularity of densities and prove related neural network approximation bounds. In addition, we show how introduction of a time-reparameterized schedule can dramatically improve the regularity of the velocity, helping resolve potential singularities. In the second part of the thesis, we focus on the interplay between flow-based models and nonparametric statistics. In particular, we consider pullback density estimators under these flow based models from likelihood-based objectives. The estimators we consider arise from both discrete and continuous-time parameterizations of the transport, and the underlying function classes we consider include Hölder balls and neural networks. In all these cases, we show they achieve near minimax optimal rate for learning s-smooth densities.

Dual Pairs and Disconnected Reductive Groups

Thu, 01 May 2025 00:00:00 GMT

Dual Pairs and Disconnected Reductive Groups Gaetz, Marisa In R. Howe’s seminal paper, “Remarks on classical invariant theory,” he introduces the notion of a Lie algebra dual pair (a pair (g₁, g₂) of reductive Lie subalgebras of a Lie algebra g such that g₁ and g₂ equal each other’s centralizers in g) and the notion of a Lie group dual pair (a pair (G₁, G₂) of reductive subgroups of a reductive Lie group G such that G₁ and G₂ are each other’s centralizers in G). Both notions have since been widely used and studied. This thesis extends what is known about the classifications of complex reductive Lie group and Lie algebra dual pairs, and establishes a step towards a more general framework for understanding complex reductive Lie group dual pairs. In the first part of this thesis, we classify the reductive dual pairs in the complex classical Lie groups: GL(n, C), SL(n, C), O(n, C), SO(n, C), and Sp(2n, C). We also establish some general relationships between Lie group dual pairs and dual pairs in corresponding Lie algebras and quotient groups. These relationships lead to complete classifications of the reductive dual pairs in the complex classical Lie algebras (gl(n, C), sl(n, C), so(n, C), and sp(2n, C)) and preliminary progress towards classifying dual pairs in the projective classical groups (P GL(n, C), P Sp(2n, C), P O(n, C), and P SO(n, C)). In the second part of this thesis, we complete an explicit classification of the semisimple Lie algebra dual pairs in the complex exceptional Lie algebras, initially outlined by H. Rubenthaler in a 1994 paper. This explicit classification makes Rubenthaler’s 1994 result more complete, usable, and understandable. A major obstacle to understanding reductive Lie group dual pairs is their potential disconnectedness. Inspired in part by this obstacle, in the third part of this thesis we describe the possible disconnected complex reductive algebraic groups E with component group Γ = E/E₀. We show that there is a natural bijection between such groups E and algebraic extensions of Γ by Z(E₀). Finally, in the last part of this thesis we classify the reductive dual pairs in P GL(n, C). While the connected dual pairs in P GL(n, C) can be easily understood using tools from the first part of this thesis, the classification of the disconnected dual pairs in P GL(n, C) is much more difficult and requires tools from the third part of this thesis. This serves as the first complete classification of dual pairs in a non-classical group and as a step towards understanding how disconnectedness factors into the classification of dual pairs more generally.

The Average Size of 2-Selmer Groups of Elliptic Curves in Characteristic 2

Thu, 01 May 2025 00:00:00 GMT

The Average Size of 2-Selmer Groups of Elliptic Curves in Characteristic 2 Achenjang, Niven Let K be the function field of a smooth curve B over a finite field k of arbitrary characteristic. We prove that the average size of the 2-Selmer groups of elliptic curves E/K is at most 1 + 2ζʙ(2)ζʙ(10), where ζʙ is the zeta function of B. In particular, in the limit as q = #k ! ∞ (with the genus g(B) fixed), we see that the average size of 2-Selmer is bounded above by 3, even in “bad” characteristics. This completes the proof that the average rank of elliptic curves, over any fixed global field, is finite. Handling the case of characteristic 2 requires us to develop a new theory of integral models of 2-Selmer elements, dubbed “hyper-Weierstrass curves.”

Cross-shore Transformation of Breaking Random Waves in the Surfzone

Thu, 01 May 2025 00:00:00 GMT

Cross-shore Transformation of Breaking Random Waves in the Surfzone Chen, Jinshi The transformation of breaking waves in the surfzone, including the evolution of the roller, the foamy air-water mixture on the surface of a breaking wave, and the turbulence, determines the wave-driven onshore-directed mass transport, the vertical structure of the compensating return flow (undertow), and the increase in the mean water level (setup). A two-phase Reynolds-Averaged Navier-Stokes (RANS) model and field and laboratory observations are used to study the cross-shore transformation of the roller, turbulence, and undertow resulting from irregular breaking waves. Modeled wave heights, wave spectra, setup, and undertow agree well with field and laboratory observations on barred and unbarred bathymetry. The roller forcing contributes 50% - 60% to the setup. The horizontal advection and turbulence each contribute ∼ 20% to the setup, whereas the contribution of bottom stress is largest (up to 20%) for shallow sandbar crest depths. The majority of the energy transferred to the roller is dissipated internally, while 15% - 25% of the energy in breaking waves first is transferred to the roller and then diffused back to the water column. Internal dissipation of roller energy increases with increasing depth of the sandbar crest, possibly indicating a change from plunging to spilling breakers. The momentum flux balance in the mid- and lowerwater column is between the wave, vertical turbulence transfers, vertical inertia, and setup, whereas near the surface the roller and pressure slope are important. Turbulence transports momentum downwards, while vertical inertia transfers momentum upwards. Turbulence production dominates the near-surface turbulence-energy-flux balance, and its penetration depth in the trough onshore of the sandbar is correlated with the local wave height. The roller thickness is related to the local wave height. Surfzone turbulence is more anisotropic than plane-wake turbulence, and is dominated by cross-shore normal stresses. Cross-shore vertical two-dimensional anisotropy is dependent on the cross-shore position in the surfzone, vertical shear of the cross-shore current, wave directional spread, frequency, and proximity to the seafloor. The three dimensional turbulence structure is related to the total vertical current shear, and to the directions of both mean currents and waves. Horizontal turbulence length scales are larger than the vertical length scales, consistent with prior studies.

Advancing Artificial Intelligence for Efficient and Synthesizable In-silico Molecular Design

Thu, 01 May 2025 00:00:00 GMT

Advancing Artificial Intelligence for Efficient and Synthesizable In-silico Molecular Design Gao, Wenhao Small organic molecules possess an astronomical number of structural possibilities and a wide range of functionalities, holding immense potential to provide material-level solutions to critical societal challenges such as health and the environment. However, the discovery of molecules with functionalities tailored to specific applications remains a challenging, timeconsuming, and resource-intensive process, often relying on trial-and-error experimentation. Recent advances in computational techniques—particularly in artificial intelligence—offer promising solutions to this inefficiency. These developments are paving the way toward a more systematic and efficient approach to molecular discovery, enabling the design of novel functional molecules tailored to specific needs and accelerating the development of solutions to urgent issues in health, sustainability, and energy. This thesis presents algorithmic advances in artificial intelligence, particularly deep learning, for de novo molecular discovery, framed as a black-box optimization problem with a focus on small organic molecules. The contributions span three core aspects: The first section focuses on improving the sample efficiency of molecular optimization. A central capability of any molecular design algorithm is to determine which direction to explore next within chemical space in order to identify molecules with more optimal properties, given a limited set of known examples. Due to the inherent trade-off between computational efficiency and predictive accuracy in modeling methods, it is crucial to evaluate as few candidate molecules as possible to identify the optimal structure. This section introduces the problem formulation and benchmarking efforts for sample-efficient molecular optimization, followed by several approaches aimed at enhancing efficiency. The second section addresses the challenge of ensuring synthetic accessibility during molecular design. For small organic molecules with non-trivial syntheses, any design that cannot be realized in the lab has limited practical value. This presents a unique constraint in small molecule design that often renders direct adoption of algorithms developed for language or vision tasks ineffective. After framing the problem, this section introduces a generative modeling framework that integrates synthesis and design, ensuring that the search is constrained to synthesizable chemical space. It further introduces the concept of “generative molecular projection” and demonstrates its application in balancing sample efficiency and synthetic feasibility. The third section targets the improvement of oracle accuracy for molecular discovery. Achieving both accurate and efficient prediction of molecular properties has long been a central goal in computational chemistry. While deep learning has shown promise in breaking the traditional trade-off between accuracy and efficiency by leveraging large-scale historical data, its full potential—especially for directly learning experimentally measured bioactivities under data-scarce conditions—has yet to be realized. This section presents a benchmarking effort on applying deep learning to therapeutic-related property prediction, and introduces substrate scope contrastive learning as a strategy to learn reactivity-related patterns from published reaction datasets. Together, these three components present a systematic, data-driven methodology for small organic molecule discovery that minimizes the need for extensive domain expertise. The algorithms developed in this thesis are designed to support autonomous workflows, potentially enabling closed-loop molecular discovery that maximizes efficiency and reduces both cost and reliance on human intuition. While the demonstrations in this thesis primarily target pharmaceutical applications, the methods are task-agnostic and can be readily extended to broader material discovery efforts.

Leveraging Competitive Sorption in Microporous Polymer Membranes to Enhance Gas Separation Performance

Thu, 01 May 2025 00:00:00 GMT

Leveraging Competitive Sorption in Microporous Polymer Membranes to Enhance Gas Separation Performance Dean, Pablo A. Chemical separations account for roughly half of the United States’ industrial energy consumption, 49% of which is attributed to distillation alone. Membrane-based systems, on the other hand, offer a more energy-efficient alternative to conventional separation processes because they do not require thermally intensive phase changes to operate. Specifically, polymer membranes with a more permanent porosity (termed “microporous”) have gained attention due to their impressive combination of permeability (throughput) and permselectivity (separation efficiency) relative to the empirically defined “upper bound” for membrane materials. Traditionally, the permeability of a membrane for a gas is defined by the product of the gas’s diffusivity and sorption coefficient in the material. By extension, a membrane’s permselectivity can be broken down into the product of its diffusion selectivity and sorption selectivity. Microporous polymer membranes exhibit impressive diffusion selectivity due to their small free volume elements (< 2 nm) and rigid backbones. However, separating gases based primarily on size can become exceedingly difficult given that some gases differ in kinetic diameter by less than an angstrom. Instead, recent advancements in the design of microporous polymers have indicated that a phenomenon known as competitive sorption can be used to enhance separation performance by leveraging gas–polymer interactions instead of differences in gas diffusivity. This thesis investigates how the increase of sorption selectivity through competition between gases can be exploited to enhance the permselectivity of microporous polymer membranes. Specific focus is placed on the archetypal polymer of intrinsic microporosity (PIM-1) and its amine-functional analog (PIM-NH₂) to study how enhanced acid-gas (CO₂ and H₂S) sorption brought on by amine functionality positively impacts separation performance. To confirm the generalizability of these trends, competition effects in the microporous poly(arylene ether) (PAE) backbone were studied as well. To investigate more industrially viable membranes while retaining strong gas–polymer interactions afforded by the amine group, this PAE backbone was also used to develop 8 solution-processable tertiary-amine-functional analogs. Lastly, in an effort to study the effects of water vapor on CO₂-focused separations in amine-functional microporous polymer membranes, a humidified gas permeation apparatus was developed and used to measure dry and humidified CO₂ transport in PIM-1, PIM-NH₂, and a novel secondary-amine-functional analog, PIM-NHiPr. Taken together, this thesis focuses on the fundamentals and practical implications of leveraging competitive sorption to enhance performance in application-relevant and multi-component gas mixtures. More specifically, this work provides valuable insight regarding amine functionalization and its strong effects on sorption energetics and humidified gas transport that will help to inform future design of polymer membranes for gas separations.

Catalytic implications of confined solvent ensembles within Lewis acid zeolites

Thu, 01 May 2025 00:00:00 GMT

Catalytic implications of confined solvent ensembles within Lewis acid zeolites Johnson, Blake A. Lewis acidic zeolites are microporous crystalline materials that offer promise as catalysts for the activation and conversion of biomass-derived precursors in the liquid-phase due to their unique water-tolerance and synthetic versatility. The active site environment in zeolite catalysts is multifaceted in nature and is composed of a primary catalytic binding site, the secondary pore structure that confines such binding sites, and occluded solvent and reactant molecules that interact with adsorbed species. Moreover, Lewis acidic heteroatoms can adopt structurally diverse coordination that selectively catalyze different classes of chemical transformations and can be difficult to control synthetically or characterize spectroscopically. In this thesis, precise mechanistic interpretation of liquid-phase zeolite catalysis was realized through the development of synthetic, spectroscopic, and kinetic methods that decouple complex active site structures and probe the interactions that occur between confined active sites, solvent and reactant molecules, and adsorbed intermediates and transition states. First, we show how hydrophobic Beta zeolites containing framework Sn atoms catalyze transfer hydrogenation reactions of cyclohexanone in a 2-butanol solvent 10x faster than their hydrophilic analogues. This rate enhancement stems from the ability of hydrophobic Sn-Beta to inhibit the formation of extended liquid-like 2-butanol oligomers and promote dimeric H-bonded 2-butanol networks. The ordered H-bonding solvent network present in hydrophobic Sn-Beta stabilizes the transfer hydrogenation transition state to a greater extent than the liquid-like 2-butanol solvent present in hydrophilic Sn-Beta, giving rise to higher turnover rates on hydrophobic Sn-Beta. Additionally, reactant adsorption within hydrophobic Sn-Beta is entropically-driven by the breakup of intraporous solvent-solvent interactions, resulting in positive enthalpies of adsorption that are partially compensated by an increase in the solvent reorganization entropy. These results emphasize the ability of the zeolite pore to regulate the structure of confined non-aqueous H-bonding solvent networks, which offers an additional dimension to modulate adsorption and reactivity. Next, we extend our studies to understand how different intraporous alcohol networks reorganize in response to adsorbate sterics and the presence of non-H-bonding co-solvents. Here, we find that first-order rates for methyl-cyclohexanone transfer hydrogenation are ~2-5x higher than for tert-butyl-cyclohexanone, but converge in the zero-order regime across all temperatures (333-393 K) in a bulk 2-butanol solvent. These results show that, while intrinsic bond-activation steps at the active site are largely independent of molecular functionalization of the ketone reactant, adsorption within hydrophobic Sn-Beta is still driven by the breakup of intraporous solvent-solvent interactions. Furthermore, comparisons between bulk toluene or acetonitrile solvents, with 1 M 2-butanol as a reactant, show the significance of intraporous solvent for stabilizing kinetically-relevant species and the complex interdependencies between solvent and catalyst hydrophilicity. Apparent zero-order activation enthalpies and entropies increase with decreasing solvent polarity over hydrophobic zeolites indicating that the transition state is more tightly bound to the open Sn site when first-shell solvent molecules become more polarizing. Conversely, adsorption and activation entropies and enthalpies measured on hydrophilic zeolite in toluene and acetonitrile solvents are nearly identical to those measured in a bulk 2-butanol solvent, suggesting that the intraporous solvating environment in bulk, non-H-bonding co-solvents is similar to that observed when bulk 2-butanol is the solvent. Finally, we exploit the ability of carbonyl groups to measure electric field differences arising from the different intraporous solvent structures through the vibrational Stark effect. By measuring infrared absorption spectra of Ti-bound acetone in Beta zeolites of varying framework hydrophobicity across a wide range of non-coordinating solvents, we find unique electric field differences arising from distinct solvation under nanoconfinement. Moreover, in the absence of intraporous solvent, we observe a ~7 cm-1 shift in the Ti-bound carbonyl stretching frequency. These results suggest that local differences in the Lewis acid site environment, which influence observed kinetics across reaction classes, arise from the synthetic protocol used to produce each material. Taken together, the results of this thesis reveal how different solvent-mediated, non-covalent interactions control liquid-phase reactivity within porous, Lewis acid zeolite catalysts. It is our hope that the kinetic and spectroscopic approaches advanced here will provide a useful roadmap for further experimental investigations into the catalytic implications of confined solvent.

Algorithmic Advances for Fair and Efficient Decision-Making in Online Platforms

Thu, 01 May 2025 00:00:00 GMT

Algorithmic Advances for Fair and Efficient Decision-Making in Online Platforms Chen, Qinyi Modern online platforms—such as recommendation systems, advertising markets and e-commerce sites—operate in dynamic and complex environments where efficient algorithmic decision-making is essential. These platforms must continuously adapt to rapidly changing user behaviors, market fluctuations, and data uncertainties while optimizing for both learning efficacy and revenue generation. However, focusing solely on performance can lead to biased outcomes and inequitable treatment of users and items, raising concerns about fairness. Balancing efficiency and fairness is therefore crucial for sustainable platform growth. In this thesis, we tackle these challenges by developing novel algorithmic frameworks and methods that integrate fairness considerations with robust learning and optimization techniques. We explore these problems from three distinct perspectives, each contributing to enhancing the decision quality and fairness considerations in online decision-making. In Chapter 2, we first focus on the topic of efficiency, by addressing the challenge of performing online learning in a highly non-stationary environment. User behaviors and preferences often change over time, making it difficult for traditional algorithms to maintain good performance. This issue is particularly prevalent in real-world applications such as recommendation systems and advertising platforms, where shifts in user dynamics can undermine decision-making efficacy. To tackle this, we propose a novel algorithm for the widely adopted multi-armed bandit framework that enables platforms to adaptively learn in a fast-changing environment characterized by auto-regressive temporal dependencies. In Chapter 3, we shift our focus to the realm of fairness and explore how fairness considerations can be effectively integrated into the context of assortment planning. As algorithmic recommendations become integral to platform operations, a purely revenue-driven approach can result in highly imbalanced outcomes, leading to certain items receiving minimal exposure and exiting the platform in the long run. To address this, we develop a combinatorial optimization framework that incorporates fairness constraints, ensuring equitable exposure and opportunities for all items on the platform. We design a series of polynomial-time approximation algorithms to solve the fair assortment problem. Through numerical studies on both synthetic data and real-world MovieLens data, we showcase the effectiveness of our algorithms and provide insights into the platform's price of fairness. In Chapter 4, we bridge the topics of fairness and learning efficiency by examining how to achieve multi-stakeholder fairness in a multi-sided recommendation system. Here, the challenge is multifaceted, including ensuring high platform revenue, maintaining fair outcomes for diverse stakeholders, and enabling robust learning amidst data uncertainty. We propose a novel optimization framework that maximizes platform revenue while enforcing fairness constraints for both items and users, accommodating various fairness notions and outcome metrics. Building on this, we introduce a low-regret online learning and optimization algorithm that dynamically balances learning and fairness—two objectives that are often at odds. Finally, we demonstrate the efficacy of our approach via a real-world case study on Amazon review data and offer actionable guidelines for implementing fair policies in practice.

The interaction of shock waves with porous materials

Sat, 01 Jan 1983 00:00:00 GMT

The interaction of shock waves with porous materials McMillan, Charles Frederick. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1983; Includes bibliographical references.

An operational analysis of industrial research

Tue, 01 Jan 1957 00:00:00 GMT

An operational analysis of industrial research Freeman, Raoul J. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics and Social Science, 1957; Vita.; Bibliography: leaves 101-106.

Structural and biochemical characterization of RNA polymerase II transcription

Sat, 01 Feb 2025 00:00:00 GMT

Structural and biochemical characterization of RNA polymerase II transcription Su, Bonnie G. Eukaryotic development requires precise temporal regulation of gene expression orchestrated through a series of complex mechanisms. One such mechanism involves pausing of RNA polymerase II (Pol II) in the promoter-proximal region of genes. Pausing is stabilized by the protein complexes DRB-sensitivity inducing factor (DSIF) and negative elongation factor (NELF). Prior structural and biochemical studies provide specific mechanisms for stabilization of paused Pol II by NELF. However, cellular data suggests that NELF can accompany actively elongating Pol II into the gene body, indicating that NELF may be able to associate with pol II without enforcing Pol II pausing. This thesis presents cryo-electron microscopy structures of Pol II-DSIF-NELF complexes with NELF in two distinct conformations on the surface of Pol II, the paused state and the poised state. The poised state does not support a tilted RNA-DNA hybrid, a key characteristic of pausing, indicating that NELF in the poised state is compatible with elongating Pol II. Furthermore, Pol II bound to NELF in the poised conformation can accommodate TFIIS binding simultaneously, allowing reactivation of Pol II at sites of pausing or backtracking. Finally, a region of the flexible NELF-A tentacle engages with the RPB2 protrusion, an interface necessary for pausing. These results define how NELF can support both Pol II pausing and elongation and provide the molecular basis for how transcription can be reactivated when NELF is bound to Pol II.

Synaptic Multimodal Imaging and Molecular Network Inference

Wed, 01 May 2024 00:00:00 GMT

Synaptic Multimodal Imaging and Molecular Network Inference Falkovich, Reuven All cognitive function is reliant on synaptic function – the molecular computation that integrates activity history, chemical environment, and the genetic state of its pre- and post-synaptic neurons to modulate neuron-neuron communication through synaptic plasticity. This computation is performed by the highly compartmentalized, tightly regulated, and complex network of interactions between synaptic activity and hundreds of proteins and the mechanisms that regulate them. Isolating individual processes loses the context in which they occur, while bulk analyses average over highly heterogeneous populations and lose correlation information. A top-down study of the entire system in action requires measurement of multiple synaptic parameters – composition and activity - simultaneously in individual synapses. Building on a previously developed probe exchange multiprotein imaging technique, this thesis presents MINI-ME, a versatile, modular platform for integrating multiple information modalities at single synapses. We developed an approach for tandem live-fixed imaging to combine synaptic calcium dynamics or glutamate spiking information with multiprotein measurements. We also developed an integration of rolling circle amplification-based in situ methods, such as a reporter on gene specific translation. We analyzed, based on simulated and experimental data, the application of Bayesian network inference to analyze high-dimensional multimodal synapse distributions to extract biological insight. Finally, we applied this new approach to in-depth investigation of synaptic molecular perturbations associated with autism and schizophrenia genetics and psychiatric drug activity

Sideroflexins enable mitochondrial transport of polar neutral amino acids

Sat, 01 Feb 2025 00:00:00 GMT

Sideroflexins enable mitochondrial transport of polar neutral amino acids Block, Samuel Mitochondria contribute to compartmentalized metabolism in eukaryotic cells, facilitating diverse enzymatic reactions that support cell function. However, this compartmentalization of metabolism necessitates regulated transport of metabolites across the inner mitochondrial membrane. While many proteins enabling mitochondrial membrane transport of metabolites are known, how some metabolites are transported is not known, and several mitochondrial amino acid transporters are largely uncharacterized. The goal of this dissertation is to better understand which proteins in the mitochondrial inner membrane regulate amino acid transport, particularly for substrates that lack known transporters, and how these proteins regulate associated metabolic pathways. Using CRISPR-Cas9-mediated candidate transporter knockouts coupled with assessment of metabolite transport via a mitochondrial swelling assay, we identified SFXN1 as a gene that mediates mitochondrial membrane permeability to polar neutral amino acids, including proline, glycine, taurine, hypotuarine, beta-alanine, and gammaaminobutyric acid (GABA). SFXN2 and SFXN3 partially complemented loss of SFXN1 to enable glycine transport, while SFXN2 and SFXN5 partially complemented loss of SFXN1 to enable GABA transport. Altogether, this work suggests that sideroflexins regulate the delivery of polar neutral amino acids across the inner mitochondrial membrane, many of which lack known carriers, and contributes to a better understanding of how mitochondrial amino acid transport regulates cellular metabolism.

Iterative Engineering, System Confidence, and In-space Servicing Assembly & Manufacturing

Thu, 01 Jun 2023 00:00:00 GMT

Iterative Engineering, System Confidence, and In-space Servicing Assembly & Manufacturing Luu, Michael A. System Confidence is proposed as a method for quantifying the progress and performance of engineering systems. Confidence measures the degree of certainty that a system, design, or process will perform as intended. This metric aggregates existing tools in model-based systems engineering with requirement verification/test events. This is used to mitigate the obstacles of adopting iterative engineering for hardware systems due to the loosely defined requirements set at the beginning of these programs. Iterative engineering has enabled the rapid progress of recent commercial space systems, reduced launch costs through reusable rockets, and deployed large-scale constellations to orbit. This engineering method has been made possible through recent advancements in digital engineering, additive manufacturing, scalable systems, modeling, and simulation software. Iterative engineering challenges the traditional V-model of systems engineering as the default and only choice in designing complex space systems. Rapidly testing novel payloads and satellite capabilities early in the design process can reduce development schedules and deliver capabilities earlier than traditional systems. The advent of In-space Servicing, Assembly, and Manufacturing (ISAM) has ignited new prospects for system architectures, designs, and applications. Until now, iterative engineering has only leveraged in designing robust space systems to be resilient and self-reliant in post-launch deployment/operations. ISAM extends options, flexibility, and engineering decisions beyond the launch phase of space systems. First, System Confidence is defined and quantified, measuring a system's capabilities throughout its development cycle. Second, System Confidence is used to evaluate three existing and historic space programs. Third, ISAM is explored as an extension of iterative engineering for space systems. Lastly, the relationship between iterative engineering outcomes and the utility of ISAM for a space systems architecture is analyzed.

Evolution and engineering of protein-protein interactions

Sat, 01 Feb 2025 00:00:00 GMT

Evolution and engineering of protein-protein interactions Ghose, Ashavari (Dia) Protein-protein interactions are crucial elements in most biological processes. The gain and loss of interactions during evolution have important phenotypic consequences that are subject to selection. Therefore, in a crowded cellular environment, proteins must evolve mechanisms to maintain the correct interactions and avoid inappropriate ones. In this work, I leveraged high-throughput methods for the functional characterization of thousands of protein variants to characterize the sequence spaces associated with paralogous families of interacting proteins. Protein families are formed by gene duplication and divergence, a common source of evolutionary novelty. Family members maintain conserved structural and sequence elements, and yet must often form distinct protein-protein interactions. To probe the extent to which the requirement for interaction specificity constrains evolution, I focused on the twocomponent system family of bacterial signaling proteins. I tested protein variants with all possible single substitutions in the interacting domain of a model protein for their ability to interact with a cognate partner protein and with closely related non-cognate partners. I found that a large fraction of substitutions introduce non-specific interactions, suggesting that paralogs only evolve ‘marginal specificity’ that can easily be disrupted. Bioinformatic evidence indicates that the resulting crowded local sequence space has restricted the evolvability of two-component systems. I also characterized the effects of environmental context constraints, specifically temperature, on the sequence space relevant to two-component system function. This revealed generally conserved sequence-function landscapes across temperatures, with small numbers of variants showing either temperature sensitivity or resistance. Biochemical characterization of these variants challenges existing paradigms relating to the effects of temperature on evolution. Finally, I utilized insights into the evolution of protein-protein interaction specificity to inform the design of protein binders to toxin-antitoxin systems. These binders are selective in their interactions with toxin homologs, and inhibit toxin-antitoxin-mediated bacterial anti-phage defense activity, suggesting their potential use in clinical phage therapy applications. Taken together, these results shed light on the role of protein-protein interactions and their specificity in shaping evolution and suggest the utility of leveraging interaction specificity for engineering purposes.

Market value and financial structure in the railroad industry

Sun, 01 Jan 1961 00:00:00 GMT

Market value and financial structure in the railroad industry Nielsen, Scott. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics and Social Science, 1961; Includes bibliographical references (leaf 117).

Some physical and rheological properties of human blood.

Sat, 01 Jan 1966 00:00:00 GMT

Some physical and rheological properties of human blood. Meiselman, Herbert Joel. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 1966; Bibliography: p. 312-325.

A model for groupoids of homeomorphisms

Fri, 01 Jan 1982 00:00:00 GMT

A model for groupoids of homeomorphisms Greenberg, Peter A. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1982; Bibliography: leaf 84.

Solvent extraction in packed columns

Sat, 01 Jan 1938 00:00:00 GMT

Solvent extraction in packed columns Evans, James E. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 1938; Vita.; Includes bibliographical references (leaf 142).

Tissue-encoded Design Principles of Host Defense

Sun, 01 Sep 2024 00:00:00 GMT

Tissue-encoded Design Principles of Host Defense Misra, Aditya Inflammatory diseases have been rising in incidence over the past few decades and are a result of inappropriate activation of tissue-resident immunity. This inappropriate activation can be derived from any number of cell types, including immunoregulatory functions of non-immune cells such as epithelial cells. In this thesis, we investigated tissue metabolism and inflammation across different temporal and spatial scales using a unique combination of metabolomics, mathematical modeling, metabolic assays, and chemical characterization. Our aim was to identify pathways that protect against inflammation-induced tissue damage and improve clinical outcomes. Thus, we studied A) chronic local tissue inflammation using a colitis model (Chapter 2) and B) acute systemic inflammation using a sepsis model (Chapter 3). In each disease, we studied changes in tissue architecture and the resulting cross-talk among cell types in the microenvironment. In colitis, we found that upon release during tissue damage, IL-18 launches a unique metabolic program in macrophages that 1) exhibits bistable and hysteretic behavior, 2) provides protective memory against inflammatory challenge, and 3) relies on positive feedback with intestinal epithelial cells to maintain the program. In our mouse model of bacterial sepsis, we performed liver tissue metabolomics and found that branched-chain ketoacids (BCKAs), metabolic products of branched-chain amino acids, are released during systemic inflammation and serve as endogenous antioxidants that neutralize extracellular peroxides. They thus reduce tissue damage and increase survival rates by more than double. Through this thesis, we show tissue-intrinsic mechanisms that 1) organize positive feedback loops among cells to establish protective memory against inflammation and 2) secrete endogenous antioxidants to limit pathogenic extracellular oxidants induced by inflammation without quenching bactericidal intracellular oxidants.

Enhancing Muscle Sensing Modalities for Advanced Bionics

Wed, 01 May 2024 00:00:00 GMT

Enhancing Muscle Sensing Modalities for Advanced Bionics Yeon, Seong Ho Muscle sensing technologies have significantly advanced our understanding of biomechanics and enhanced the efficacy of bionic devices. These technologies enable volitional control of prostheses and assistive devices by mapping the electrical and mechanical activities of muscles as control inputs. This dissertation presents novel paradigms and findings to improve the utility and efficacy of muscle sensing modalities for advanced bionic applications. In the first part, I introduce a comprehensive approach to improve the acquisition and processing of surface electromyography (sEMG) signals for bionic applications. This includes innovations in electrode materials and design to enhance user comfort and signal quality for long-term use within prosthetic sockets. Additionally, I propose a real-time impulse filtering algorithm to effectively suppress artifacts while preserving the underlying sEMG signal during dynamic movements. Furthermore, I demonstrate a synchronous sEMG and ultrasound acquisition method that enables simultaneous assessment of muscle electrical activity and mechanical deformation, providing valuable insights into muscle function and control. In the second part, I explore how Magnetomicrometry can serve as a new in-vivo and real-time mechanical muscle state tracking modality. Previous work has shown significant potential for Magnetomicrometry in muscle-state tracking via a tightly-controlled in situ setup. In this work, I demonstrate real-time tracking of muscle tissue length in freely-moving animals performing various motor activities, suggesting that Magnetomicrometry could be extended as a viable in-vivo and real-time muscle sensing modality. In the final part, I propose a novel theoretical framework leveraging Riemannian geometry and manifold theory to enhance the magnet tracking technology stack for Magnetomicrometry. By representing the magnetic dipole state on a manifold and incorporating its dynamics, I develop a more accurate and robust magnet tracking algorithm that addresses the limitations of existing methods. Through simulations and real-world data evaluations, I demonstrate the superior performance of the proposed manifold-based tracking paradigm, showcasing its potential to improve the resolution and extend the observable depth of Magnetomicrometry. The advancements presented in this dissertation have significant implications for the development of next-generation bionic devices, enabling more adaptive, versatile, and reliable myo-neural interfaces. Through this work, I hope to open up new possibilities for the design and control of advanced prostheses and assistive technologies with the advanced myo-neural control interface.

Polyanionizing rocksalt cathodes for lithium-ion batteries

Wed, 01 Feb 2023 00:00:00 GMT

Polyanionizing rocksalt cathodes for lithium-ion batteries Huang, Yimeng Rocksalt-type and olivine polyanion-type cathodes are the two most dominant families of practical lithium-ion battery (LIB) cathodes. Rocksalt-type cathodes, including layered LiCoO₂, [chemical formula] (NCM), spinel LiMn₂O₄ and [chemical formula], and the later-developed disordered rocksalt cathodes (DRX), can have high energy densities up to 1000 Wh kg⁻¹ when utilizing hybrid anion-/cation- redox (HACR) under high upper cutoff voltages > 4.5 V vs. Li/Li⁺. However, anion (oxygen) redox sacrifices cycling stability as oxidized oxide ions [chemical formula] are more mobile than O²⁻, which can lead to percolating lattice oxygen diffusion to the reactive particle surface, oxygen loss, and extensive side reactions with the electrolyte. Meanwhile, polyanion-type cathodes, represented by LiFePO₄, has excellent thermal and structural stability, due to strong covalent bonding in the PO₄ polyanion structure unit that improves structural integrity. But its application is limited by low energy density. While each of them has their own advantages, there was never a marriage between the two materials families for performance-safety synergy. To achieve high energy density with good cycling stability, we hybridize rocksalt and polyanion-type cathodes by introducing a new cathode family of polyanionized disordered rocksalt cathode with spinel order (DRXPS), where an optimal amount of XO₄ (X = P, S, etc.) polyanions are incorporated in Li-M-O (M = Mn, Fe) rocksalt cathodes, free of Co and Ni. We propose design rules to estimate the optimal polyanion amount, x, such that [XO₄]x is sufficient to suppress long-range percolation of lattice oxygen diffusion/loss at high voltages, while not harming capacity (XO₄ content is much less than that in LiFePO₄). The estimated optimal x by design is verified with electrochemical cycling data. Rules for Li/M/O ratio selection are also proposed and verified experimentally. The DRXPS cathode family, represented by [chemical formula], can deliver high initial discharge capacities and energy densities up to 367 mAh g⁻¹ and 1122 Wh kg⁻¹, respectively (among the highest for LIB cathodes to date). But most importantly, they can have > 70% capacity and energy density retention after 100 cycles, far exceeding the cycling performance of un-polyanionized DRX with high energy densities. This addresses the cycling stability issues that are the bottleneck for DRX development. The DRXPS cathodes also demonstrate good rate performance and large compositional tunability (similar performance for compositions with varying M, X elements and u, v, x selection). In addition, we clarify their crystal structure, morphology, and redox mechanism via systematic characterizations. We believe that polyanionization should be a general strategy to address the poor high-voltage cyclability issue, which is a key challenge for earth-abundant cathodes. The superior performance demonstrated and the design rules proposed for DRXPS shed light on the future development of advanced sustainable cathodes.

A class of high-efficiency air-core power transformers with flux-guiding resonators

Wed, 01 May 2024 00:00:00 GMT

A class of high-efficiency air-core power transformers with flux-guiding resonators Salk, Noah J. Developments in high frequency power semiconductors have enabled the miniaturization of power system components, leading to the reduction of heavy, lossy magnetic steel cores as a media for electromagnetic energy transfer. A final push towards fully “air-core” power devices is underway and a new class of coreless transformers is under development at MIT which targets the cost-sensitive application of grid-tied renewable energy farms. The topology is composed of a primary coil, a secondary coil, and one or more nested resonant tanks that facilitate efficient multi-path energy transfer. This class of transformers presents opportunities for upfront cost savings via material reduction, and long-term cost savings via efficiency gains and the resulting reduction of lost profit. This work will examine the theory, modeling efforts, system-level considerations, and rigorous experimental validation necessary to compare the performance of these transformers with other topologies and establish industrial viability.

The Acoustic Expander: A New Expansion Machine for Use in Cryogenic Refrigeration

Wed, 01 May 2024 00:00:00 GMT

The Acoustic Expander: A New Expansion Machine for Use in Cryogenic Refrigeration Adams, Jacob The acoustic expander is a new expansion machine with potential applications to cryogenic refrigeration and liquefaction. Cryogenic expansion machines produce mechanical energy from the expansion of a working fluid from high-pressure to low-pressure thereby cooling the low-pressure fluid for use in refrigeration. The novelty of the acoustic expander is that the mechanical energy is transferred through a gaseous acoustic wave, as opposed to a traditional piston- or turbo-expander where the mechanical energy is transferred through a solid piston or a spinning shaft. The acoustic expander is comprised of passive reed-valves that are coupled to a resonant cavity, much like a wind instrument. The working fluid enters and exits the resonant cavity through the oscillating reed-valves which drives a standing acoustic wave in the resonant cavity. The acoustic wave carries mechanical energy from the low-temperature region to high-temperature region where the energy is then dissipated as heat to the ambient environment; this transfer of energy cools the low-pressure fluid as it exits the acoustic expander. The practical advantage of the acoustic expander over piston- or turbo-expanders is the absence of dynamic sliding seals or complex moving parts at cryogenic temperatures. This dissertation presents a first principles thermodynamic model of the acoustic expander and describes the behavior of the coupled reed-resonator system. The model reveals that flow blow-by through the reed-valves at large pressure differentials is a primary loss mechanism. Several proof-of-concept prototypes were constructed, with both a single reed-valve and a double reed-valve, demonstrating isentropic expansion efficiencies between 40% to 50% at pressure ratios up to 2.5. These experiments incorporated the acoustic expander with a recuperative heat exchanger, reaching temperatures of -62 C (211 K) with vacuum insulation and air working fluid. The cooling power of these prototypes is between 50 to 150 Watts at room temperature with a mass flow rate of 1 to 3 g/s. Instabilities that cause the acoustic expander to shut-off and reed-valve fatigue are identified as key challenges. Future work may address these challenges and integrate the acoustic expander into cryogenic cooling systems for the refrigeration of superconducting magnets or quantum computers.

Machine Learning through the Lens of Data

Sun, 01 Sep 2024 00:00:00 GMT

Machine Learning through the Lens of Data Park, Sung Min Many critical challenges in machine learning—e.g., debugging model behavior or selecting good training data—require us to relate outputs of models back to the training data. The goal of predictive data attribution, the focus of this thesis, is to precisely characterize the resulting model behavior as a function of the training data in order to tackle these challenges. In the first part of this thesis, we introduce a framework, datamodeling, for formalizing and constructing effective methods for predictive data attribution. Despite the complexity of modern machine learning systems (e.g., end-to-end training of deep neural networks using stochastic gradient algorithms), we show that we can accurately predict model outputs from simple linear functions of the training data. We then demonstrate that these predictors—which we call datamodels—provide a versatile primitive for various tasks, ranging from predicting the effect of dataset counterfactuals to identifying brittle predictions. Next, to further improve the scalability of data attribution in this framework, we design a new method trak (Tracing with the Randomly-projected After Kernel) that is both effective and computationally tractable for large-scale, differentiable models. By leveraging a kernel approximation and other classic ideas from statistics and algorithm design, we are able to reduce the challenging problem of attributing the original DNN to that of attributing a simpler surrogate. We demonstrate the effectiveness of trak across various modalities and scales: image classifiers trained on ImageNet, vision-language models (CLIP), language models (BERT and mT5), and diffusion models. In the second part of this thesis, we explore applications of this framework developed in the first part: First, we leverage datamodels for the problem of learning algorithm comparison, where the goal is to detect differences between models trained with two different learning algorithms. Our algorithm, ModelDiff, enables us to automatically surface biases that distinguish different learning algorithms by differentiating how they use the same training data. Lastly, we tackle the challenging problem of machine unlearning, wherein the goal is to “unlearn” a small fraction of training data from a trained model. By leveraging the fact that datamodels can accurately approximate the “oracle” predictions, we design a simple finetuning algorithm that allows us to unlearn at a significantly smaller cost than prior methods.

A study of technological diffusion : the replacement of steam by diesel locomotives in the United States.

Sat, 01 Jan 1977 00:00:00 GMT

A study of technological diffusion : the replacement of steam by diesel locomotives in the United States. Hydell, Richard Paul. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1977; Vita.; Bibliography : leaves 306-308.

Detection of products of molecular beam reactions by laser-induced fluorescence.

Thu, 01 Jan 1976 00:00:00 GMT

Detection of products of molecular beam reactions by laser-induced fluorescence. Silver, Joel Art. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1976; Vita.; Includes bibliographical references.

On the existence of certain entire functions of zero type

Fri, 01 Jan 1943 00:00:00 GMT

On the existence of certain entire functions of zero type Scanlan, Robert H. (Robert Harris), 1914- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1943; Includes bibliographical references.

The semi-simplicial free lie ring

Wed, 01 Jan 1964 00:00:00 GMT

The semi-simplicial free lie ring Schlesinger, James W. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1964; Vita.; Includes bibliographical references (leaf 29).

Olefins from amine oxides

Tue, 01 Jan 1957 00:00:00 GMT

Olefins from amine oxides Lebel, Norman A. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1957; Vita.

Communication in the presence of noise

Wed, 01 Jan 1992 00:00:00 GMT

Communication in the presence of noise Schulman, Leonard J. Y. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1992; Includes bibliographical references (leaves 58-61).

High energy scattering of helium by the molecular hydrogen isotopes

Sun, 01 Jan 1967 00:00:00 GMT

High energy scattering of helium by the molecular hydrogen isotopes Fowler, Michael Coolidge, 1941- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1967; Vita.; Bibliography: leaves [168]-[171].

The political economy of trade : a computer simulation of contending regimes and the international division of labor

Thu, 01 Jan 1981 00:00:00 GMT

The political economy of trade : a computer simulation of contending regimes and the international division of labor Pollins, Brian. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Political Science, 1981; Bibliography: leaves 319-330.

Representation of the spatial organization of three-dimensional shapes for visual recognition.

Sun, 01 Jan 1978 00:00:00 GMT

Representation of the spatial organization of three-dimensional shapes for visual recognition. Nishihara, H. K. (Herbert Keith) Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1978; Bibliography: p. 179-181.

Application of pressure broadening theory to atmospheric microwave absorption.

Thu, 01 Jan 1976 00:00:00 GMT

Application of pressure broadening theory to atmospheric microwave absorption. Lam, Kai S. (Kai Shue), 1949- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1976; Vita.; Bibliography: leaves 415-419.

Engineering Mechanocaloric Effects and Tunable Thermal Conductivity in Amorphous Elastic Polymer Fibers

Thu, 01 Feb 2024 00:00:00 GMT

Engineering Mechanocaloric Effects and Tunable Thermal Conductivity in Amorphous Elastic Polymer Fibers Li, Buxuan Energy-efficient clean technologies for active heating/cooling and passive thermal regulation are in high demand for applications spanning different scales, from city planning and building heating/cooling to wearable and portable devices to miniature electronics. To advance relevant technologies and simultaneously lower their environmental footprint, two key research and engineering questions await to be answered: (1) how to pump/convert energy between thermal and other forms more efficiently, and (2) how to transport thermal energy in a tunable and scalable way for dissipation/insulation applications. Properly-engineered polymer materials may provide solutions to both challenges in a synergistic way. Among all materials, polymers stand out by several figures of merit, including low cost, chemical inertness, ease of manufacture and scalability, and light weight. They can be engineered by the application of temperature and/or strain, which impose different molecular arrangements within the material, enabling control over the degree of crystallinity, chain entanglement, and the dominant chain orientation. When polymers undergo microscopic structural changes, they may exhibit temperature responses driven by their internal entropy changes, known as mechanocaloric (mC) effects. mC effects offer a venue of conversion between mechanical and thermal forms of energy. Polymer chain alignment, on the other hand, also has a strong effect on the vibration characteristics of polymers, and thus on their thermal conductivity (TC) values. Through a continuous strain-temperature engineering of elastic amorphous polymer fibers, we demonstrate unique opportunities to address both challenges in energy conversion and transfer. We developed elastic fibers, which are melt spun from an olefin block copolymer (OBC), and exhibit (1) competitive mC performance with the temperature change exceeding 5K and the material coefficient of performance (COP) larger than 10, and (2) reversible thermal conductivity, which is continuously tunable in the range from 1.2 to 2.5 W/mK via uniaxial strain deformations. The entanglement-enabled elasticity of the cross-linker-free block co-polymer chosen for this research allows the fibers to survive thousands of loading-release stretching cycles. In striking contrast with the vulcanized rubber commonly used as an efficient mC material, the OBC is a thermoplastic with a relatively low melting temperature (<120C), which can be easily recycled and molded into different geometries. By optimizing both the fabrication parameters and the operational scenarios, we demonstrated high potential of elastic OBC fibers in advanced thermal applications within a wide temperature window from -20C to 70C. We further analyzed structural changes, thermodynamics, and vibration spectra of OBC fibers under different strains and temperatures, elucidating the mechanisms underlying the observed phenomena. This study provides insights into sustainable engineering and optimization of polymer-based solid-state refrigerators, heat pumps and tunable materials for efficient energy dissipation and passive thermoregulation.

Characterizing Language Representations in the Human Mind and Brain

Sat, 01 Feb 2025 00:00:00 GMT

Characterizing Language Representations in the Human Mind and Brain Tuckute, Greta Language allows for the mapping of speech signals or written characters to meaning every time we engage in conversation or read. How can biological tissue, our brains, support this mapping process? This thesis characterizes the neural representations that enable humans to infer the meaning of a sentence. My work builds on the foundation that regions in the left frontal and temporal parts of the brain causally and selectively support language processing (the ‘language network’). Chapter 2 asks how the language network develops. Through a case study of an individual born without their left temporal lobe (but with neurotypical language abilities), I demonstrate that the presence of temporal language regions appears to be necessary for the development of ipsilateral frontal regions, which echoes evidence from aphasia that the temporal areas are more important for language function. Chapters 3-5 aim to understand the representations and computations that mediate language comprehension. Traditionally, this line of inquiry has been challenging given the limited utility of probing animal models whose communication systems differ substantially from human language. However, the recent advent of artificial language models (LMs) has demonstrated that a system other than the human brain is capable of generating fluent and coherent text. Chapter 3 introduces the use of LMs as model systems for studying neural representations of language. I ask what aspects of an LM’s representation of the linguistic input matter the most for model-to-brain similarity. Across a series of systematic comparisons, I show that meanings of content words, such as nouns and verbs, matter more than syntactic structure (e.g., word order and function words). In Chapter 4, I leverage this model-to-brain similarity to ask what kinds of linguistic input the human language regions are most responsive to. I use an LM to identify sentences that maximally drive or suppress activity in language regions, and I demonstrate that these regions respond most strongly to sentences that are sufficiently linguistically well-formed but unpredictable in their structure or meaning, suggesting that this network is tuned to input predictability in the service of efficient meaning extraction. Finally, in Chapter 5, I use high-field (7T) fMRI to search for the organizing dimensions of the language network. By performing a data-driven decomposition of neural responses to linguistically diverse sentences, I show that only two components—shared across individuals—emerged robustly, accounting for about 34% of the explainable variance. In line with work in Chapter 4, the first component appears to correspond to processing difficulty. The second component appears to correspond to meaning abstractness. Both components are distributed across frontal and temporal brain areas but show systematic topographies across participants. Altogether, this thesis provides a detailed characterization—across thousands of sentences and through spatially-precise neural measurements—of how the fronto-temporal language network supports language comprehension. This work brings us closer to deciphering the circuits and mechanisms that underlie the astonishing human capacity to infer complex meanings through language.

Spontaneous activity in the mouse visual cortical slice: biophysical characterization and pathophysiology

Sat, 01 Feb 2025 00:00:00 GMT

Spontaneous activity in the mouse visual cortical slice: biophysical characterization and pathophysiology Heinrich, Maxwell John As we continue to await the first disease-modifying treatment for Fragile X Syndrome (FXS), the leading inherited cause of intellectual disability, the search continues for novel ways to address the core pathophysiology of this neurodevelopmental disorder. FXS is caused by silencing of the FMR1 gene, which results in the loss of fragile X messenger ribonucleoprotein (FMRP), a critical protein in regulating nervous system development and neural circuit function. Due to the loss of FMRP’s canonical role in inhibiting mRNA translation, an elevated rate of protein synthesis is widely recognized as a core feature of FXS pathophysiology. In this thesis, I present my investigation of a relatively understudied form of pathophysiology that arises in brain slices prepared from a mouse model of FXS, the Fmr1-knockout (KO) mouse. Relative to wildtype (WT) slices, Fmr1-KO visual cortical slices exhibit increased spiking activity in layer 5. Critically, this hyperactivity phenotype is rapidly reversed not only by treatments known to restore elevated rates of protein synthesis to WT levels, but also by the protein synthesis inhibitor cycloheximide. Therefore, rapidly turned over pathogenic proteins are suspected to actively maintain this form of pathophysiology. Identifying these pathogenic proteins could reveal novel therapeutic targets for the treatment of FXS. Progress requires a deeper understanding not only of the cellular pathophysiology supporting this hyperactivity, but also of the biophysical mechanisms driving the activity itself, as each remains relatively unexplored. In Chapter 1, I review relevant FXS pathophysiology and our understanding of the various forms of spontaneous activity generation in neocortical brain slices. In Chapter 2, I dive into the biophysical mechanisms underlying the sparse, spontaneous spiking activity generated in WT visual cortical slices. Here, I find extreme sensitivity to the ionic composition of the artificial cerebral spinal fluid (aCSF) bathing the slices. Lower, more physiologic concentrations of extracellular divalent cations render extratelencephalic layer 5 pyramidal neurons intrinsically active by altering the activity of the persistent sodium current. In Chapter 3, I detail my journey investigating the pathophysiology underlying the hyperactivity phenotype in Fmr1-KO mice. While my early investigations indicated that depolarized intratelencephalic layer 5 pyramidal neurons drive hyperactivity of the layer 5 circuit, this intracellular phenotype proved to be ephemeral and likely due to suboptimal slice conditions. Informed by the investigations of Chapter 2, I conclude that the hyperactivity phenotype is not driven by cell-intrinsic hyperexcitability of layer 5 pyramidal neurons. My optimization of slice conditions preserves the hyperactivity phenotype, setting the stage for future intracellular investigation of the cause of this pathophysiology. In Chapter 4, I describe the implications of my work for understanding activity generation in neocortex and provide direction for future studies of spontaneous activity in WT and Fmr1-KO visual cortical slices.

Models and Tools for Studying Infants’ Attention

Sat, 01 Feb 2025 00:00:00 GMT

Models and Tools for Studying Infants’ Attention Raz, Gal From birth, infants actively control where they look, long before they gain any significant motor control over other body parts. This early emergence of attentional preferences has allowed psychologists to use infants' gaze to gain insight into the developmental origins of perception and cognition. Understanding infant gaze therefore is critical both for understanding early development, and for interpreting decades of literature in developmental psychology. This thesis studies the functions of infants' looking behavior, and introduces novel tools to accelerate its study. Chapter 1 is a theoretical review which challenges the notion that learning in infancy is primarily incidental and passive. I outline ways in which infants use their gaze to learn, as well as form and manage social relationships. Chapter 2 demonstrates that, indeed, infants' looking behavior is better understood as an active sampling process. I describe a computational model that posits that infants' gaze is optimized to maximize expected information gain from noisy perceptual input, and show through large-scale behavioral experiments that infant looking is well described by this model. Chapter 3 then confronts the methodological challenges of studying infant gaze empirically: to obtain and process data from a single infant in an infant looking time experiment takes about 2 hours per infant. I describe a workflow in which we reduce this time to about 5 minutes per infants by a) using asynchronous, instead of in-lab, testing, b) training parents, rather than experimenters, to control the flow of experiments, and c) replacing manual gaze coding with automatic annotation using modern computer vision tools. Finally, synthesizing the preceding chapters, Chapter 4 describes outstanding challenges for the empirical and computational study of infant attention.

Neural correlates of trait mindfulness

Sat, 01 Feb 2025 00:00:00 GMT

Neural correlates of trait mindfulness Treves, Isaac N. There is a clear and present need to identify the brain biological bases of mental health and mental illness. In this thesis, I focus on the brain bases of trait mindfulness, measured using self-report. Mindful individuals pay attention to the present moment and bring an attitude of acceptance and non-judgement to their thoughts and feelings. Despite the well-established importance of trait mindfulness to well-being, there are no well-established brain measures of trait mindfulness. This may be because of methodological obstacles to brain-behavior association studies. In this thesis, I evaluated the significance of these obstacles in the field and addressed them empirically. In Chapter 2, I conducted a systematic review of 68 brain imaging studies of trait mindfulness. There were some commonalities, but also large gaps in the literature. Sample sizes were small, and studies focused on single regions, networks or EEG responses. There was a lack of research on self-awareness and body awareness, important components of mindfulness. In the following chapters, I conducted three fMRI studies using large existing datasets and rigorous methodology to elucidate brain-mindfulness associations. In Chapter 3, I conducted connectome predictive modelling with the largest sample of any lab-based neuroimaging study of mindfulness (n = 367 adults). I found whole-brain network models of attention and non-judgement components of mindfulness that generalized to one of two held-out datasets. The models incorporated default-mode, somatomotor, and visual networks. Overall mindfulness scores were not predictable, suggesting challenges to a single brain marker of mindfulness. In Chapter 4, I analyzed a dataset of resting-state fMRI in adolescents, conducting dynamic connectivity analyses to find time-varying brain states. I selected brain states that showed good test-retest reliability, and these brain states correlated with mindfulness. Interestingly, one brain state exhibited global hyperconnectivity, perhaps a marker of arousal or awareness. Finally, in Chapter 5, I questioned whether using a task involving breath-counting would bolster correlations with trait mindfulness. I found differential brain responses to the task vs resting-state, but the responses did not correlate with trait mindfulness. Together, these studies contribute to an emerging picture of the mindful brain as being reflected in both unimodal (e.g. VIS) and heteromodal (e.g. DMN) brain networks, as well as static and dynamic functional organization. However, results underscore the difficulty of finding generalizable correlates across samples, conditions, and mindfulness scales.

Postnatal specialization of astrocyte regional heterogeneity in the mammalian brain and improved tools for studying glia

Sat, 01 Feb 2025 00:00:00 GMT

Postnatal specialization of astrocyte regional heterogeneity in the mammalian brain and improved tools for studying glia Schroeder, Margaret E. Astrocytes are an abundant class of glial cells with critical roles in neural circuit assembly and function. Though many studies have uncovered significant molecular distinctions between astrocytes from different brain regions, the developmental trajectory of this regional heterogeneity requires further systematic study. Chapter 1 of this thesis provides a detailed literature review on the development of astrocyte regional heterogeneity. To address existing knowledge gaps, we used single-nucleus RNA sequencing to characterize the molecular diversity of brain cells across six developmental stages and four brain regions in the mouse and marmoset brain (Chapter 2). Using this transcriptomic atlas, we show that astrocyte regional specialization is shaped by postnatal development in both species, with significant species divergence in astrocyte gene expression signatures (Chapter 3). In Chapter 4, we report multiplexed expansion revealing (multiExR), a technique that can be used to visualize 20 or more proteins at nanoscale resolution in the same tissue sample. Finally, in Chapter 5, we describe the generation and characterization of Gpr17-Cre, a novel Cre recombinase driver line sensitive and specific for the oligodendrocyte lineage and a subset of astrocytes in the central nervous system.

Automated and Provable Privatization for Black-Box Processing

Sun, 01 Sep 2024 00:00:00 GMT

Automated and Provable Privatization for Black-Box Processing Xiao, Hanshen This thesis initiates a study on universal leakage quantification and automated privacy-preserving solutions. To minimize assumptions on leakage generation and symbiotically accommodate cutting-edge advances in both algorithms and their implementations, a framework is established that models leakage as the output of a black-box processing function and produces rigorous privacy analysis based entirely on end-to-end simulation. At a high level, we demonstrate the following results: Given access to the underlying black-box secret generation, through mechanized evaluations of the black-box processing function, the hardness of adversarial inference can be provably quantified and controlled through properly selected perturbations. The detailed contributions can be summarized from three perspectives: a). Privacy Definition: We propose a new and semantic notion, called ProbablyApproximately-Correct (PAC) Privacy. This concept describes privacy intuitively as an impossible inference task for a computationally-unbounded adversary and supports expression of a universal privacy concern that is accessible to a general audience. b). Black-Box Leakage Quantification: We introduce randomization optimization and noise smoothing tricks and develop a set of information-theoretical tools based on f-divergence to characterize privacy risk through a statistical mean estimation. Provided sufficient sampling, one can approach this objective risk bound arbitrarily closely, which thus leads to a high confidence proof. The established theory also connects algorithmic stability and generalization error, demonstrating win-win situations in machine learning that simultaneously improve PAC Privacy and learning performance. c). Automated Privacy-Preserving Solutions: Theoretically, we characterize the tradeoff between required privacy guarantees (privacy budget), approximation error of the optimal perturbation strategy (utility loss), and simulation budget (computation power) to automatically construct a perturbation-based privacy solution from black-box evaluations. Operationally, we establish a series of tools to efficiently optimize the noise distribution in high-dimensional or constrained support spaces, and study their online versions with adversarially-adaptive composition. Concrete applications are presented, ranging from formal privacy proof for heuristic obfuscations, to privacy-preserving statistical learning, to response privacy in deep learning with vision models and large language models (LLM), such as ResNet and GPT-2, and hardware security, such as side-channel cache-timing leakage control.

The mechanism of hydrolysis of triphenylsilyl fluorides in water-acetone solutions and the mechanism of decarboxylation of β-ketoacids in water, benzene, and hexane

Mon, 01 Jan 1951 00:00:00 GMT

The mechanism of hydrolysis of triphenylsilyl fluorides in water-acetone solutions and the mechanism of decarboxylation of β-ketoacids in water, benzene, and hexane Esteve Campderá, Ramón María. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1951; Vita.

Capillary phenomena in cohesionless soils

Thu, 01 Jan 1948 00:00:00 GMT

Capillary phenomena in cohesionless soils Lambe, T. William. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Civil and Sanitary Engineering, 1948; Includes bibliographical references (leaves 186-188).

Modelling, off-design performance and control analysis of OTEC power plants

Thu, 01 Jan 1981 00:00:00 GMT

Modelling, off-design performance and control analysis of OTEC power plants Calvo Sotelo, José, 1893-1936. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Ocean Engineering, 1981; Includes bibliographical references.

Systems Analysis of Plant Responses to Drought

Sat, 01 Feb 2025 00:00:00 GMT

Systems Analysis of Plant Responses to Drought Yun, Jie Understanding how plants respond to environmental stress is critical for ensuring stable crop performance and predicting how natural populations may adapt to a changing climate. While plant biology has traditionally focused on plant physiology and molecular biology of model plants to elucidate plant responses, there is immense diversity in how plants respond to environmental conditions, arising from complex genotype-by-environment interactions (GxE). This dissertation investigates these themes, aiming to advance our understanding of the mechanisms driving plant responses to environmental stress and providing insights for improving agricultural resilience and sustainability, as well as contributing to evolutionary biology. This thesis focuses on three projects: (1) While GxE is widely observed in traits and gene expression patterns, the mechanisms driving these interactions remain unclear. This thesis will present a framework using casual inference to study GxE interactions in gene regulatory networks to uncover the molecular mechanisms driving diverse environmental responses. We study two genotypes of the model grass species Brachypodium distachyon, leveraging natural variation and RNA-sequencing to study their responses to drought stress. (2) Natural perturbations can be used to understand complex traits. In wild species, limited resources drive allocation strategies that balance trade-offs between survival risks and fitness benefits, which is central to their ecology. This thesis particularly focuses on understanding a whole plant trait – carbon allocation – using divergent responses of annual and perennial species of Brachypodium to drought stress. (3) Does domestication trade-off stress tolerance for rapid growth? Plant domestication is thought to create trade-offs between high yield and stress tolerance, raising concerns about yield stability in future climates. This thesis will present a high-throughput phenotyping approach to study this question, focusing on leaf growth environmental response and its cellular regulatory mechanisms.

Evaluating the Effects of Pharmaceutical Interventions, Social Policies, and Exogeneous Shocks on People's Health and Behavior

Sat, 01 Feb 2025 00:00:00 GMT

Evaluating the Effects of Pharmaceutical Interventions, Social Policies, and Exogeneous Shocks on People's Health and Behavior Charpignon, Marie-Laure Aging individuals tend to suffer from chronic conditions, some of which manifest in midlife (e.g., type 2 diabetes and hypertension) and some later (e.g., neurodegenerative disorders). As the global population increases and as people are living longer, finding strategies to prevent or delay these diseases has become a key priority. Concurrent advances in public health and biomedicine offer an array of pharmaceutical (e.g., oral drugs, vaccines) and non-pharmaceutical solutions (e.g., preventative and behavioral health measures). Meanwhile, exogenous shocks such as pandemics also affect the health and well-being of aging and other vulnerable individuals or populations (e.g., immunocompromised individuals, multigenerational households). In such circumstances, pharmaceutical interventions may not be readily available, forcing governments to implement socio-behavioral policies such as lockdowns and mask-wearing mandates and companies to adopt remote and hybrid work practices. Natural experiments, such as the social isolation induced by the COVID-19 pandemic or incentive-based vaccine distribution programs aimed to bolster vaccine uptake during this time, provide an opportunity to assess retrospectively the effect of federal, state, or local government policies. Another example consists of leveraging new drug approvals and changes in clinical guidelines to learn from electronic health records (EHR) which existing treatments could be repurposed to delay neurodegeneration and/or increase longevity, and if so, for whom they would work best. However, unlike randomized controlled trials, natural experiments suffer from multiple sources of confounding. The use of appropriate causal inference methods can help mitigate confounding bias, including via weighting and regression discontinuity designs. This thesis illustrates the use of existing causal inference approaches in population health and proposes new methods to evaluate the effects of pharmaceutical interventions (Chapters 1 and 2), exogenous shocks (Chapters 3, 4, and 5), and socio-behavioral policies (Chapters 3 and 5) on the health and well-being of aging and other vulnerable individuals or populations. Specifically, Chapters 1 and 2 leverage the target trial emulation framework to study the comparative effectiveness of antidiabetic and antihypertensive drugs towards preventing dementia or delaying its onset, using EHR data from Mass General Brigham healthcare system. Our target trial emulations suggest the diabetes drug metformin and the antihypertensive drug class of angiotensin receptor blockers as potential repurposing candidates for dementia, especially if initiated before age 70. Chapter 3 uses regression discontinuity designs to quantify the benefits of a local vaccine companion program in Massachusetts during the COVID-19 pandemic. We estimate that this initiative may have bolstered vaccine uptake among older adults aged 75+ by up to 22 percentage points. Chapter 4 implements counterfactual time series modeling to estimate pandemic-period excess mortality associated with overdoses in the US, by substance and geography. We find ∼25,650 excess deaths nationally (March 2020-August 2021), disproportionately affecting Southern and Western regions of the country and attributable mainly to synthetic opioids, methamphetamines, and alcohol as well as polysubstance use. Chapter 5 characterizes changes in team coordination among knowledge workers at a large global tech company to better understand the rise of hybrid work practices and their potential implications for well-being. Using two-way fixed effect regression models, we find evidence of voluntary alignment of work schedules with managers and greater co-attendance among employees who were recently hired or work in shared office spaces. Collectively, these five studies demonstrate how we can effectively learn from data about past events, medical records, and office attendance logs, to provide insights that inform the design of future public health strategies.

Data futures: Transforming digital traces into public goods in the age of commercial surveillance

Sat, 01 Feb 2025 00:00:00 GMT

Data futures: Transforming digital traces into public goods in the age of commercial surveillance Berke, Alex For decades, government agencies have collected surveys to produce datasets and statistics that serve as public goods, enabling research and empowering communities from whom data are collected. These data sources are costly to collect and are in decline as survey response rates drop. In contrast, increasing quantities of data are collected from the public by companies -- data we unavoidably generate by making purchases, using the Internet, or simply operating a mobile phone. This data collection might be considered a form of surveying the public, but where privatized datasets empower corporations rather than communities, and the ensuing potential harms cannot be empirically assessed without access to these data. This thesis considers a future where corporations can more accurately track populations and estimate statistics than the government agencies traditionally tasked with such efforts. This thesis illustrates how this future may be nearby and explores resulting questions through case studies. Namely, are there more privacy-preserving or equitable or cooperative ways to manage these data, to benefit the public from whom they are sourced? The first set of case studies use location data from mobile phones, first developing a more privacy-preserving approach by leveraging recurrent neural networks to generate realistic synthetic data, and second developing aggregated mobility metrics to improve country level population estimates and COVID-19 epidemic models. The next set of case studies use web browser data to evaluate risks of cross-site user tracking that are present despite privacy-enhancing browser developments. The first web study repurposes data collected by a data broker; the second uses a dataset we crowdsourced and openly published to benefit this research and future research. For the next set of case studies, we crowdsourced and published a first-of-its-kind open dataset of purchase histories from thousands of Amazon.com users, along with their sociodemographics. We use this dataset to demonstrate how corporate data can provide insights into societal changes and also evaluate privacy risks due to inferring sensitive consumer information from purchases. The data used in this thesis (mobile device locations, web browsing data, purchase histories) are examples of digital traces collected continuously from people throughout everyday activities, without explicit consent. This work points towards cooperative data sharing as a paradigm to empower research that benefits the public while prioritizing consent. Could such a paradigm exist with public support and participation? In order to study this and inform future crowdsourcing efforts, we embedded behavior experiments and surveys into our crowdsourcing tools, shedding light on what impacts users' likelihood to share their data, how users believe their data should be used, and how results differ across demographics. Throughout these studies, this thesis asks a broader question: Can we envision, and build towards, a future with alternative data economies that shift the power dynamics of data collection, along with the control and benefits of these data? To begin to address this question, this thesis proposes speculative, privacy-enhancing, and cooperative commerce networks. Such system changes may incur new costs for consumers. The final case study measures consumers' willingness to pay for privacy in new package delivery networks.

Generative Discovery via Reinforcement Learning

Sat, 01 Feb 2025 00:00:00 GMT

Generative Discovery via Reinforcement Learning Hong, Zhang-Wei Discovering new knowledge is crucial for technological advancement and mirrors how humans and animals learn new skills—often through trial and error. Ancient humans, for example, discovered fire by experimenting with different methods, and children learned to walk and use tools through repeated attempts and failures. In chemistry, scientists find new catalysts by testing various compositions. But how exactly do humans use trial-and-error to improve existing solutions (like learning more efficient ways to walk or synthesizing novel compounds)? Can we design computational models that mimic or exceed human discovery? Such computational models could greatly accelerate progress in science and engineering since they can automate or assist human scientists’ and engineers’ works and discover new knowledge more efficiently (e.g., new compounds, streamlining the robot controller design, etc.). Reinforcement learning (RL) is well-suited for discovery tasks because it enables machines to learn through trial and error. My work overcomes the following major limitation of today’s RL algorithms and thereby advances their discovery potential: Mitigate the bias of reward shaping. RL relies on reward signals from trial-anderror experience, but these signals can be sparse, meaning they are only provided once a desired solution is found and otherwise zero. Most trials, therefore, offer little to no feedback. A common strategy to improve performance under sparse rewards is to provide additional hints (i.e., reward shaping) to guide RL algorithms. However, if these hints are inaccurate, they can steer the algorithm toward worse solutions than those without them. I propose a new RL framework that can be combined with any standard RL algorithm, ensuring that training with hints finds better solutions instead of harming performance. Learning with sub-optimal data. RL can learn not only from online interaction with the world but also from datasets of logged experiences. For expensive or time-consuming tasks like material discovery or robot learning, offline RL could be preferred because it leverages existing data rather than requires new interaction with the world. However, such datasets could contain mostly low-reward solutions, which limits the offline RL algorithm’s performance in finding solutions better than what’s in the dataset (as we show later in this thesis). I introduce sample reweighting strategies that reweight the dataset in a way that current offline RL algorithms trained with the weighted samples are able to discover solutions far better than what’s in the dataset, even if low-reward solutions predominated the dataset. Safety via Diversity. Standard RL algorithms aim to find a single “best” solution. Yet, in many discovery problems—such as drug development—it is more valuable to generate multiple high-rewards solutions with distinct properties (i.e., diversity) than to focus on only one. I study this problem in an emerging discovery task-red-teaming large language models (LLMs). In red-teaming, we desire diverse prompts that trigger undesired outputs from target language models. Current approaches leveraging RL to train an LLM to red-team another one, but they fall short of the diversity of generated prompts and often converge to a few prompts that consistently trigger undesired outputs. I propose to reward the agent to maximize the diversity of generated prompts, which also improves the the success of prompts at triggering undesired outputs from the target LLM.

Bridging the Gap: Generative Machines and Inventive Minds

Sat, 01 Feb 2025 00:00:00 GMT

Bridging the Gap: Generative Machines and Inventive Minds Singh, Nikhil Recording technologies, from the phonograph to digital media, have profoundly reshaped the human experience by enabling the capture and reproduction of our sensory world. These technologies allow us to relive experiences through artifacts of remarkable fidelity like photographs and videos, extending the reach of our perception and memory. Of course, we didn’t stop at the phonograph; we have built a rich ecosystem of tools for creating, sharing, and exploring recorded media that have had transformative effects on cognition and culture. Recently, a new and powerful class of tools has emerged: generative models. Unlike recorded media, which reproduces external experiences, generative models can translate our ideas directly into artifacts. Here, ideas refer to abstract mental constructs that seed media creation, externally expressed in text prompts, sketches, vocalizations, or other intuitive representations. Just as recorded media augmented our ability to perceive and remember, generative media promises to expand our ability to imagine and invent by offering a more immediate path from cognition to high fidelity creation. Creative work often has us operating at our limits, negotiating boundaries between knowledge and novelty, skill and aspiration, from individual exploration to collective understanding. Generative models, in principle, have the potential to scaffold and accelerate how we transcend these limits by increasing the efficiency with which we discover and pursue new ideas. In this thesis, I suggest that realizing this potential presents a complex set of challenges that span computation and design. I argue that it requires us to develop a rich stack of precision tools for human-AI co-creation, as we have done and continue to do for recorded media. Specifically, I present contributions across two key dimensions of this: 1. Computational machinery that supports creative work. I present research on topics including visually-driven acoustic simulation, interpretable and controllable sound generation from descriptions, and audiovisual content understanding. Focusing on sound as a case study, I describe systems that effectively represent and manipulate creative knowledge across modalities and levels of abstraction. 2. Interactive systems and studies that investigate the integration of human and machine effort in content creation. This includes work on conceptual integration in AI-assisted story writing, author-in-the-loop description authoring for accessibility of complex scientific figures, and generative constraints for human ideation. In all, this work seeks insights for designing systems that support human creators through exploration, collaboration, and feedback, rather than aiming to replace or constrain human agency and expertise. To conclude this thesis, I present a discussion on bridging AI and HCI to gain insights into human creative work and develop stable, generalizable design knowledge for augmenting it. I argue for the design of flexible, parametric tools that enable systematic study of creative behavior under different augmentation designs. Based on this, I propose a conceptual framework to seed the development of a more robust science of human-AI co-creation.

Causal Inference Under Privacy Constraints

Sat, 01 Feb 2025 00:00:00 GMT

Causal Inference Under Privacy Constraints Yao, Leon Causal inference is an important tool for learning the effects of interventions in observational or experimental settings. It is widely used in many fields such as epidemiology, economics, and political science to find answers like the average treatment effect of a medical procedure or the individual treatment effect of a personalized ad campaign. In commercial applications, the era of big data allows companies to increase their experiment volume, incentivizing them, in turn, to collect more user data. On one hand, large volumes of data are necessary to train generative models like ChatGPT. At the same time, companies’ increasing use of user data has drawn heavy criticism and consumer backlash, incurring legitimate concerns about privacy and consent. As concerns over user data safety and privacy grow, rules and regulations like GDPR change what kinds of data companies and researchers can acquire and how they can analyze the data. The necessity of now performing causal inference under a range of privacy constrants has carved new spaces for research at the intersection of causal inference and privacy. In my thesis, I will be exploring three paradigms for protecting user data — data minimization, differential privacy and synthetic data — and how to perform causal inference techniques under these new privacy regimes.

Inferencing Techniques for Enhanced Monitoring of Thermal-Fluid Systems

Sat, 01 Feb 2025 00:00:00 GMT

Inferencing Techniques for Enhanced Monitoring of Thermal-Fluid Systems Kim, Haeseong Sensor data augmentation for accurate system monitoring is relevant to many engineering applications, as there is often a gap between available instrumentation and measurement needs. Installing sensors can be limited due to factors such as harsh environmental conditions, the need to avoid operational distortions, and limited space. While continued efforts to develop novel sensor technologies to improve measurement density and quality are important, it is equally crucial to maximize the use of data from existing sensors and measurements. In this work, we employed physics-based methods to solve inverse heat transfer (IHT) problems. Because accurate and well-understood physics models provide strong prior knowledge, physics-based IHT can provide clear solution with use of small amount of temperature measurements. However, existing work in IHT relies on 'perfect' physics models and has been used to solve relatively simple problems such as conduction heat transfer problems. This thesis extends the IHT problem scope to thermal fluid systems, including the efficient use of sensor data and uncertainty quantification (UQ). We leveraged high-resolution thermal-fluid experiments to demonstrate the solution of two types of IHT problems. The first problem estimates the operating conditions of the experiment based on the minimal use of sensors from high-resolution temperature data. The estimated solution is used to reconstruct the entire temperature distribution on a heating surface, while the rest of the data is used to validate the inverse problem methodology. The estimation result is supported by UQ considering measurement errors and modeling errors that adds value to the estimation. The second IHT problem consists of identifying sharp-featured 2D heat source distributions with an array of temperature sensors from a subset of experiment data. Solving IHT involved regularization prior with strong sparsity-promoting capability. The designed iterative solution optimization process finds the unknown heat source distribution as well as regularization hyperparameter. In addition, Bayesian inference enhanced the solution quality by providing UQ of the heat source magnitude. Expanding the scope of IHT problems, we also addressed online state estimation in dynamic systems. This work focuses on a hypothetical inverse conduction problem of a transient heat source in a composite materials system. The physics modeling of system is assumed to include uncertainty arising from gap thermal resistance at material interfaces, which complicates the estimation of an internal heat source from external sensor data. To address this challenge, the IHT approach leverages future time-step measurements to correct estimates at the current time step, enabling more efficient use of limited sensor information. The approach is sampling-based and its statistics provides UQ on the quantity of interest. While this work addresses inverse problems within specific thermal-fluid systems, the methodology is designed for broad applicability beyond these cases. It lays the groundwork for advanced sparse sensing and inverse problem-solving in thermal systems, offering a more efficient, tractable, and reliable tool for engineers and researchers addressing system monitoring with modeling uncertainty. Looking forward, these methodologies could be valuable for digital twin applications, where live sensor measurements are integrated to provide robust, real-time estimation of the state of physical systems.

Three Essays on the Economics of Land Use, Environmental Value, and Public Spending

Sat, 01 Feb 2025 00:00:00 GMT

Three Essays on the Economics of Land Use, Environmental Value, and Public Spending Larson, Kelsey R. Across the world, public spending on government programs profoundly alters land use, preservation of environmental value, and the wellbeing of rural populations. These essays explore three such programs and derive lessons for improving their targeting. Chapter 1 tests the effect of conservation easement tax incentives on land conservation in Virginia, using a difference-in-difference design around a 2002 tax reform. This finds that the environmental quality distribution of easements is wide and matches the statewide quality distribution of all undeveloped land, suggesting the program has considerable room to improve targeting. Increasing tax incentives attract donations of similar or lower quality, but targeting tax incentives only at high-quality land would substantially increase high-quality acres at a cost of 1.18 low-quality acres per high-quality acre. Chapter 2 investigates the targeting of short-term incentives for long-term behavior change, focusing on the case of the EQIP agricultural incentives program. The model connects the short-term and long-term effects of incentives as products of the immediate adoption costs and long-term repeated costs and benefits of a practice. If populations vary primarily by adoption cost, targeting groups with the greatest short-term effect will also maximize the long-term effect. If populations vary primarily by long-term costs and benefits, the groups with the greatest short-term impact are those for whom the practice is highly unprofitable in the long run, and a program can improve long-term impacts by instead targeting those for whom the practice is slightly profitable in the long run. A discontinuity analysis comparing successful and unsuccessful EQIP applicants shows that EQIP induces significant short-term change. Chapter 3 investigates the behavior of Mongolian livestock markets after severe weather shocks, and the role that a livestock insurance program may play in smoothing shocks. During severe Mongolian winters, livestock sales increase and prices fall as credit-constrained nomadic herders look to make necessary investments to protect their remaining herd. National integration in livestock markets absorbs a significant share of the weather-related shocks, as 40-60% of district price risk is due to national market fluctuations and 20-40% is due to province effects. This paper finds that national mortality strongly drives price variations, and livestock insurance reduces sales during high-mortality periods.

Engineering Disease Resistance in a Reservoir Species for the Mice Against Ticks Project

Sat, 01 Feb 2025 00:00:00 GMT

Engineering Disease Resistance in a Reservoir Species for the Mice Against Ticks Project Buchthal, Joanna This thesis explores the application of genome editing technologies to combat zoonotic infectious diseases through the development of a novel heritable immunization strategy targeting reservoir species. Focusing on Lyme disease, where white-footed mice (Peromyscus leucopus) serve as the primary reservoir, we propose embedding immunity into the germline of these animals to disrupt the disease transmission cycle and reduce the prevalence of the disease in the environment. By establishing genome engineering protocols for Peromyscus and demonstrating heritable protection against Lyme disease in genetically engineered Mus musculus, we show the feasibility of heritable immunization for long-term disease prevention. This work highlights the potential of genetic engineering for ecological interventions, offering a novel approach to public health challenges while fostering responsible community engagement in ecosystem engineering.

Mapping the Spatial Transcriptome Across Whole Organisms

Sat, 01 Feb 2025 00:00:00 GMT

Mapping the Spatial Transcriptome Across Whole Organisms Zhang, Ruihan This study utilizes Expansion Sequencing (ExSeq) to thoroughly investigate the spatial transcriptome of the Caenorhabditis elegans (C. elegans) body. Beyond mapping gene distribution within individual specimens, this research sequences multiple C. elegans to identify both shared and distinct transcriptomic features. The findings lay crucial groundwork for future integration of transcriptomic data with in situ connectomics and in vivo neural activity recordings. Understanding the spatial transcriptome in C. elegans is vital for insights into neural circuit coordination, disease mechanisms, and developmental biology.

The Economic Engineering of Personalized Experiences

Sat, 01 Feb 2025 00:00:00 GMT

The Economic Engineering of Personalized Experiences Haupt, Andreas A. Consumer applications employ algorithms to deliver personalized experiences to users, among others, in search, e-commerce, online streaming, and social media, impacting how users spend their time and money. This dissertation studies the design of such personalization algorithms and the economic consequences of their deployment. The first chapter focuses on the impacts of reward signal precision on online learning algorithms frequently used for personalization. Reward signals are precise when individual measurement is accurate and heterogeneity is low. While some algorithms, which we call "risk-averse", favor experiences that yield more precise reward signals and hence favor measurability and homogeneity, others, in the limit, choose experiences independently of the precision of their associated reward signals. The third chapter analyzes how preference measurement error differentially affects user groups in optimal personalization. If such measurement error is symmetric, welfare maximization requires delivering majority-preferred experiences at a rate beyond their proportion in the user population and hence increasing concentration. However, asymmetric preference measurement errors may arise due to users' actions to reduce measurement error. Participants in a survey of TikTok state that they engage in such costly actions. The fifth chapter studies, through the introduction of a new desideratum for market design, how to achieve personalization without infringing on user privacy. Contextual privacy demands that all (preference) information elicited by an algorithm is necessary for computing an outcome of interest in all possible configurations of users’ information. This property is demanding, as it requires that no two pieces of information can jointly but not unilaterally influence the outcome. Algorithms can protect the privacy of users who are queried late and whose information is not used to compute public statistics of the user population, hence achieving the relaxed notion of maximal contextual privacy. Two brief chapters introduce new models of human-machine interaction. The first examines the design of generative models, while the second proposes stated regret of past consumption as a new data modality and presents a corresponding data collection tool.

Creating Links: Building an Educational Platform to Ask Relevant Questions in Education

Sat, 01 Feb 2025 00:00:00 GMT

Creating Links: Building an Educational Platform to Ask Relevant Questions in Education García Bulle Bueno, Bernardo In this thesis, I document the findings and process through which we built an educational platform (JANN) to do research while having a positive impact on a community. Through JANN we have coordinated more than 100k hours of tutoring sessions and built (to our knowledge) one of the largest databases of educational recordings in the world. Broadly the contributions here are twofold: first, we demonstrate the research potential building a platform can offer. Second, using our educational platform, we pursue novel questions in the field of education with granular information that is traditionally inaccessible for research. After introducing the work and describing the construction of the platform, the first chapter details an RCT where we show the effect of receiving tutoring on Math performance. Second, we document how we built an estimator of emotions using audio. The estimator was further validated on our dataset and then used to show that activating emotions are related to better class quality. Third, we document an RCT where Math tutors were asked to dedicate some time per week to teach Socioemotional learning skills. We show that this had a positive effect on learning. Moreover, it also caused tutors to teach longer Math classes. Students showed more trust in their tutors, and ultimately the classes had a higher prevalence of positive emotions. Finally, we also study doing causal inference on observational data on another platform. Using Facebook data we study digital groups and through a regression discontinuity design we find that joining a group has a positive effect on making new friends and can diversify a person's connections in terms of income. Overall, we find that building a platform, can broaden the granularity of the data one has access to, make research more scalable, and ultimately also have a positive effect on a community.

Artificial Intelligence Tools, Curricula, and Agents for Creative Learning

Sat, 01 Feb 2025 00:00:00 GMT

Artificial Intelligence Tools, Curricula, and Agents for Creative Learning Ali, Safinah Arshad Children's early development of creativity contributes to their learning outcomes and personal growth. However, as children enter formal schooling systems, their creativity declines. Although Artificial Intelligence (AI)-powered tools for K-12 learning hold immense potential for reducing barriers to creative expression, access to these AI tools and AI knowledge among K-12 students and educators remains inequitable to children from groups underrepresented in STEM. In this thesis, I explore how AI, as an emerging creative medium, can be made more accessible to all young creators. I explore two mechanisms of making a mode of creation more accessible: Creative AI literacy materials for diverse classrooms and AI agentic interactions for scaffolding creative expression for diverse learners. Utilizing literacy as a mode of making Creative AI tools accessible, I outline the design and evaluation of various Creative AI curricula that I have developed for diverse groups of K-12 students and teachers. To adapt AI learning to art classrooms, I co-developed the AI and Art curriculum with creative educators, designed specifically for use in creative classrooms with creative educators and learners. I implemented the curriculum with 94 middle and high school students across six week-long sessions. I report findings from teacher co-design sessions and students’ learning experiences. Teachers designed learning objectives and AI tools for their classrooms. Students gained knowledge and skills in art concepts, AI concepts, and the application of art in AI. Students also demonstrated significant shifts in their attitudes towards using AI in the creative process, and their sense of belonging in both AI and art communities was heightened. I discuss how AI curricula can be adapted to diverse disciplines and how art can serve as a meaningful avenue for students to engage with AI concepts. Utilizing social interaction from AI agents as a mode of fostering creative expression in children with neurodevelopmental disorders, I designed and applied inclusive child-robot interactions for collaborative creativity, where 32 elementary school children and a social robot collaboratively created picture stories. The robot provided creativity scaffolding during different parts of the creative storytelling process through social interactions such as feedback, question-asking, divergent thinking, and positive reinforcement, while personalizing the scaffolding to meet the unique needs of neurodivergent children. I investigated the impact of the social robot on children’s exhibited creativity and their emergent creative collaborative interactions in storytelling over multiple sessions. Inclusive design practices eliminated creative barriers for children with neurodevelopmental disorders, and the robot's creativity scaffolding interactions positively influenced children’s creative product and creative process in storytelling. I propose Inclusive Co-creative Child-robot Interaction (ICCRI) guidelines for fostering creativity in children with neurodevelopmental disorders, and accommodating diverse creator styles in complex, open-ended creative tasks. In this thesis, I contribute curricula, learning tools, child-robot interactions, and findings from examining long-term child-AI co-creative interactions. I discuss design implications for integrating AI tools, curricula and agents in creative learning environments. This thesis is a step towards empowering all children with powerful modes of creation, while helping them be responsible creators, thinkers and citizens in an AI-driven future.

Higher-Order Interactions in Social Systems

Sat, 01 Feb 2025 00:00:00 GMT

Higher-Order Interactions in Social Systems Sarker, Arnab Kumar The de facto representation of a social network is a graph— individuals are represented as nodes, and relationships between pairs of individuals are represented as edges. This results in a powerful abstraction by which social relationships can be systematically studied to understand emergent population-scale behavior. However, many social interactions occur in groups: three individuals may co-author a paper, a team of employees may collaborate on a task, a single tweet may mention four users. Breaking such interactions into a collection of pairwise relationships can oversimplify the rich social contexts in which these individuals know one another. This thesis explores a different paradigm of social network analysis, namely, using "higher-order" network models such as hypergraphs and simplicial complexes which can explicitly encode co-present contexts between three or more individuals. The first two projects describe how higher-order interactions can differ from pairwise interactions in terms of micro-level content and macro-level structure, respectively. The latter two projects then develop an applied mathematical toolkit for the algebraic topological analysis of higher-order interactions in social networks.

Modulating the Electrochemistry of Calcium Metal Anodes

Wed, 01 May 2024 00:00:00 GMT

Modulating the Electrochemistry of Calcium Metal Anodes Melemed, Aaron M. Rechargeable lithium ion (Li-ion) batteries have been a foundational energy storage technology. However, there are significant technoeconomic, geopolitical, and sustainability concerns regarding the procurement of Li-ion battery components, from transition metals within the cathode to lithium itself. As such, the development of battery chemistries beyond Li is vital to the long-term viability of electrochemical energy storage. Batteries based on calcium (Ca) metal anodes offer a compelling alternative; Ca is the fifth-most abundant element in the earth’s crust at 41,500 ppm (vs. 200 ppm for Li), offering potential improvements in scalability and sustainability. Ca metal also offers attractive electrochemical metrics, with a redox potential 0.17 V more positive than Li and a theoretical volumetric capacity of 2073 mAh/cm³ (vs. 850 mAh/cm³ for graphite and 2062 mAh/cm³ for Li metal). The field of Ca metal batteries is currently in its early stages, however, due to a limited number of electrolytes that can reversibly plate and strip Ca — a requirement for rechargeability. Two important challenges to overcome are (1) the formation of a passivating solid electrolyte interphase (SEI) between Ca and the electrolyte that inhibits Ca²⁺ transport to the anode, and (2) attractive Ca²⁺--anion interactions in the electrolyte that suppress ionic conductivity and hinder Ca electrochemistry. These limitations rendered Ca plating/stripping unattainable until a groundbreaking first demonstration in 2015. In the decade since, only a handful of reversible electrolytes have been reported, reflecting a severely constrained electrolyte design space. This thesis expands upon this design space through interfacial and electrolyte engineering, offering novel techniques to modulate Ca electrochemistry that provide new degrees of freedom for the development of Ca-based batteries. To begin, the practical assembly and cycling behavior of Ca foil electrodes are examined in a reversible electrolyte system for the first time. In contrast to historical work examining Ca foil in other common battery electrolytes, Ca foils are demonstrated to be electrochemically accessible for both plating and stripping in Ca(BH₄)₂ in tetrahydrofuran (THF). However, the first cyclic voltammetry (CV) cycle reflects persistent, history-dependent behavior from prior handling, which manifests as characteristic interface-derived features. Three exemplar SEI exhibit this interface-dominated behavior during initial CV cycles, though the interfacial features diminish with continued cycling. These results reveal that long-term cycling behavior is, to a greater extent, governed by the electrolyte, informing ensuing research into electrolyte composition and speciation. Competitive interactions between Ca²⁺, anions, and solvent molecules are next harnessed to modify the Ca²⁺ coordination environment in this baseline electrolyte. An exemplar dual-salt electrolyte with differing Ca²⁺--anion interaction strengths, Ca(BH₄)₂ + Ca(TFSI)₂ in THF, is systematically altered. Introduction of a more-dissociating source of Ca²⁺ via Ca(TFSI)₂ drives re-speciation of strongly ion-paired Ca(BH₄)₂, generating larger populations of charged species and enhancing Ca plating currents. A critical parameter is proposed to govern electroactivity, the BH4 /Ca²⁺ ratio. Parasitic TFSI- decomposition prevents Ca plating when the BH4-/Ca²⁺ ratio is less than one. However, Ca plating in a TFSI--containing electrolyte is demonstrated for the first time when the BH4-/Ca²⁺ ratio is greater than 1, as BH₄⁻ displaces strongly coordinating TFSI⁻ from the Ca²⁺ coordination environment. These results directly evidence the impact of coordination-shell chemistry on plating activity and indicate that Ca²⁺--BH₄⁻ interactions can unlock electroactivity in the presence of other Ca salts, significantly increasing the Ca electrolyte design space. Ca²⁺--solvent interactions are next examined as a subtler tool for electrochemical manipulation. The systematic introduction of glymes into the baseline electrolyte is shown to induce differential changes in Ca²⁺ coordination, as stronger glyme coordination displaces THF from the Ca²⁺ coordination environment, weakens Ca²⁺--BH₄⁻ interactions, and prompts BH₄⁻ redistribution. Examination of electrochemically-formed SEI indicates that BH₄⁻-facilitated solvent decomposition governs Ca electrochemistry in these systems, as coordinated THF promotes beneficial borate formation in the SEI but coordinated glymes instead favor the formation of Ca²⁺ blocking phases. The link between Ca²⁺ coordination strength and solvent decomposition is corroborated through the quantification of gaseous products. Altogether, these strategies for the modulation of Ca electrochemistry reveal new avenues for electrolyte engineering that will promote further development of Ca-based batteries.

Heat transfer and friction for heating and cooling of fluids in pipes

Wed, 01 Jan 1930 00:00:00 GMT

Heat transfer and friction for heating and cooling of fluids in pipes Keevil, Charles S. (Charles Samuel) Thesis: Sc. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 1930; Includes bibliographical references (leaves 135-136).

The classification of the indecomposable integral representations of the dihedral group of order 2p

Mon, 01 Jan 1962 00:00:00 GMT

The classification of the indecomposable integral representations of the dihedral group of order 2p Leahey, William Joseph. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1962; Vita.; Includes bibliographical references (leaf 84).

Visual system analysis through use of finite field sine gratings.

Mon, 01 Jan 1973 00:00:00 GMT

Visual system analysis through use of finite field sine gratings. Magnuski, Henry Stanley. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering, 1973; Vita.; Bibliography: leaves 150-152.

From Data, to Models, and Back: Making Machine Learning Predictably Reliable

Sat, 01 Feb 2025 00:00:00 GMT

From Data, to Models, and Back: Making Machine Learning Predictably Reliable Ilyas, Andrew Machine learning systems exhibit impressive performance, but we currently lack scalable ways to anticipate their successes, failure modes, and biases. This position limits our ability to deploy these systems in the appropriate contexts, and to build systems which we can confidently deploy in high-risk settings. Motivated by this state of affairs, this thesis aims to develop design principles for predictably reliable machine learning. Our ultimate goal is to enable developers to know when their models will work, anticipate when they will fail, and understand “why” in both cases. In pursuit of this goal, this thesis combines large-scale experiments with theoretical analysis to form a precise understanding of the ML “pipeline,” from training data (and the way we collect it), to learning algorithms, to deployment. Fully realized, such an understanding would allow us to build ML systems the same way we build buildings or airplanes—safely, scalably, and with a robust grasp of the underlying principles. In this thesis, we focus on four design choices within this pipeline: model deployment (Part I), dataset creation (Part II), data collection (Part III), and algorithm selection (Part IV). For each of these design choices, we use targeted experiments to uncover the corresponding principles that actually underlie the behavior of ML systems. We distill these principles into concise conceptual models which allow us to both reason about existing systems and design improved ones. Along the way, we will revisit, challenge, and refine various aspects of conventional wisdom surrounding ML model development.

ΔB₀ Field Control in High Field MRI with Local Multcoil Shim Arrays

Sat, 01 Feb 2025 00:00:00 GMT

ΔB₀ Field Control in High Field MRI with Local Multcoil Shim Arrays Arango, Nicolas Local multicoil ΔB₀ shim arrays enable low-cost, simple to fabricate, and physically small, static magnetic field control in magnetic resonance imaging. The presented thesis will show frameworks for coil current calculation for homogeneity and novel selective excitation applications. As MRI RF coils trend towards repositionable and flexible systems for their ease of use and tight-to-the-patient fit, ΔB₀ shim arrays are left behind for lack of rapid, patient-on-the-table calibration. We show an inverse problem approach with physics-based regularization and adaptation to accelerate calibration by over 50 fold. The numerical tools developed for calibration also proved useful for design to enable novel upper bounds on ΔB₀ shim performance and new tools for automatic application and anatomy-specific local multicoil array design.

Non-orthogonal multiple access using guessing random additive noise decoding aided macrosymbols

Sat, 01 Feb 2025 00:00:00 GMT

Non-orthogonal multiple access using guessing random additive noise decoding aided macrosymbols Yang, Kathleen We propose guessing random additive noise decoding-aided macrosymbols (GRANDAM) as a nonorthogonal multiple access (NOMA) method that can detect, error correct, and decode multiple users in multiple input multiple output (MIMO) systems that involve imperfect channel estimation, symbol-wise asynchronous transmission, and interference. GRAND-AM is a NOMA method that uses both joint multiuser detection and joint error correction decoding to handle multiple access interference (MAI) from the users of interest. Our method avoids codebook design and iterative decoding techniques, which are associated with other commonly researched NOMA techniques. We introduce the concept of a macrosymbol, which is constructed from the combination of all user symbols, for the joint detection component of GRANDAM. For the error correction decoding component, we introduce multiple access channel (MAC) codes, which are codes that are used to split the channel rate between users and correct errors due to the MAI. Each user has their information bits encoded with independent MAC codes, which can be short, low rate linear codes such as cyclic redundancy check (CRC) codes or space time codes such as the Alamouti code. We use a soft detection variant of GRAND, a near maximum likelihood (ML) universal decoding algorithm that inverts noise effect sequences from a sequence of symbols to arrive at a codeword, to correct the received sequence of macrosymbols, and ensure that all user codebooks are simultaneously satisfied in the joint decoding process. We show that the methodology of using joint detection and joint decoding at the receiver leads to lower error rates compared to an individual detection and decoding technique, and has comparable performance to an orthogonal multiple access (OMA) system with a similar code rate and length.

System-Technology Co-Optimization of Scaled Electronics Based on Two-Dimensional Materials

Sat, 01 Feb 2025 00:00:00 GMT

System-Technology Co-Optimization of Scaled Electronics Based on Two-Dimensional Materials Zhu, Jiadi Over the past 60 years, the semiconductor industry has focused on developing highly scaled electronic devices and high-density integrated circuits. However, bottlenecks have arisen recently as transistor dimensions approach the physical limits, and integration density is constrained. This thesis addresses these issues with two-dimensional (2D) materials, which includes inventing a low-temperature (< 300 °C) metal-organic chemical vapor deposition (MOCVD) method for 2D materials on 8-inch wafers, investigating extreme device scaling and multi-channel transistors. Design-Technology Co-Optimization (DTCO) and SystemTechnology Co-Optimization (STCO) are employed to rapidly model, evaluate and optimize device and circuit performance. Moreover, heterogeneous integration and monolithic 3D integration techniques are investigated, addressing challenges in integrating 2D materials with silicon complementary-metal-oxide-semiconductor (CMOS) circuits and flexible substrates. This research aims to advance high-density, high-performance electronics with low-power consumption for next-generation integrated systems.

Data-Driven General Purpose Foundation Models for Computational Pathology

Sat, 01 Feb 2025 00:00:00 GMT

Data-Driven General Purpose Foundation Models for Computational Pathology Lu, Ming Yang (Max) The field of computational pathology has undergone a remarkable transformation in recent years. Researchers have leveraged supervised and weakly-supervised deep learning with varying degrees of success to address a wide range of tasks, including cancer subtyping and grading, metastasis detection, survival and treatment response prediction, tumor site-of-origin identification, mutation prediction, biomarker screening, and more. However, traditional task-specific models often require extensive labeled data and struggle to generalize across diverse pathology tasks. This limitation motivates the exploration of foundation models, which promise a more scalable, versatile solution by learning broad representations that can be adapted to various downstream applications. In this thesis, I will investigate the capabilities and limitations of data-driven foundation models in computational pathology. Specifically, I will explore two frameworks for developing general-purpose encoder models for pathology images: one using paired image-text data, and another leveraging self-supervised learning on large-scale unlabeled images. Additionally, I will examine downstream applications of these foundation models, including zero-shot transfer to gigapixel whole slide images and the development of an interactive multimodal AI assistant for pathologists.

Exciton Dynamics and Optical Properties of Lead Halide Perovskite Nanocrystals: From Nanorods to Nanocubes

Sat, 01 Feb 2025 00:00:00 GMT

Exciton Dynamics and Optical Properties of Lead Halide Perovskite Nanocrystals: From Nanorods to Nanocubes Šverko, Tara Lead halide perovskites, particularly CsPbBr3, have emerged as leading light emitters for their spectral purity, brightness, and facile synthesis. Their soft, ionic lattice makes them unusually defect tolerant but introduces problems with stability. Additionally, dephasing mechanisms and coupling to phonons are not yet well understood in these semiconductors. In the first part of the thesis, I investigate highly confined, anisotropic CsPbBr3 nanorods, elucidating the photophysics governing their broad single-particle linewidths. I utilize ensemble and single particle photoluminescence techniques across a wide temperature range in order to pinpoint exciton-phonon coupling mechanisms, structural and surface effects, and spin mixing in these novel materials. In the second part of the thesis, I focus on the opposite size regime, where collective behaviour dominates the optical properties. I develop a novel spectroscopy to pinpoint dephasing mechanisms that could reduce superradiant and coherent emission in order to promote rational design and future integration of these nanocrystals into quantum information devices.

Systems for Usable Machine Learning

Sat, 01 Feb 2025 00:00:00 GMT

Systems for Usable Machine Learning Zytek, Alexandra Many real-world decision problems are complex, with outcomes difficult to measure and evaluate. The impact of decisions made in these domains is nuanced and takes a long time to be fully realized. Individual mistakes can lead to significant costs, and computational tools such as ML models must be integrated alongside existing, well-established human workflows. These properties of such decision problems means that ML solutions must be usable in order to be effective — in other words, developed and deployed in such a way as to be used by humans in decision-making and improve outcomes. In order improve ML usability, developers create ML tools, or diverse kinds of interfaces that allow users to understand ML models and their predictions. In this thesis, we use real-world case studies to synthesize generalizable lessons for applying usable ML tools to complex, real-world decision problems. Based on experience developing ML tools for child welfare screening, we propose a formal taxonomy of feature properties related to usability and interpretability. We then discuss the design and development of a system to make generating ML explanations that use such interpretable features more effective. Pyreal is a framework and Python library implementation that uses updated data transformers to generate explanations of ML models and predictions using interpretable features. Motivated by the development and customization effort required to develop ML tools for new applications, we then discuss the development of Sibyl, a configurable and comprehensive system for generating usable ML interfaces for a wide range of applications. We then discuss our case study in applying Sibyl to the decision problem of wind turbine monitoring. We then discuss Explingo, our system for transforming traditional ML explanations into natural language narratives to further improve the usability of ML outputs. We finish by discussing the practical lessons this work demonstrates related to the need for usable ML, the challenges specific to these complex applications, ethical questions, and future directions.

Causal Foundations for Pragmatic Data Science

Sat, 01 Feb 2025 00:00:00 GMT

Causal Foundations for Pragmatic Data Science Squires, Chandler A key goal of scientific discovery is the acquisition of knowledge that is practically useful for societal endeavors, such as the development of medicine or the design of fruitful economic policies. In this thesis, I place front and center the role that scientific models play in the process of decision-making, emphasizing the importance of causal models in science, i.e., models which describe the possible effects of actions upon a system. The work contained explores central topics in this domain, including causal discovery (learning causal models from data), causal representation learning (learning how to coarse-grain observations into causally sensible “macro-variables”), and end-to-end causal inference (the interplay between causal discovery and downstream decision-making).

Techniques for Foundational End-to-End Verification of Systems Stacks

Sat, 01 Feb 2025 00:00:00 GMT

Techniques for Foundational End-to-End Verification of Systems Stacks Gruetter, Samuel Today's software is full of bugs and vulnerabilities. Formal verification provides a promising remedy through mathematical specifications and machine-checked proofs that the implementations conform to the specifications. However, there could still be bugs in the specifications or in the verification tools, which could lead to missed bugs in the software being verified. Therefore, this dissertation advocates for foundational end-to-end verification, a proof-based software development method that can mitigate both of these concerns: It is end-to-end in the sense that the correctness proofs of individual components are used to discharge the assumptions of adjacent components throughout the whole stack, resulting in end-to-end theorems that only mention the top-most and bottom-most specifications, so that bugs in intermediate specifications cannot invalidate the soundness of the end-to-end statement anymore. The method is foundational in the sense that the soundness of the proofs relies only on the foundations of mathematics and on the correctness of a small proof-checking kernel, but not on the correctness of other, domain-specific verification tools, because these tools are either proven correct once-and-for-all, or they output proofs that are checked by the kernel. Ensuring that all the reasoning can be checked by the same small foundational kernel requires considerable effort, and the first part of this dissertation presents techniques to reduce this effort: Omnisemantics, a new style of semantics that can be used instead of traditional small-step or big-step operational semantics, offer a smooth way of combining undefined behavior and nondeterminism, and enable forward-simulation compiler correctness proofs with nondeterministic languages, whereas previous approaches need to fall back to the much less convenient backward simulations if support for nondeterminism is needed. Live Verification is proposed, a technique to turn an interactive proof assistant into a programming assistant that displays the symbolic state of the program as the user writes it and allows the user to tweak the symbolic state as long as the tweaks are provably sound. An additional convenience-improving feature is that instead of stating lengthy loop invariants, the user only needs to give the diff between the symbolic state before the loop and the desired loop invariant, resulting in shorter and more maintainable annotations. Finally, in order to make Live Verification practical, a number of additional proof techniques is presented. The second part of the dissertation shows how these techniques were useful in three collaborative case studies: An embedded system running on a verified processor with an end-to-end proof where the software-hardware interface specification cancels out, a cryptographic server with an end-to-end proof going from high-level elliptic-curve math all the way down to machine code, and a trap handler to catch unsupported-instruction exceptions whose correctness proof combines program-logic proofs about C-level functions, a compiler correctness proof, and proofs about hand-written assembly.

Tackling Algorithmic Problems on Massive Graphs

Sat, 01 Feb 2025 00:00:00 GMT

Tackling Algorithmic Problems on Massive Graphs Biswas, Amartya Shankha As datasets grow increasingly larger, traditional computational models, which require reading the entire input, become impractical due to constraints on time, memory, and randomness. This thesis explores alternative algorithmic approaches for processing massive graphs under these constraints. Specifically, we focus on algorithms for the following graph problems. Motif Counting and Sampling: This involves developing efficient algorithms for counting and sampling small motifs (constant sized subgraphs) like stars and triangles, which are crucial for applications in biology, chemistry, and social networks. The thesis presents improved methods for both approximate and exact counting and sampling of general motifs. Graph Sparsification and Spanners: The problem of sparsifying graphs involves removing (usually most) edges of the input graph in a way that preserves essential properties such as connectivity and approximate distances. This thesis introduces algorithms for constructing sparse spanning graphs, as well spanners – sparse subgraphs that approximate distances up to a multiplicative factor. We obtain faster algorithms in parallel settings, and also initiate the study of average case graph inputs in the sublinear setting, and obtain results beyond the worst case lower bounds We investigate both of these problems in different models, including sublinear query access, local computation algorithms (LCAs), and the MPC model, and also discuss implications of these in distributed and parallel models of computation.

Design of Lewis Acidic Pnictenium Ions Using Carbone and Capping Arene Ligands for Bond Functionalization

Sat, 01 Feb 2025 00:00:00 GMT

Design of Lewis Acidic Pnictenium Ions Using Carbone and Capping Arene Ligands for Bond Functionalization Warring, Levi Interest in the chemistry of antimony and bismuth is rapidly growing due to isolation of low coordinate, subvalent or Lewis acidic compounds that can mediate reactivity traditionally reserved for their d block counterparts. Ligand strategies play a key role in the isolation of such species. Anionic ligands with large steric profiles, as well as carbenes, have been widely implemented to stabilize subvalent heavy group 15 element compounds. However, synthetic strategies to prepare Lewis acidic antimony and bismuth complexes remain underexplored. Cationization is one of the most common methods used to enhance the Lewis acidity of heavy group 15 elements by creating a vacant p orbital on the pnictogen atom. Lewis acids are also employed in frustrated Lewis pair (FLP) chemistry to enable intra- and intermolecular reactivity. Carbone ligands, which are neutral, 4 electron donor ligands, offer a unique ability to support highly electrophilic main-group elements. This dissertation investigates the stabilization of heavy pnictenium ions using neutral donor ligands, such as carbodicarbenes and capping arene ligands, and explores their potential in Lewis acid-mediated chemistry. In Chapter Two, the synthesis and characterization of a series carbodicarbene-pnictenium ions is described. The utilization of strongly donating carbodicarbene ligands enables the isolation of mono-, di- and tri-cationic antimony and bismuth cations. These ions have multiple bond character between carbon and antimony/bismuth, representing some of the first examples of stibaalkene and bismaalkene cationic compounds. The Lewis acidity of these ions was assessed using the Gutmann-Beckett method and computationally derived fluoride ion affinities, the latter of which indicates Lewis superacidity for the bis(pyridyl)carbodicarbene-pnictenium trications. In Chapter Three, the reactivity of the bis(pyridyl)-carbodicarbene stibenium trication toward C(sp³)–H and C(sp)–H bonds is demonstrated. The Lewis superacidic antimony cation mimics the chemistry of frustrated Lewis pairs in the presence of the sterically encumbered base 2,6-di-tert-butylpyridine to enable C–H bond breaking of acetonitrile and a set of terminal alkynes. Kinetic analyses, in conjunction with density functional theory, support a mechanism by which acetonitrile coordinates to antimony, acidifying the C–H bonds, which can be subsequently deprotonated by the base in solution. The resulting stiba-methylene nitrile and stiba-alkynyl adducts undergo reactivity with elemental iodine to generate iodoacetonitrile and 1-iodoalkynes while reforming a stibenium trication. In Chapter Four, capping arene ligands are coordinated to antimony and bismuth tribromide to afford a series of κ²-bound complexes. Bromide abstraction from these neutral adducts affords ionic compounds. Both the neutral and ionic species have distinctive Menschutkin interactions, whereby the lone pair on the pnictogen atom is oriented toward the π system of the pendant arene. Shortening of the distances between the pyridyl nitrogen atoms and pnictogen atom are observed upon cationization from the neutral adducts. The Lewis acidity of these complexes was assessed using the Gutmann-Beckett method. Notably, acceptor numbers as high as 111 are observed for these ions.

Leveraging Structure for Efficient and Dexterous Contact-Rich Manipulation

Sat, 01 Feb 2025 00:00:00 GMT

Leveraging Structure for Efficient and Dexterous Contact-Rich Manipulation Suh, Hyung Ju Terry Contact-rich manipulation has proved challenging due to the need to consider multiple combinatoric possibilities of making or breaking contact with the surrounding environment. As a result, existing methods have often resorted to combinatorial optimization that utilizes dynamics structure but considers all possibilities exhaustively, or compute-heavy and inefficient sampling methods that utilize blackbox optimization such as Reinforcement Learning (RL). In this thesis, I aim to show that by combining structured contact smoothing in conjunction with local gradient-based control and sampling-based motion planning, we can bypass the combinatorial explosion of contact modes while still leveraging structure and achieve highly efficient contact-rich manipulation. To achieve this capability, I first shed light on how RL abstracts contact modes and optimizes difficult landscapes by combining stochastic smoothing and zeroth-order optimization; yet, I show how following a similar stochastic strategy while using gradients suffers from several drawbacks such as empirical bias and high variance. To leverage structure in a more helpful manner, I propose a method for smoothing contact dynamics without relying on stochastic smoothing, bypassing these drawbacks. Using this smoothing scheme, I present a highly efficient and performant local control based on gradient-based trajectory optimization and model predictive control. Finally, I connect these local control capabilities with global sampling-based motion planners to achieve long-horizon global plans. The proposed method achieves contact-rich plans such as dexterous in-hand reorientation and whole-body manipulation much more efficiently than RL while being highly scalable compared to methods that explicitly reason about contact modes. These results achieve a reduction of contact-rich manipulation to kinodynamic motion planning, and exposes the true difficulty of contact-rich manipulation from combinatorial explosion in contact modes to combinatorial and highly non-local decisions over motion planning behaviors.

Quality-Centric Single-Image Procedural Material Generation

Sat, 01 Feb 2025 00:00:00 GMT

Quality-Centric Single-Image Procedural Material Generation Li, Beichen Procedural materials, represented as functional node graphs, are ubiquitous in computer graphics for photorealistic material appearance design. They allow users to perform intuitive and precise editing to achieve desired visual appearances. However, even for experienced artists, creating a procedural material given an input image requires professional knowledge and significant effort. Current inverse procedural material modeling approaches enable the automatic generation of procedural materials from input images. However, the visual quality of the generated materials is fundamentally limited by insufficient high-quality training data from industry-standard procedural materials, reliance on token-space supervision without visual feedback, and the absence of approximation-free node parameter post-optimization. My thesis presents advanced dataset augmentation, model training, and parameter post-optimization algorithms to address these challenges, significantly improving the perceptual match between the generated procedural material and the input image. Furthermore, the methodologies can be applied to other inverse procedural graphics problems to expedite similar artistic creation processes.

Development of Additively Manufactured Quadrupole Mass Filters for Low-Cost and High-Performance Applications

Sat, 01 Feb 2025 00:00:00 GMT

Development of Additively Manufactured Quadrupole Mass Filters for Low-Cost and High-Performance Applications Eckhoff, Colin C. With a growing need for more compact and affordable mass spectrometers, many efforts have been made to miniaturize quadrupole mass filters (QMFs). Unfortunately, these efforts have yielded devices with inadequate performance for practical applications in analytical chemistry. This study reports the successful creation of a low-cost, high-performance QMF by means of additive manufacturing. Vat photopolymerization of glass-ceramic feedstock was used to create a novel, monolithic structure, and selective electroless nickel-boron plating metallizes the structure, forming a completed QMF that is lightweight and inexpensive to produce (20 USD per device). Furthermore, additive manufacturing allows QMF dimensions to be rapidly scaled to the optimal sizes for a given application, which is larger than most prior affordable quadrupole designs. Despite the limited precision of additive manufacturing, optimization techniques can be leveraged to produce high-quality devices with smooth surfaces. As a result, our QMFs achieved mass resolutions up to 164 at 69 Da, with abundance sensitivities sufficient to detect carbon-13 isotopes at lower masses—a level of performance comparable to commercial devices. These results indicate that additive manufacturing, properly employed, can significantly advance the state of the art of QMFs and other mass spectrometry technologies.

Fast methods for full-wave electromagnetic solvers in MRI

Sat, 01 Feb 2025 00:00:00 GMT

Fast methods for full-wave electromagnetic solvers in MRI Guryev, Georgy D. High static field ( 3T) MR scanners can produce human tissue images of astounding clarity, but rely on high frequency ( 123MHz) electromagnetic radiation that generates complex in-tissue field patterns that are patient-specific and potentially harmful. Many such scanners use multiple transmitters to better control field patterns, but then adjust the transmitters based on general guidelines rather than optimizing for the specific patient, mostly because computing patient-specific fields was presumed far too slow. It was recently demonstrated that the combination of fast low-resolution tissue mapping and fast voxel-based field simulation can be used to perform a rapid patient-specific MR safety check. However, the field simulation still required several minutes, making it too slow to perform the dozens of simulations that would be needed for patient-specific optimization. In this work, we develop a set of numerical acceleration techniques that facilitate fast field simulations that bridge the gap between the performance of current state-of-art full-wave electromagnetic packages and time requirements dictated by real-time patient-specific field optimization in a clinical setting. These techniques cater to a large range of body sizes and complex coil geometries.

Weak Shock Waves on a Chip: Generation and Applications

Sat, 01 Feb 2025 00:00:00 GMT

Weak Shock Waves on a Chip: Generation and Applications Deschamps, Jude In conventional laser-shock experiments in solid media, shock waves are typically excited from the ablation of a photoacoustic transducer layer deposited onto the sample of interest. Unavoidably, the target materials are damaged. This leads to the necessity of changing targets after each exposure, likely lowering the shot-to-shot reproducibility and data quality, while lowering the throughput of the experiment. Motivated by the need to generate large-amplitude transient strain waves at a high repetition rate, this thesis introduces a novel platform for the non-destructive generation and amplification of acoustic waves with associated strain levels in the percent range — up to the formation of shock waves. The acoustic amplification scheme is first described. Then, owing to the capabilities of the technique to repeatedly load a material with finite-amplitude strain waves, a demonstration of the use of the platform for microscale fatigue testing is made. Finally, the strain localization of surface acoustic waves is leveraged by transiently modulating a monolayer of a transition metal dichalcogenide deposited on a substrate.

Novel Structures for Scalable Vertical Gallium Nitride Power Devices

Sat, 01 Feb 2025 00:00:00 GMT

Novel Structures for Scalable Vertical Gallium Nitride Power Devices Perozek, Joshua Andrew Solid state electronic devices have been the backbone of modern power systems for decades. However, as we enter an era fuelled by renewable energy and defined by pervasive electrification, novel power devices must be developed to address the increasingly stringent demands for high power density and efficiency. In this thesis, the theory and fabrication of several new gallium nitride (GaN) power devices will be developed to push beyond current device limitations. A key advancement surrounds the acknowledgment that vertical GaN power devices are fundamentally three-dimensional. Fabrication of these devices does not readily benefit from the decades of expertise gained in planar processing within the silicon industry. Instead, we will present how a new approach to creating vertical fin-based devices will enable self-aligned fabrication of vertical GaN finFETs and related devices. Within this work, we also explore the scalability of vertical GaN finFETs. Working with 8-inch GaN substrates, we demonstrate that vertical finFETs can be fabricated using a fully CMOS compatible process flow. This enables a scalable pathway to the widespread adoption of GaN by leveraging existing manufacturing capabilities. As a final look towards the future of GaN devices, we explore methods to surpassing the one-dimensional, unipolar limit of GaN through devices known as superjunction. The theory that has been highly successful for Si devices is applied to GaN, and a new framework for designing devices is presented. Using our approach to creating vertical fin-based devices, we are able to fabricate record high aspect-ratio demonstrations of a new class of fin diodes that reveal a promising path towards the next generation of GaN power devices.

Learning Generalizable Systems by Learning Composable Energy Landscapes

Sat, 01 Feb 2025 00:00:00 GMT

Learning Generalizable Systems by Learning Composable Energy Landscapes Du, Yilun How can we construct intelligent embodied agents in the physical world? Such agents should be able to autonomously solve tasks that have not been seen before, subject to external disturbances in the environment, as well as new combinations of factors such as lighting, varying sensor inputs, and unexpected interactions with agents and other objects. An important subgoal towards constructing such intelligent agents is to construct models that can robustly generalize, not only to distributions of tasks similar to ones seen at training time but also to new unseen distributions. This departs from standard machine learning techniques which usually assume identical training and test distributions. Towards this goal, in this dissertation, we’ll illustrate how we can achieve certain forms of generalization by estimating energy landscapes over possible predictions for each task, with accurate predictions assigned lower energy. This modeling choice formulates prediction as a search process on the energy landscape, enabling zero-shot generalization to new constraints by adapting the energy landscape. In addition, this allows us to generalize to entirely new distributions of tasks in a zero-shot manner by composing multiple learned energy landscapes together. In this dissertation, we first introduce a set of techniques to train energy landscapes and an algebra in which we can compose and discover composable energy landscapes. Next, we illustrate how energy landscapes can be composed in a diverse set of ways, ranging from logical operators, probability distributions, graphical models, constraints, and hierarchical compositions, enabling effective generalization across vision, decision-making, multimodal, and scientific settings.

(De)fluorination of Organic Substrates Mediated by Nontrigonal Phosphorus Triamide

Sat, 01 Feb 2025 00:00:00 GMT

(De)fluorination of Organic Substrates Mediated by Nontrigonal Phosphorus Triamide Lim, Soohyun Due to its high electronegativity and small size, fluorine atoms form the strongest single bond to carbon, and impart unique physical, chemical, and physiological properties to organic compounds. Therefore, the number of industrially synthesized products containing fluorine has seen a substantial increase in recent decades. The strategies to access organofluorine compounds include two opposite approaches: 1) (nucleophilic, electrophilic, or radical) fluorination, and 2) selective defluorination of polyfluorinated substrates. Both creating and breaking C−F bonds in selective manners are of great importance, and present challenges of their own. The work herein describes chemical transformations incorporating the cleavage or formation of the C−F bonds mediated by nontrigonal phosphorus triamide. Thanks to the enhanced biphilicity resulting from geometric deformation, the Cs-symmetric tricoordinate phosphorus compound can activate strong covalent bonds. At the outset, Chapter 1 reviews the existing literature on (de)fluorinative chemical transformations focused on deoxyfluorination and hydrodefluorination, as well as examples of nontrigonal tricoordinate phosphorus compounds and their characteristic reactivity. Combining the two approaches, Chapters 2 and 3 introduce method development for accessing organofluorine compounds using a butterfly-shaped phosphorus triamide as a bond activator. In Chapter 2, the method for deoxyfluorination of aliphatic alcohol substrates via O−H activation by phosphorus, catalyzed by borane Lewis acids, is detailed. The scope of the method covers tertiary alkyl fluorides, which are generally challenging targets, selectively yielding stereoinversion products for chiral substrates. Chapter 3 reports a closed P(III)/P(V) synthetic cycle for the hydrodefluorination of polyfluoroarene substrates that consists of C−F oxidative addition, F-to-H ligand metathesis, and C−H reductive elimination. The overall sequence is analogous to transition metal-catalyzed aryl cross-coupling reactions. Taken together, the methods described in this dissertation highlight the potential of nontrigonal phosphorus compounds as a mediator for the manipulation of strong covalent bonds, useful in the development of synthetic methods that complement existing ones.

Developing Telecom Band-Compatible Molecular Color Centers for Quantum Networking

Sat, 01 Feb 2025 00:00:00 GMT

Developing Telecom Band-Compatible Molecular Color Centers for Quantum Networking Greer, Rianna Bliss Quantum networking is a new modality of information transmission that will revolutionize the future of telecommunications. However, the realization and widespread use of quantum networking demands low signal loss and distortion over long distances. To achieve this, prospective materials for quantum networking must emit in fiber optics’ optical communications band defined as 1260 to 1625 nm, commonly known as the “telecom band.” Vanadium dopants in silicon carbide have demonstrated near-infrared emission combined with a spin-photon interface, but these systems lack tunability over emission wavelength, preventing emission in the telecom band. This thesis combines the promising electronic structure of these dopants and the inherent tunability of molecular systems to create a family of luminescent paramagnetic vanadium complexes that can achieve both telecom band emission and generalized finetuned control over emission wavelength. Chapters 2 and 3 will outline approaches to target telecom band emission in a series of V_III complexes through a gradual and controlled increase of metal-ligand bonding covalency. This strategy culminates in a series of V_III complexes which tune emission wavelength from 1237 nm to 1424 nm, achieving emission into the telecom band. Chapter 4 will discuss the impact of these strategies on the magnetic properties and spin dynamics of these systems through an analysis of their behavior under high-frequency high-field EPR spectroscopy. This work provides a blueprint for the next generation of molecular spins with optical addressability in the near-infrared regime for applications in quantum networking.

Defining the Influence of Host Cell Proteostasis Networks and Temperature on Influenza Evolution

Sat, 01 Feb 2025 00:00:00 GMT

Defining the Influence of Host Cell Proteostasis Networks and Temperature on Influenza Evolution Patrick, Jessica Viruses accumulate mutations and evolve more rapidly than any domain of life. Not only does the random acquisition of mutations drive this high evolutionary rate, but constant pressure from the host also contributes. As minimalistic pathogens, viruses rely on host machineries to synthesize, fold, and degrade their proteins. These proteostasis machineries can influence the accessible sequence landscape of viral proteins, and thus shape their evolution. Furthermore, the entire viral replication cycle takes place within the host cell. Therefore, the environment of the host, including factors such as temperature, can influence the evolutionary trajectory of viral proteins. The overarching goal of my thesis work is to better understand the influence of the host cell environment, with a particular focus on the proteostasis networks and the temperature of the cell. My first project uses deep mutational scanning to elucidate the roles of the host proteostasis networks in defining influenza hemagglutinin’s evolutionary ability. My second project takes a similar approach to investigate how high or low temperature impact the accessible sequence space of HA. My third project combines both proteostasis network and temperature perturbations to investigate how the host cell environment can impact HA’s ability to escape neutralizing antibodies. My final project leverages the high mutation rate of influenza to study the phenomenon of error catastrophe, and the impact of altered proteostasis network environments on buffering the effect of mutations. Together, these studies clearly define a role for both the host proteostasis networks as well as temperature environment in determining influenza’s accessible sequence space, currently underappreciated factors in predicting how viruses evolve to evade selection pressures and a critical component to consider for successful vaccine and drug development as well as pandemic preparedness.

Co-Designing Efficient Systems and Algorithms for Sparse and Quantized Deep Learning Computing

Sat, 01 Feb 2025 00:00:00 GMT

Co-Designing Efficient Systems and Algorithms for Sparse and Quantized Deep Learning Computing Tang, Haotian Deep learning models are becoming increasingly complex, expanding from 1D text and 2D images to 3D point clouds, while their size continues to grow exponentially. This trend highlights the need for greater efficiency. This thesis systematically explores efficiency in two resource-intensive domains—autonomous driving and generative AI—by focusing on fundamental model compression techniques: sparsity and quantization, alongside the co-optimization of systems and algorithms. Sparsity is crucial for autonomous vehicle (AV) applications. LiDAR processing, which requires 3D sparse computation, is inefficiently handled by current GPU libraries, creating a performance bottleneck in AV perception. To address this, we propose TorchSparse++, a high-performance GPU system for 3D sparse convolution, achieving 1.7-3.3× speedups over state-of-the-art libraries. Additionally, we introduce BEVFusion, an efficient multi-sensor fusion framework that fuses information in bird’s-eye-view (BEV) space, reducing computation by 1.9× while enhancing accuracy compared to prior methods. Generative AI is constrained by the massive size of models, necessitating quantization for efficient deployment. This thesis presents two GPU systems for accelerating large language models (LLMs): TinyChat for edge LLM deployment and QServe for cloud-based LLM serving. TinyChat boosts edge LLM inference by 3× using activation-aware weight quantization (AWQ). QServe further improves performance with activation and KV cache quantization, enhancing the throughput of NVIDIA TensorRT-LLM by 1.2-2.4× on A100 GPUs. Finally, we introduce HART, an efficient autoregressive image generation method that achieves 4.5-7.7× higher throughput compared to diffusion models while maintaining visual quality. HART achieves this improvement by leveraging quantized, or discrete, visual tokens to capture the high-level structure of images, while a lightweight diffusion model is used for fast inference of finer details.

Building World Models with Neural Physics

Sat, 01 Feb 2025 00:00:00 GMT

Building World Models with Neural Physics Ma, Pingchuan World models learn the dynamics of environments in a data-driven manner, enhancing performance and efficiency in downstream tasks such as control, design, recognition, and generation, thanks to cost-effective simulation and differentiability. A pre-trained world model should ideally (1) accurately simulate ground-truth dynamics, (2) adapt easily to novel configurations, and (3) generalize across diverse physical effects. Previous attempts in this area have either utilized differentiable model-based physics with few parameters exposed or trained for specific scenarios with minimal physical priors integrated. These world models fall short of their objectives, limiting their applicability in real-world accuratecritic deployments and scalability to larger pre-trained world models. In this thesis, we aim to build world models with neural physics, a hybrid neural-physics framework that models the basic dynamics with differentiable physics while learning all additional modules through neural networks. By integrating neural physics, the world models adhere closely to physical principles while efficiently learning diverse effects. The modular structure of neural physics allows world models to generalize to novel configurations simply by installing different pretrained neural modules. We will demonstrate the effectiveness of this novel framework in applications such as reconstruction, robotic control, and scientific discovery.

Chemical Sensing of N-Nitrosodimethylamine and Methane

Sat, 01 Feb 2025 00:00:00 GMT

Chemical Sensing of N-Nitrosodimethylamine and Methane Feng, Haosheng In Chapter 1, an introduction to chemical sensing is presented. Several modalities are introduced, including optical, gravimetric as well as chemiresistive together with brief in-troductory backgrounds. Subsequently, metrics to assess sensor performance are sum-marized. Finally, some strategies to combat interferants during chemical sensing are dis-cussed. In Chapter 2, published work on a luminescent method to determine levels of N-nitrosamines is presented. This work involved the synthesis of five phosphines bearing N-heterocycles, followed by coordination with Cu(I) to give luminescent complexes. Emission spectra spanned the visible range, demonstrating the tuneability of these compounds. The complexes’ interactions with N-nitrosamines were also examined through spectroscopy and crystallography. In Chapter 3, development of free-volume promoting monomers and catalysts for in-sertion polymerization is demonstrated. Insertion polymerized material was compared to that synthesized using Ring Opening Metathesis Polymerization (ROMP), showing that the former had superior properties for methane detection through higher surface areas and po-rosity. In Chapter 4, the structure activity relationship of components within a previously pub-lished methane sensing assembly was thoroughly examined to identify how changes in humidity levels influenced sensing response. Poly-4-vinylpyridine modification was per-formed under flow conditions, while the chemical composition of the polyoxometalate (POM) component was also varied. Humidity was determined to most significantly affect the POM and influence the electrical contact between carbon nanotubes and gold. Finally, Chapter 5 presents several modifications of the parent porous framework out-lined reported in Chapter 3. A soluble monomer bearing adamantyl substituents was suc-cessfully synthesized by attachment of isopropyl units. Its propensity to participate in inser-tion polymerization was then examined. Sulfonation and nitration of the parent polymer I-AntN were also conducted and the product analyzed. Attempts at copolymerization of AntN with CO were also described.

Engineering Scalable Quantum Systems From First-Principles to Large-Scale Control

Sat, 01 Feb 2025 00:00:00 GMT

Engineering Scalable Quantum Systems From First-Principles to Large-Scale Control Harris, Isaac B. W. Color centers in solids are promising platforms for quantum communication, sensing, and computing, featuring highly coherent optical transitions, as well as native electron and nuclear spins that can be used as quantum memories. Existing state-of-the-art demonstrations have shown that multi-qubit control, spin-photon entanglement, and heralded entanglement are possible with devices consisting of a few color centers. However, the path to scaling the number of color centers integrated in these devices to the thousands or millions needed for advanced quantum networking and computing applications remains unclear. In particular, the requirement for highly coherent quantum operations both necessitates operation at cryogenic temperatures, and precise classical control signals delivered to each color center. Precise qubit control greatly increases the system complexity, while the cryogenic operation limits the amount of power that the system can dissipate. Both factors severely limit the number of color centers that can realistically be included in a single device using existing methods. This work will tackle the scaling problem from a system-level perspective from two directions. Firstly, I will quantify performance trade-offs between coherence, temperature, and optical properties of the group-IV color centers. A novel color center system, the ¹¹⁷SnV⁻ hyperfine color center, will be presented and its advantages compared to traditional group-IV color centers will be explored. Secondly, a method to integrate color centers with application specific integrated circuit (ASICs) will be demonstrated. The ASICs provides multiplexed control signals and increased control field efficiency, thus decreasing both the wiring complexity and thermal load per qubit. This work will thus pave the way to color center-based devices in which the number of qubits is not limited by the complexity or power dissipation of the control system.

Information-centric Algorithms for Feature Extraction in High-Dimensional Sequential Data

Sat, 01 Feb 2025 00:00:00 GMT

Information-centric Algorithms for Feature Extraction in High-Dimensional Sequential Data Jin, Jiejun Hidden Markov Models (HMMs) are a cornerstone of sequential data analysis, offering a robust framework for modeling observable events influenced by hidden internal states. With applications spanning speech recognition, video analysis, bioinformatics, and financial time series, HMMs enable the prediction and classification of raw data by leveraging their dual-layer stochastic structure: hidden Markov states and observable outputs. However, as real-world data grows increasingly high-dimensional, extracting meaningful features from observations becomes critical to reduce computational complexity while retaining relevant information. This thesis addresses key challenges in feature extraction for high-dimensional HMMs. Current methods, such as neural networks (NNs), are widely used for nonlinear feature learning but lack mechanisms to prioritize useful features or incorporate known structural constraints. To bridge this gap, this work proposes novel algorithms to decouple representation learning from task-specific objectives and extract features aligned with predefined constraints. The theoretical foundation, including local information geometry and Hirschfeld-Gebelein-Rényi (HGR) maximal correlation, is introduced in Chapter 2. Chapter 3 details three innovative feature extraction algorithms and their corresponding neural network architectures, highlighting their strengths and limitations. Convergence analyses and tail bounds for these methods are presented in Chapter 4. Numerical simulations validating the efficacy of the proposed approaches are provided in Chapter 5, while Chapter 6 concludes with a summary of contributions and potential future research directions. This thesis advances the field by offering structured, constraint-aware feature extraction techniques tailored for high-dimensional sequential data, setting the stage for more effective and interpretable inference in HMMs.

Accelerating Distributed Deep Neural Network Training and Fine-Tuning Through Resource Interleaving

Sat, 01 Feb 2025 00:00:00 GMT

Accelerating Distributed Deep Neural Network Training and Fine-Tuning Through Resource Interleaving Rajasekaran, Sudarsanan The ever-growing increase in dataset and model sizes of deep learning has created a massive demand for efficient GPU clusters. As the number of GPUs increases, the communication overhead of distributed Machine Learning (ML) training and fine-tuning workloads quickly takes up a significant portion of iteration time. Yet state-of-the-art ML schedulers tend to ignore the communication pattern of ML jobs when placing workers on GPUs. This thesis advocates for communication-aware resource scheduling as a critical approach to optimizing network utilization in ML clusters. We introduce a key idea for accelerating Deep Neural Network (DNN) jobs that interleaves the communication demands of different jobs sharing a network link. To illustrate this concept of interleaving, we first demonstrate how intentionally creating unfairness in bandwidth share between different DNN jobs improves their iteration times. Building on this insight, we present two novel systems designed to minimize network congestion and accelerate DNN training and fine-tuning jobs. The first system, Cassini, achieves interleaving using a centralized approach. In contrast, the second system, MLTCP, achieves the same goal using a distributed approach. Both systems are practical and readily deployable, depending on the service provider’s preference on deploying centralized or distributed solutions. In particular, Cassini, is a centralized network-aware job scheduler for ML clusters. Cassini introduces a novel geometric abstraction to consider the communication pattern of different jobs while placing them on network links. To do so, Cassini uses an Affinity graph that finds a series of time-shift values to adjust the communication phases of a subset of jobs such that the communication patterns of jobs sharing the same network link are interleaved with each other. Second is MLTCP, a distributed technique to approximate an interleaved centralized flow schedule. At the heart of MLTCP lies a straight-forward principle based on a key conceptual insight: by scaling the congestion window size (or sending rate) based on the number of bytes sent at each iteration, MLTCP flows eventually converge into a schedule that reduces network contention. To evaluate these systems, we conduct experiments using real-world DNN models on a testbed with Nvidia A100 GPUS. Cassini and MLTCP improve the average iteration times by up to 1.6× and 1.9×, respectively, demonstrating their effectiveness in reducing network congestion and accelerating ML workloads.

Robot Fleet Learning From Heterogeneous Data

Sat, 01 Feb 2025 00:00:00 GMT

Robot Fleet Learning From Heterogeneous Data Wang, Lirui One of the key roadblocks for training generalist robotic models today is heterogeneity. Previous robot learning methods often collect data to train with one specific embodiment for one task, which is expensive and prone to overfitting. Similar to humans, robots and embodied agents inherently have to deal with heterogeneous inputs and outputs due to the nature of the perception-action loops across diverse environments. The data format and distributions collected from these systems and used for training them are varied in different modalities such as color, depth, tactile, and proprioceptive information, and/or collected in different domains such as simulation, real robots, and human videos. Moreover, fleets of robots and machines ingest massive amounts of streaming data generated by interacting with their environments in a distributed fashion, and teams of robots shall co-acquire diverse skills through their experiences in varied settings. The core idea behind my research, fleet learning, is to embrace the heterogeneous nature of robot learning to develop efficient and general algorithms. In this thesis, I will present a few angles toward tackling such challenging problems and application domains, ranging from tokenizing data, aligning representations, and merging policies, to composing skills. We develop insights and theories, often from linear settings, for how fleet learning can lead to more principled and effective use of robotic data and propose algorithmic progress, often through alignments, toward building generalist robotic foundation models. Empirically, we show advanced robotic manipulation capabilities by leveraging data from multimodal sensory inputs and multiple domains. In addition to outperforming several previous state-of-the-art across simulation and real-world benchmarks, we develop intelligent systems for robotic applications such as package handling in warehouses as well as dexterous tool-use tasks that have applications such as manufacturing, logistics, and household robots.

Application of foundation models for molecular representation in cancer drug discovery and precision oncology

Sat, 01 Feb 2025 00:00:00 GMT

Application of foundation models for molecular representation in cancer drug discovery and precision oncology Khokhlov, Khrystofor Drug discovery is a resource-intensive and time-consuming process, often requiring decades of effort and substantial financial investment, with a high risk of failure. Despite advances in high-throughput screening technologies, the size of chemical space presents a significant challenge: it is not feasible to experimentally screen all potential drug-like molecules. Most commercially available chemical libraries consist of molecules that are synthesized on demand from pre-existing building blocks, further limiting the exploration of novel chemotypes. This thesis aims to explore whether drug discovery could be accelerated by leveraging advances in deep learning (DL) models to identify promising hit candidates and improve the prediction of drug response in cancer. Development of cancer drugs that will be effective on a predictable set of targets remains a major challenge. We are developing a DL model capable of identifying potentially novel cancer drug chemotypes and reliably predicting drug response on cancer cell line targets. Leveraging recent progress in transformer-based architectures and graph neural networks, we use molecular language models, graph models and cell foundation models to embed both molecular and genomic data into low-dimensional subspaces and then use standard machine learning (ML) tools in these low-dimensional spaces to predict the efficacy of the molecules in particular cell lines. We utilize the large-scale drug repurposing and oncology datasets from the PRISM project at the Broad Institute, which provide a wealth of drug repurposing and oncology data, enabling robust training of ML models. We show that these vector embeddings are superior to existing methods, as they enable more accurate drug response predictions. The first part of this thesis is dedicated to development of a deep learning cancer drug discovery model, focused on in silico screening of chemical space to search for cancer drug candidates. The second part is focused on development of a precision oncology model, based on a multichannel neural network architecture. Our pipeline involves training single-target models on drug molecular structures, followed by integrating genomic data to enhance biological context and train a hybrid model capable of predicting drug response for novel drug:target pairs. Our results demonstrate that vector embeddings produced by the proposed framework outperform existing approaches, offering a more accurate and efficient means of exploring chemical space. This work highlights the transformative potential of ML/DL methods in drug discovery, enabling targeted, cost-effective exploration of chemical libraries, and advancing the development of precision oncology treatments.

Thermally Hardened RF GaN HEMTs in Extreme Environments

Sat, 01 Feb 2025 00:00:00 GMT

Thermally Hardened RF GaN HEMTs in Extreme Environments Niroula, John Traditional, room temperature electronics based on silicon has truly changed the world around us over the past 70+ years. However, many more applications still exist that are limited by the temperature performance of silicon devices (<250◦C). This area of high temperature (HT) electronics is an increasingly growing field with critical future applications in geothermal energy, space exploration, hypersonic aircraft, and deep gas/oil drilling, among others. Gallium Nitride (GaN) high electron mobility transistors (HEMTs) are especially well suited for high temperature electronic applications due to their low intrinsic carrier concentration and excellent electrical properties. Despite great progress in HT GaN technology, most demonstrations target logic or mixed-signal applications, and the performance of radio-frequency (RF) GaN devices remains lacking at high temperatures despite the critical need for wireless communication systems and high-speed electronics for these high-temperature applications. In this thesis, we investigate the physics of GaN HEMT devices at high temperatures and design RF transistors that demonstrate record performance at these temperatures.

Data-Rich Personalized Causal Inference

Sat, 01 Feb 2025 00:00:00 GMT

Data-Rich Personalized Causal Inference Shah, Abhin . There is a growing interest in individual-level causal questions to enable personalized decision-making. For example, what happens to a particular patient’s health if we prescribe a drug to them, or what happens to a particular consumer’s behavior if we recommend a product to them? Conducting large-scale randomized experiments to answer such questions is impractical—if not infeasible—due to cost, the level of personalization, or ethical concerns. Observational data offer a valuable alternative, but their lack of explicit randomization makes statistical analysis particularly challenging. In this thesis, we exploit the richness of modern observational data to develop methods for personalized causal inference. In the first part, we introduce a framework for causal inference using exponential family modeling. In particular, we reduce answering causal questions to learning exponential family from one sample. En route, we introduce a computationally tractable alternative to maximum likelihood estimation for learning exponential family. In the second part, we leverage ideas from doubly robust estimation to enable causal inference with black-box matrix completion under a latent factor model.

Coarse Modality

Sat, 01 Feb 2025 00:00:00 GMT

Coarse Modality Flor, Enrico One of the early successes of the application of possible worlds semantics to the analysis of natural language is Kratzer’s account of modality. A large part of the subsequent literature on modals has sought to expand the crosslinguistic coverage of that framework, and, in so doing, many new generalizations and constraints have been proposed and re-examined. The present dissertation situates itself within this tradition and makes both an empirical and theoretical contribution. Using the Italian adverb magari as the main empirical source, it will be argued that there exists a previously unnoticed type of modality which is referred to here as “coarse”. Its most evident manifestation is a special type of epistemic possibility, one that comes with an “antievidential” requirement. Antievidential possibility in assertions and questions is discussed in Chapters 1 and 3 respectively. Chapter 2 frames coarse modality as a more general phenomenon that comes about through modification of modal expressions. The theoretical argument of this dissertation is a novel corroboration of Kratzer’s premise semantics approach. It will be argued that the most natural and general account of coarse modality is possible by utilizing the premise set, a powerful resource of the system, in a novel way.

Data Interpretation and Management for an Atmospheric Probe Mission to Venus

Sat, 01 Feb 2025 00:00:00 GMT

Data Interpretation and Management for an Atmospheric Probe Mission to Venus Apodaca Moreno, Maria Regina After nearly 40 years without a dedicated U.S. mission to Venus, the Rocket Lab Mission to Venus is planning to launch a small probe to analyze the composition of Venus’ cloud layers. As the probe descends through the atmosphere, it will spend around five minutes in the cloud deck, from 66 km to 48 km above the surface, and roughly 20 minutes total in the atmosphere [French et al., 2022]. The probe’s primary scientific instrument, the Autofluorescence Nephelometer (AFN), will gather data by measuring the light scattering off particles, providing insight into their chemical composition based on refractive index and particle size [Baumgardner et al., 2022]. Unfortunately, the natural phenomena described by Mie scattering [Mie, 1908], the physics theory underpinning the AFN, holds that light scattering for a small solid angle is fundamentally degenerate: different combinations of refractive index and particle size can lead to identical light scattering. This degeneracy limits scientists’ ability to uniquely determine physical parameters of interest, leading some previous authors to rely upon helpful, but perhaps limiting, assumptions that mitigate this degeneracy. Complicating matters still further, the probe’s communication with Earth is subject to a strict data budget, limiting the amount of AFN measurements that may be used for analysis to begin with. This thesis addresses two important problems associated with the Rocket Lab Mission to Venus: 1) how to mitigate the light scattering degeneracy with minimal assumptions and 2) how to transmit valuable information within the limited data budget. To address the first problem, I introduce a data retrieval algorithm, based upon Bayesian statistical inference [Lindley, 1965], which combines a physical model of the instrument and a prior probability distribution describing each physical property. In some cases, this method can estimate the correct particle size and refractive index of a particle as the maximum likelihood value, from a single measurement even as it relaxes certain assumptions that were previously standard in the field, such as a small refractive index range. Using my data retrieval algorithm, I reanalyze the data collected by the Pioneer MultiProbe Mission to Venus’ nephelometers without the need for supplementary data from a different instrument [Ragent and Blamont, 1980]. I also provide new insight into the particle size and refractive index distributions seen by the Pioneer Mission’s small probes, which had not been possible with previous techniques. To address the second problem, I propose a data strategy for limited data missions like the Rocket Lab Mission to Venus. The method developed in this work relies upon Gaussian Mixture Models, which can efficiently represent multiple measurements as

Investigating the Atmospheric and Oceanic Drivers of Atlantic Multidecadal Variability and Predictability

Sat, 01 Feb 2025 00:00:00 GMT

Investigating the Atmospheric and Oceanic Drivers of Atlantic Multidecadal Variability and Predictability Liu, Glenn Yu-zu Despite its numerous impacts across the Earth system, the relative importance of ocean and atmospheric dynamics in generating Atlantic Multidecadal Variability (AMV) remains an open question. This thesis presents three pathways to understanding how oceanic and atmospheric processes generate key spatio-temporal signatures of AMV through a combination of processed-based and data-driven approaches. Part 1 (Chapter 2) takes a "bottom up" approach, building a hierarchy of stochastic models to identify the contributions of vertical entrainment and seasonality in local upper-ocean processes to sea surface temperature (SST) variability. Through this hierarchy, I highlight unrealistic features present in slab ocean models widely used to isolate atmospheric contributions to AMV. On the opposite end of the spectrum, Part 2 (Chapter 3) utilizes a "top-down" data-driven approach where deep neural networks are trained to predict the North Atlantic SST Index in both the Community Earth System Model 1 Large Ensemble (CESM1) and observation-based datasets using atmospheric and oceanic predictors. I apply explainable artificial intelligence techniques to highlight a significant source of multidecadal predictability over the Transition Zone in oceanic predictors such as sea surface salinity (SSS) and sea surface height in the presence of external forcings. Part 3 (Chapter 4) returns to the process-based hierarchy, but applies this to understanding SSS variability. The stochastic salinity model is used to investigate the role of mixed-layer re-emergence, subsurface ocean damping and SST-evaporation feedback in shaping the pattern and amplitude of AMV.

Leveraging Information Sharing for Satellite Navigation and Coordination

Sat, 01 Feb 2025 00:00:00 GMT

Leveraging Information Sharing for Satellite Navigation and Coordination Dolan, Sydney As the number of objects in orbit grows, so does the risk of collisions. The sheer volume of collision warning messages far exceeds the capacity of human analysts, placing a significant burden on satellite operators and underscoring the need for autonomous, decentralized traffic management. Unlike centralized conjunction analysis, decentralized space traffic management distributes coordination across multiple independent nodes, allowing satellites to collaborate directly. This approach could enhance the resilience, speed, and international cooperation of space operations, helping to manage the space environment. For decentralized space traffic management to be viable, satellites must possess an accurate understanding of both the locations and intentions of other satellites. While satellites have precise knowledge of their own state, this accuracy diminishes when predicting the state of others. This gap is due to the limitations of onboard measurement systems and knowledge of each satellite’s structure, configuration, and maneuverability. Such differences motivate the exploration of information sharing between operators to improve coordination. Sharing information could benefit both individual operators and the broader space community by enabling more accurate trajectory predictions, facilitating formal maneuver negotiations, and enhancing overall orbital safety and efficiency. The main contribution of this thesis is to develop methods for autonomous satellite decision-making. By advancing the state of satellite autonomy, we can enhance high-level decision-making processes, enabling more adaptive and intelligent satellite coordination. This thesis begins by developing a multi-agent reinforcement learning environment to simulate satellite interactions in complex, high-dimensional settings. Then, we relax the assumption on synchronous communications and explore an alternate learning framework that relies on asynchronous communication between satellites. Our final contribution lies in a game-theoretic model of operator behavior in non-cooperative settings. Space is a competitive environment, and willingness to collaborate is mixed. As a result, we use game theory to obtain strategies to determine maneuvering and timing.

Max-Stable Processes, Measure Transport & Conditional Sampling

Sat, 01 Feb 2025 00:00:00 GMT

Max-Stable Processes, Measure Transport & Conditional Sampling Konomis, Dimitris C. The modeling of extremes, known as extreme value theory (EVT), aims to understand events characterized by extreme deviations from the mean of a probability distribution. These events are significant in fields such as finance, environmental science, engineering, and insurance. EVT aims to predict the occurrence and impact of these events, which often have severe consequences. Applications of EVT include modeling extreme market movements in finance, natural disasters in environmental sciences, structural reliability in engineering, and catastrophic event risk management in insurance. Conditional sampling and simulation methods, such as normalizing flows and measure transport, are crucial for estimating extremes at un-monitored sites or under specific conditions, thereby improving our understanding and risk management strategies. The goal of this thesis is to make significant contributions to both extreme value theory and measure transport, as well as to establish a link between the two. First, we develop new Markov chain Monte Carlo algorithms for conditional sampling of max-stable processes. Next, we create models that incorporate physical laws, encoded by partial differential equations, to extend max-stable processes into regions without observations. Third, we design specialized transport map frameworks for distributions with bounded support, enabling accurate and efficient sampling and inference. Finally, we use transport maps parameterized by neural networks to learn and condition the distributions of shortest path statistics in polymer systems, accelerating the prediction of microstructural evolution under various conditions.

Numerical investigations of vortex dynamics: bursting, twist waves, and sensitivity analysis

Sat, 01 Feb 2025 00:00:00 GMT

Numerical investigations of vortex dynamics: bursting, twist waves, and sensitivity analysis Ji, Lingbo Vortical structures are ubiquitous in real-world fluid flows, from the vortices generated by swimming fish to the wakes of aircraft and propellers. They form the backbone of high Reynolds number turbulent flows. Their dynamics are governed by non-linear processes, leading to a range of vortical instabilities that significantly influence engineering applications. Despite decades of research, many questions remain about core mechanisms responsible for the dynamic evolution of vortical structures due to the nonlinearity and complexity of flows at high Reynolds numbers. A particular scenario that lacks systematic investigation is vortices with initial core-size variations, which leads to the phenomena of twist wave propagation and vortex bursting. In this thesis, we first examine straight vortex tubes with initial core-size perturbations at high Reynolds numbers by performing high-fidelity numerical simulations. The differential rotation along the vortex tubes generates twist wave packets that propagate and collide, resulting in a sudden increase in the local core size – the phenomenon of bursting. We analyze the effects of perturbation amplitudes on the detailed evolution at each stage, including the underlying mechanisms for the growth and decay of the bursting structure. The bursting process is associated with significant energy dissipation, which is quantified and compared to that of unperturbed vortex tubes. Meanwhile, vortices in real fluid flows are often nonrectilinear and experience strain from environmental or self-induced effects. We extend our study to curved vortex tubes and investigate the impact of centerline non-rectilinearity on twist wave propagation and the stability of the bursting structure. Additionally, we adopt a relatively recent geometric perspective on vortical flows and analyze the helicity dynamics during the flow evolution. To systematically initialize vortex dynamics simulations based on a late-time or time-averaged flow metric, we explore different methods for sensitivity analysis of two-dimensional vortical flows. The sensitivity values obtained are then used in gradient-based optimizations, which shows promising pathways for control and optimization of vortical flow applications. Additionally, we present a numerical study of the locomotion of a rotating cylinder pair with periodic gaits in a low Reynolds number flow. We characterize the motion pattern and efficiency of the cylinder pair through a combination of theoretical arguments and numerical simulations, which provides a foundation for potential engineering applications at the microscale. Overall, our findings provide understanding of fundamental mechanisms for vortex bursting and associated twist wave dynamics at high Reynolds numbers, explorations of sensitivity analysis for vortical flow applications, along with insights into locomotion at low Reynolds numbers.

On the nature and measurement of variational bias: a developmental perspective

Sat, 01 Feb 2025 00:00:00 GMT

On the nature and measurement of variational bias: a developmental perspective Cai, Haoran Natural selection cannot work with imaginary phenotypes, only those realized by developmental systems. The observed diversity of life on Earth occupies only a subset of conceivable forms in the absence of selection. This is because of the non-linear and discrete nature of genotype-to-phenotype maps as an outcome of the developmental system. Despite that, it is widely accepted in population and quantitative genetic modelings that the phenotypic production from random mutations is isotropic and uniform. Conventional methods linking genetic variants and phenotypic variation often assume that the origin of phenotypic variation is purely due to genetic and environmental factors. Here, in this thesis, I adopt a developmental causation view which proposes that patterns of variation may emerge as an inherent consequence guided by physico-chemical principles and that part of the nature can not be fully reducible to genetic factors. The distribution of phenotypic variants that arise from genetic and environmental variation is influenced by the developmental processes that transform the embryonic phenotype into the adult form. This developmental process is subject to constraints that stem from the structure, character, composition, or dynamics of development. We term such a constraint as developmental bias. Despite the prevalence of developmental bias, detecting and testing its role remains a challenge. To address this gap, in the thesis, I propose frameworks and showcase examples aimed at identifying developmental bias and testing its implications in shaping phenotypic evolution. Specifically, I answer three questions: (1) How does the central conponent of nonlinear genotype-to-phentype map --- transcriptional regulation --- bias the analyses of gene-gene interactions? (2) How to disentangle the contribution of developmental bias in trait-trait interdependencies? (3) How expression variability affects gene retention and gene expression evolution following gene and genome duplication.

A Systems-Theoretic Approach to Design of Early Concepts for Novel, Complex Systems in Aerospace

Sat, 01 Feb 2025 00:00:00 GMT

A Systems-Theoretic Approach to Design of Early Concepts for Novel, Complex Systems in Aerospace Hillman, Alexander P. The complexity of engineered systems has grown exponentially over the last forty years. One of the main challenges in modern engineering is managing this complexity, particularly as the pace of technological change continues to accelerate across industries. Traditional approaches to generating early concepts for novel, aerospace control-oriented systems typically employ a design-first approach, ignoring critical steps required to truly understand the intent and context of a new system. This tendency also leads to a focus on low-level, highly granular design activities that seek to integrate advanced technologies together for technology’s sake. Unfortunately, today’s applied early concept generation methods do not facilitate the effective generation of early system concepts and an initial high-level design for aerospace control systems. To address these shortcomings, this thesis proposes a systematic and rigorous framework to generate early system concepts using Systems-Theoretic Accident Model and Processes (STAMP) principles and a new lens to examine system intent for a novel, complex system. This work also introduces a new level of abstraction for a portfolio-of-systems context and a method to propose an initial design artifact for new systems that is both architecture-agnostic and relevant during the earliest system engineering lifecycle activities. This method, Systems-Theoretic Concept Design, uses a top-down, three-phased approach to conduct mission analysis and determine the intent for a new system within a specific portfolio-level context. Upon building this intent model, the method enables the synchronization of stakeholder mental models through the use of transformation models built using the principles of Systems Theory. Finally, in its last phase, this early design concept generation framework delivers an initial design artifact that is technology- and requirements-agnostic in the language of Systems Theory using the semantics of STAMP. This initial design artifact is in the form of the Portfolio-of-Systems control structure, a control structure that frames a portfolio’s desired high-level capability as a control problem at a new level of abstraction while enabling analysis and examination of complex interactions across systems that may operate asynchronously or in geographically separated operating environments.

Computational-Experimental Process Development for Laser Powder Bed Fusion Additive Manufacturing

Sat, 01 Feb 2025 00:00:00 GMT

Computational-Experimental Process Development for Laser Powder Bed Fusion Additive Manufacturing Weißbach, Reimar Laser powder bed fusion (LPBF) additive manufacturing (AM) is instrumental for advances in high-value industries such as aerospace and medical devices. However, widespread adoption is still held back, in part due to challenges with powder handling, identification of process parameters, part qualification and quality control, and low build rates that lead to high part costs. This thesis presents workflows, tools, and understanding for practitioners and researchers seeking to address these challenges, in particular (i) powder spreading, (ii) parameter selection, and (iii) build rate improvement. Cohesive powders (D50 ≤ 20 𝜇𝑚) are challenging to spread and therefore not commonly used in LPBF, but promise more stable melting conditions during laser melting and potentially allow for finer geometrical resolution. Various spreading strategies are explored using an integrated discrete element-finite element (DEM-FEM) framework and a schematic process window for counter-rotational roller spreading is proposed. A new strategy of spreading with a transversely oscillating tool is chosen for experimental implementation and validated using a custom-built mechanized powder spreading testbed. Powder layers are analyzed using X-ray transmission imaging and layer quality is statistically correlated to kinematic spreading parameters. A methodology for performing melt track experiments using high-precision metal templates as well as a machine learning-based automated image analysis tool is presented and applied to melt track scaling studies. Based on single track parameter studies with layer thicknesses and laser spot sizes of up to 600 𝜇𝑚, a dimensionless LPBF process window using the normalized enthalpy Δ𝐻 / ℎₛ as well as the Fourier number is developed. A workflow for rapid LPBF build parameter selection is proposed, that is shown to fabricate near-full dense parts (up to 99.99 %) on the first attempt. Build rate scaling analysis reveals the trade-off between laser spot size and laser scan speed given laser power limitations. Further, LPBF with a standard powder (15 − 45 𝜇𝑚) is compared to a fine powder (0 − 25 𝜇𝑚) under similar processing conditions. The fine powder exhibits superior melt track stability and continuity, as well as significantly increased melt track cross-sectional area, allowing build rate to be increased by almost 20 %. Finally, to enable better understanding of the underlying thermo-fluid dynamics of the melt pool, an approach for computational model parameter estimation using Bayesian inference is presented and applied to the important model parameter of laser absorptivity. This is within the context of a Smooth Particle Hydrodynamics (SPH) computational melt pool model, developed collaboratively by researchers at the Technical University of Munich. The diffuse interface approach employed in SPH is validated using a discretization refinement study, showing the sensitivity of physical phenomena characteristic for LPBF, such as the vapor-induced recoil pressure, to computational hyper-parameters. These combined contributions enhance both practical implementation and theoretical understanding of LPBF, ultimately advancing the field of additive manufacturing towards more cost-effective and higher quality LPBF processes.

A Computational Thermo-Chemo-Mechanics Framework for the Large-Scale Simulation of Material and Structural Failure in Hypersonic Environments

Sat, 01 Feb 2025 00:00:00 GMT

A Computational Thermo-Chemo-Mechanics Framework for the Large-Scale Simulation of Material and Structural Failure in Hypersonic Environments Pickard, Daniel N. Materials and structures subjected to the extreme conditions of hypersonic flight undergo complex degradation and fracture processes. This thesis presents a theoretical formulation and a computational framework that enables the large-scale simulation of thermochemically fracturing solids exhibiting complex post-fracture interface response. The continuum theory is based on a general thermodynamically-consistent description of the coupled multiphysics problem, and the numerical formulation extends the scalable discontinuous Galerkin(DG)/Cohesive Zone Modeling paradigm to thermo-chemo-fracture mechanics. The approach is distinguished by its unified DG treatment of the coupled problems, which facilitates the analysis of fracture propagation, fracture-dependent heat and mass transfer as well as thermally-activated solid-phase chemical reactions. The framework is verified against two analytical solutions of boundary value problems drawn from thermo-poro-elasticity and thermally-driven delamination. Three-dimensional simulations of a benchmark thermochemically-driven fracture problem illustrate the parallel scalability of the fully-coupled computational framework. We utilize this framework to render models of passive oxidation-induced fracture in ultrahigh temperature ceramics computationally tractable. First, a rigorous constitutive theory is shown to capture the molecular diffusion of oxidant through the reaction product layer using only fundamental transport properties, i.e. without the need for calibration to reaction experiments. The physical processes observed on the diminutive scale of an oxide layer are explicitly resolved, but the approach is limited to microscale analyses by scale separation. We sidestep this limitation by specializing the general theory under specific phenomenological assumptions, thereby yielding a practical model that can reproduce oxidation experiments. We use this specialized model to analyze oxidation-induced swelling, fracture and delamination in SiC/coating systems, and unveil the coupled thermochemical response as well as fracture morphologies in the vicinity of critical flaws. Then, we conduct a parametric study of three-dimensional coatings that exposes the channeling mechanisms above penny-shaped delaminations of various sizes. The computational analyses identify a transition from decussating to circumferential channel cracking that explains the wide variety of surface channel cracks observed in experiment. The physical mechanisms and fracture morphology regimes are corroborated by a simple structural theory. Finally, cohesive fracture models, splitting methods and thermal solvers are developed specifically for applications to thermally shocked ceramics. Simple and rigorous calibration procedures are proposed which facilitate the direct analysis of fragmentation and comminution in brittle solids subjected to extreme advective heat transfer. The presented examples serve as evidence that the framework can successfully enable three-dimensional, thermochemically-coupled fracture analyses of unprecedented physical fidelity, which furnish new insights into complex hypersonic thermal protection system response.

On the dynamics and interparticle forces of electrostatically stabilized colloidal suspensions

Sat, 01 Feb 2025 00:00:00 GMT

On the dynamics and interparticle forces of electrostatically stabilized colloidal suspensions Krucker-Velasquez, Emily In a broad spectrum of industrial and biomedical applications, the equilibrium and dynamic properties of colloidal suspensions play a pivotal role, with systems ranging from simple gold nanoparticles in electrolyte solutions to complex assemblies like micelles, vesicles, nanocapsules, and dendritic polymers. Typically, these systems are approached through the Derjaguin–Landau–Verwey–Overbeek (DLVO) theory and Poisson-Boltzmann models, frameworks that approximate charged particles as point charges to predict interparticle interactions. While these frameworks have been instrumental for low-concentration, idealized systems, it falls short in capturing critical behaviors in more concentrated regimes. In such environments, overlooked phenomena—such as excluded volume effects and ion-ion correlations—become essential in shaping the colloidal system’s equilibrium and dynamics. By leveraging advanced computational techniques, we systematically interrogate these mesoscale interactions, offering insights that extend beyond the traditional paradigms of mean-field theory and enhance our understanding of colloidal behavior in complex environments. The first part of this work presents the development of efficient algorithms that significantly advance the computational speed of induced polarization calculations within Brownian Dynamics simulations of polarizable colloidal particles. By establishing a new benchmark in simulation methodologies, these algorithms lay the groundwork for exploring complex soft matter systems, enabling deeper insights into the dynamic and equilibrium properties of colloidal suspensions beyond the limitations of conventional theories. Together, these advancements provide a robust computational framework for examining mesoscale interactions in concentrated colloidal systems, where ion correlations, finite ion volumes, and thermal fluctuations critically influence behavior. The next part of this work focuses on the study of equilibrium properties of charged soft matter systems in crowded environments through the implementation of robust computational techniques. We meticulously examine charge-density correlations and clustering behaviors that arise due to the complex electrostatic interactions between colloidal particles. At high ion concentrations, the system undergoes distinct structural transitions that are modulated by the ionic strength and specific particle characteristics. These transitions are characterized by emergent patterns in the spatial distribution of charges, forming structured clusters that reflect the balance between electrostatic and entropic forces. We further our studies by computing the potential of mean force (PMF) between metallic nanoparticles, a measure of the effective interaction potential that inherently captures how particles interact across various separation distances in an electrolyte. The PMF analysis reveals oscillatory behavior in particle interactions at different concentrations. Our study delivers robust free energy profiles, enabling a more nuanced understanding of the electrostatic forces at play in dense colloidal suspensions. These insights shed light on the mechanisms of charge screening and packing within high-density systems. The final part of this thesis focuses on the study of the non-linear transport properties of concentrated macroions to external electric fields, revealing intricate dependencies on both ionic structure and external electric fields. Our studies reveal how conductivity is modulated by charge density correlations and field strength. A notable disruption of local ionic atmospheres was observed with increasing field strengths, which in turn accelerates ion mobility and significantly alters the transport properties. We further advance the investigation into the dynamic response of concentrated macroions and electrolytes by examining their behavior under time-varying electric fields. Through simulations involving frequency sweeps and chirp signals, we discerned that the dynamic response of these concentrated charged soft matter systems is best understood through the lens of two distinct transport regimes—characterized by short- and long-time responses. This bifurcation enables the introduction of a relaxation time scale that captures the intricate coupling between ionic correlations and the macroscopic system response, highlighting the pivotal role of excluded volume effects in densely populated environments. The study provides a detailed framework for manipulating ion transport in concentrated electrolytes and macroions, paving the way for innovations in fields reliant on precise control of electrostatic conditions and ionic mobility.

Detection and Localization of Pressure Transients in Water Distribution Systems

Sat, 01 Feb 2025 00:00:00 GMT

Detection and Localization of Pressure Transients in Water Distribution Systems Liu, Shiqing Water distribution systems are critical to urban water supply, but as they age they become increasingly vulnerable to bursts and leaks, leading to significant economic, social, and environmental consequences. The complexity and inaccessibility of underground pipelines present substantial challenges for their maintenance. As a result, the development of real-time monitoring systems for these systems is essential to reduce water waste and minimize adverse impacts to consumers and surrounding infrastructures. This thesis investigates the effectiveness of continuous pressure monitoring systems in detecting pipe bursts and transient events within water distribution systems. Using PTSNet, a parallel transient simulation Python package, we simulate pipe burst events at each node in a real-world system and examine the pressure-time response at all other nodes. By adding Gaussian noise to the simulation results to mimic real-world background noise, we assess the detection success of pressure signals at each node using a modified CUSUM algorithm. The correlations between detection success and three spatial metrics between the source and sensor are calculated. We show that a spatial metric, the effective number of magnitude-changing junctions along the fastest path, (NJFP), has a stronger correlation with detection success than the shortest travel path or the shortest distance. By comparing detection performance for networks with differing topologies (gridded, looped, and branched) and pipe characteristics, we discover that multiple shortest paths (MSP; where pressure waves from different paths arrive almost simultaneously at the sensor) amplify the signal due to transient interference phenomenon and enhance the detectability of transients. This effect is particularly pronounced in gridded networks. We investigate the capabilities of monitoring, from a network of fixed stations, to achieve unique localization of pressure transient events using a time-reversal back-propagation algorithm. This algorithm identifies the event source by matching the theoretical and detected arrival time differences at the sensors. A novel time differences space is constructed, representing the independent shortest time differences from locations along all the pipes to the sensors, based on network information and sensor locations. Pipe sections with unique shortest time differences are identified as uniquely localizable pipes. Effective-NJFP-based probabilities of transient detection with accurate arrival times (error < 0.1s) are derived from these simulation results. The localization performance of the sensor network is evaluated by the probability-weighted total lengths of the pipes that can be uniquely localized. We consider sensor placement strategies aimed at maximizing the detection and localization performance of pressure monitoring sensor networks. Detection performance is defined as the total weighted pipe lengths in the network, where the weight of each pipe corresponds to its detection probability. Two problems are addressed: In order to maximize transient event detection performance when only a limited number of sensors are available, we formulate a mixed-integer programming (MIP) optimization model and employ a genetic algorithm to find solutions. The second problem involves determining the minimum number of sensors and their optimal locations to detect transient events across the entire network without a constraint on the number of available sensors. This is formulated as a minimum set cover problem, and an optimal solution is obtained using a mixed-integer linear programming solver. We focus on maximizing transient localization performance with a limited number of sensors. A genetic algorithm is applied to determine sensor locations, and the solutions obtained by this method provide significantly better localization performance than other approaches. We show differences in sensor placements for detection and localization: sensors are more evenly distributed throughout the network for detection purposes, while for localization, they are more concentrated in areas with longer pipes and simpler network structures. Finally, we present an analysis of two pressure monitoring datasets collected from a real-world water distribution system (SLG network). The first dataset consists of data from 28 sensors with a 100 Hz sampling frequency, collected over 7 to 30 days. We propose a method to identify and analyze noise levels and distributions at each sensor. Using a modified CUSUM algorithm, we detect transients and correlate them across sensors to identify events detected by multiple sensors. A transient-magnitude-based clustering method is then employed to group events based on their magnitudes, followed by a localization approach that utilizes the arrival time differences of transients between sensors. The findings indicate that noise levels in real-world monitoring data vary both spatially and temporally and are not independently normally distributed. Additionally, the arrival times detected by the modified CUSUM algorithm may not always accurately reflect the true transient arrival times due to mismatches between the signal characteristics and tuning of model parameters. Accurately identification of transient arrival time is particularly challenging for slowly changing pressure wave fronts. The second dataset includes pressure monitoring data from 7 sensors, during which 14 active transients with known source locations, times, and magnitudes were generated. We apply the modified CUSUM algorithm to detect transients at the sensors and correlate detection success with spatial metrics. The analysis confirms that the effective NJFP has the highest correlation with detection success, consistent with the simulation results. Additionally, the transient magnitude ratios between sensors and the source are found to be similar to the ratios calculated based on theoretical transmission coefficients when the source and sensor are in close proximity, suggesting that transmission coefficients can be used to estimate transient magnitudes in real networks.

Observations of Surfzone Vorticity Using Optical Remote Sensing

Sat, 01 Feb 2025 00:00:00 GMT

Observations of Surfzone Vorticity Using Optical Remote Sensing Dooley, Ciara Jaya The surfzone is the dynamic interface between the land and ocean, where waves shoal and break as they reach shallow water near the shore. Currents and circulation patterns in the surfzone transport sediment, nutrients, pollutants, and other materials along and across the coast, and can create hazardous conditions for swimmers (rip currents). However, understanding of the strength and structure of eddies and vortices in the flow field primarily remains limited to numerical models and theory. Here, novel observations of surfzone vorticity at small [O(10m)] and large [O(100m)] spatial scales are presented and related to incident wave conditions and the measured underlying bathymetry. Field experiments were conducted at a sandy beach on the Atlantic Ocean, and nearshore flows were observed using optical remote sensing (coastal imaging) and in situ sensors. Remote sensing algorithms are expanded from previous applications to estimate high spatial resolution two-dimensional surface flows by tracking the motion of naturally occurring foam throughout the surfzone. Estimated currents are correlated with in situ flow measurements, and errors increase as the sea-surface viewing angle becomes more oblique and image quality decreases. Large spatial-scale vorticity estimated using remotely sensed flows increases with alongshore bathymetric inhomogeneity, and complex circulation patterns corresponding to holes and channels in the seafloor persist for days at a time. Small spatial-scale vorticity estimated from a 5-m diameter ring of 14 current meters increases with the directional spread of the incident wave field, consistent with increased vorticity injection from the crest-ends of breaking waves. Small spatial-scale vorticity estimated using remotely sensed flows is spatially variable and correlated with the amount of wave breaking observed at a given location. Enhanced vorticity at large and small spatial scales occurs in the inner surfzone, and virtual drifters released into the remotely sensed flow fields demonstrate cross-shore variability in dispersion and mixing. This thesis expands the understanding of vorticity dynamics in the surfzone through unique field observations and provides new tools for coastal research and monitoring through development of remote sensing techniques.

Two's More Fun than One: How the Presence of Multiple Nutrients Changes Microbial Competition and Foraging in Unexpected Ways

Sat, 01 Feb 2025 00:00:00 GMT

Two's More Fun than One: How the Presence of Multiple Nutrients Changes Microbial Competition and Foraging in Unexpected Ways Bloxham, Blox Willow Microbes exist in incredibly diverse environments with many possible resources (i.e. nutrients) to compete and forage for. To make this complex system tractable, ecologists often study microbes in the presence of a single resource in order to predict and explain what happens with multiple resources. But what gets lost when we do this? Are there phenomena that only emerge in the presence of multiple resources? Here, I explore the ecological implications of three phenomena that each require the presence of at least two resources. First, I show that the diauxic lags that occur when a microbe needs to switch between resources after one is depleted can allow ‘fast-switcher’ microbes to coexist with competitors that exclude them in single-resource environments. Then, I derive a rich temporal niche structure that arises from variations in the order in which resources are depleted in ecosystems with a pulsed resource supply and show that these temporal niches reshape community structure, vastly increasing the expected diversity of microbial ecosystems. Finally, I present a novel differential strategy in which a microbe attempting to intercept a moving source of multiple resources can treat one resource as an attractant and the other as a repellent to significantly increase its chances of successfully intercepting the source as compared to just being attracted to the resources released by the source. Each of these phenomena fundamentally requires the presence of at least two resources and reshapes microbial behavior and ecology. Thus, they collectively highlight the need to carefully consider how characterizations from single-resource environments actually combine to determine what happens in multi-resource environments and what new dynamics must be accounting for in such a bottom-up approach. I conclude with an argument that the case of two resources may be particularly relevant to study due to how much complexity can emerge at just the first step up from one resource to two.

Learning-Based Complex Terrain Navigation Under Uncertainty

Sat, 01 Feb 2025 00:00:00 GMT

Learning-Based Complex Terrain Navigation Under Uncertainty Cai, Xiaoyi In complex off-road environments, accurately identifying traversable terrain is crucial for achieving fast and reliable navigation. Existing methods learn terrain properties directly from data via self-supervision to automatically penalize trajectories moving through undesirable terrain. However, challenges remain in properly quantifying and mitigating risk due to uncertainty in learned models and improving model generalization in novel environments. To address these challenges, this thesis presents a unified framework to learn uncertainty-aware, physics-informed traversability models and achieve risk-aware navigation in both indistribution and out-of-distribution terrain. First, the proposed method efficiently quantifies both aleatoric and epistemic uncertainty by learning discrete traversability distributions and probability densities of the traversability predictor’s latent features. Leveraging evidential deep learning, this work parameterizes Dirichlet distributions with network outputs and proposes a novel uncertainty-aware squared Earth Mover’s distance loss with a closed-form expression that enhances learning accuracy and navigation performance. Second, the proposed method achieves risk-aware navigation by simulating state trajectories with the worst-case expected traversability values to handle aleatoric uncertainty and by penalizing trajectories moving through novel terrain with high epistemic uncertainty. Third, the proposed method improves model generalization by embedding physics priors directly into the mathematical formulation of evidential neural networks and implicitly aligning learned models with physics models through a physics-informed training loss. Finally, through extensive simulation and real-world experiments on wheeled and quadruped robots, it is demonstrated that this work leads to faster navigation with higher success rates when compared to existing risk-aware approaches, even in environments with significant distribution shifts.

Computational Methods to Improve Satellite Attitude Determination and Control with a Focus on Autonomy, Generalizability, and Underactuation

Sat, 01 Feb 2025 00:00:00 GMT

Computational Methods to Improve Satellite Attitude Determination and Control with a Focus on Autonomy, Generalizability, and Underactuation McKeen, Patrick The attitude determination and control system (ADCS) onboard a satellite uses sensors to measure orientation and angular velocity, enabling the satellite to manage angular momentum, counteract disturbances, and point in the desired directions. Many historical ADCS approaches are designed for constant pointing goals, high accuracy sensors, powerful actuators, or larger, high-inertia satellites. Many modern satellites are small satellites (tens of kilograms or less), with lower-cost actuators and sensors, and may have more complicated attitude goals. This dissertation presents a variety of computational approaches to improve ADCS performance by leveraging detailed satellite dynamics modeling and estimation, disturbance inclusion, and trajectory planning–all optimized for efficient onboard computation suitable for small satellites. The proposed framework generalizes ADCS operations, allowing it to adapt automatically to different satellite types, mission requirements, and operational goals, reducing reliance on predefined ground-based commands. This framework can be used in place of standard control laws to make ADCS more autonomous and “hands-off,” calculating its own slews and desaturation while meeting pointing goals, even in cases of underactuation or large disturbances. This generalized and autonomous framework is a contribution of this work, alongside each of its components, which can be individually used in their own right. One key component of this work is a generalized state estimator that integrates a dynamic model of the spacecraft. This estimator demonstrates high accuracy across various satellite configurations, achieving angular error as low as 0.01◦ in low Earth orbit (LEO) with highquality sensors (but no star trackers), compared to the typical 1◦ error of conventional methods. The estimator can account for biases, sensor errors, and external disturbances, ensuring robust performance (e.g., 0.1◦ error in LEO) even with lower-quality sensors (MEMS gyroscopes, plus magnetometers and sun sensors). This adaptability highlights the increased autonomy of the system, as it requires minimal human intervention to maintain high accuracy across diverse mission scenarios. Another major contribution is the integration of disturbance modeling into control laws. By accounting for disturbances directly (either individually or as an all-in-one value tracked by the estimator), rather than through reactive measures like integral control, the proposed methods improve stability and performance, particularly for underactuated systems–improving pointing accuracy by up to 20 degrees. The developed control laws are adaptable to various actuator configurations, disturbance environments, and pointing objectives. This flexibility extends to modifying pointing goals, such as aligning specific vectors rather than requiring a fully specified orientation, enhancing mission adaptability. This work also implements a novel trajectory planning method that generates efficient pointing trajectories for both constant and time-varying goals. The method, based on the Augmented Lagrangian iterated-LQR (ALTRO) approach, creates sequential mission trajectories that optimize performance even under underactuation or disturbance conditions. The planned trajectories are followed by two types of robust closed-loop controllers, applicable across satellite architectures ranging from large weather satellites to 3U CubeSats. By enabling onboard trajectory planning and adaptive control adjustments, this method significantly reduces the need for ground-based planning and interventions, further advancing autonomous operation. The combined framework of estimation, disturbance-aware control, and trajectory planning achieves significantly higher accuracy than traditional ADCS approaches. This enables the use of commercial off-the-shelf components in high-performance missions, overcoming the limitations of low-cost sensors and actuators. The proposed methods allow satellites to operate with weaker or fewer actuators, such as magnetic-only control, while still achieving precise pointing, thereby expanding the feasibility of more autonomous, robust, and cost-effective satellite operations.

thesis in the field of Mechanical Engineering: Relevance for Human-Robot Collaboration: Definitions, Systems, Algorithms, and Applications

Sat, 01 Feb 2025 00:00:00 GMT

thesis in the field of Mechanical Engineering: Relevance for Human-Robot Collaboration: Definitions, Systems, Algorithms, and Applications Zhang, Xiaotong Human-Robot Collaboration (HRC) combines the strengths of human and robotic capabilities to accomplish complex tasks, yielding significant impacts in various domains. To enable seamless interaction in dynamic and unpredictable environments, robots are required to efficiently and accurately perceive their surroundings, align reasoning with human cognition, anticipate key attributes, and generate safe, effective actions to support humans proactively. This thesis introduces relevance, a novel concept inspired by human cognition, to improve the efficiency, safety, and intelligence of HRC. Relevance enables robots to prioritize objects based on their importance to human goals, allowing them to concentrate computational resources on key elements. This focused approach reduces input space for essential algorithms, minimizes processing delays, and enhances safety and adaptability in dynamic environments, facilitating more natural and intuitive collaboration with humans. This thesis systematically explores the concept of relevance, introducing a hierarchical model for relevance quantification that combines scene understanding in cluttered environments with an event-based, multi-modality framework, enabling real-time relevance determination based on human objectives, preferences, spatial-temporal relationships, and constraints. A relevance-based perception strategy further directs models to prioritize key areas, reducing computational and inference times, while two new safety metrics—Critical Collision Probability (CCP) and Average Collision Probability (ACP)—quantify reduced collision risks in Human-Robot Collaboration (HRC). Additionally, a relevance-driven framework integrates relevance quantification with dynamic scene understanding and decision-making, achieving high human objective and relevance prediction accuracy. An advanced human intention prediction framework using head orientation, object affordance, and hand movement also enhances precision, accuracy, and F1 scores over baseline models. Results demonstrate that relevance quantification significantly reduces task planning time by 79.56% and inquiries by 80.84%, with a real-world coffee-serving demonstration highlighting its potential for proactive, autonomous assistance. Furthermore, the safe motion generation algorithm reduces collision incidents by 63.76% and collision frames by 44.74%, supporting accurate, safe robotic assistance in dynamic environments. The concept of relevance enhances the efficiency, safety, and intelligence of human-robot collaboration (HRC) within dynamic and unpredictable environments, supporting a deeper integration of robotics into diverse real-world applications. Its potential extends beyond HRC, with promising applicability in autonomous driving and other complex domains where adaptive, context-aware decision-making is essential.

Mechanisms of terrestrial organic carbon export and preservation in the marine environment

Sat, 01 Feb 2025 00:00:00 GMT

Mechanisms of terrestrial organic carbon export and preservation in the marine environment Boehman, Brenna L. Export of terrestrial carbon from land to sea is a globally important carbon flux that is poorly constrained and has implication for atmospheric carbon levels over modern and geologic timescales. Many factors control the fate of exported carbon and the subsequent impact on carbon budgets, including the timescales of export, the composition of organic matter, and degradation processes. This thesis uses biomarkers, bulk geochemical tools, and incubation studies to interrogate the factors controlling terrestrial carbon export and preservation in the marine environment. The thesis focuses on two globally important river systems that collectively deliver 25% of the total terrestrial carbon flux to the ocean, the Ganges-Brahmaputra (G-B) Rivers and the Amazon River. The first two chapters focus on the G-B Rivers, utilizing compound specific biomarker analysis within a high sedimentation rate (30 cm/yr) terrestrial archive in the Bay of Bengal, we interrogate (i) timescales of organic carbon export from land to sea, and (ii) basin-scale geochemical responses to rice agriculture expansion. These analyses utilize the radiocarbon ages and stable carbon-13 isotopic composition of lipids produced by Archaea and Bacteria. We identify that ca. 75% of these biomarkers experience millennial scale storage in the G-B basin, in agreement with previously assessed plant-derived compounds, highlighting that an overarching soil stabilization mechanism controls the age of exported terrestrial organic matter. Individual biomarkers and bulk geochemical analysis chronicle the change in methane-derived soil carbon within the basin due to rice paddy expansion, highlighting that 49% of Bangladesh’s methane emissions from 1990-2008 have been abated by soil storage. The last two chapters focus on the Amazon River, to examine the fate of terrestrial organic carbon in the marine environment, (iii) utilizing geochemical analysis of historical sediments and sediments from a field campaign in 2023, and (iv) utilizing terrestrial and marine endmembers in incubation experiments simulating the dynamic coastal environment. Sediment geochemical and biomarker analyses highlight the preservation of an isotopically distinct terrestrial endmember in the coastal sediments, which has led to at least 50% underestimation of the burial efficiency. Quantitative stable isotope probing incubations using 13C-lignin indicate the dual role of microbially-mediated and photo-degradation, and highlight that the microbial communities primarily responsible for lignin degradation in the marine environment are of terrestrial origin, and identify a new ecological role for Bathyarchaeota. This thesis integrates diverse biogeochemical techniques across the terrestrial-marine interface to examine important open questions in globally important carbon budgets, merging isotope geochemistry, microbiology and earth science. The findings contribute to our understanding of the modern carbon cycle and the impact of anthropogenic perturbations of the last decades and into the future.

Reproduction, settlement, and phenology of intertidal barnacles: Implications for larval dispersal

Sat, 01 Feb 2025 00:00:00 GMT

Reproduction, settlement, and phenology of intertidal barnacles: Implications for larval dispersal Weinstock, Jane B. Knowledge of the consequences of ocean warming on marine populations and communities is urgent. Warming oceans are predicted to result in changes to the seasonal timing of reproduction and settlement (phenology); faster development rates and, for crustaceans, smaller larvae; reduced larval dispersal distances; and reduced connectivity between coastal populations. However, these predictions are largely based on laboratory and modelling studies, with little observational research to explore how these interactions unfold in natural ecosystems where temperature variability is pervasive. In this thesis, I investigate the links between reproduction and settlement timing of intertidal barnacles, and I explore the extent to which the timing of these events is explained by environmental and astronomical cycles and by water column conditions. In Chapter 2, I assess the cycles driving Chthamalus fissus reproduction and settlement in Southern California, and I offer a first order estimate of alongshore larval transport. I found that barnacles were reproductively active almost year-round, with clear lunar cyclicality and modest seasonality. Conversely, settlement exhibited little cyclicality on any timescale. Chapters 3, 4, and 5 focus on the effects of temperature on Semibalanus balanoides early life history along a steep temperature gradient in the northwest Atlantic over twenty years of warming. In Chapter 3, I investigate the effects of intertidal temperature on reproduction timing, analyzing separately the processes of fertilization, embryonic brooding, and larval release. In Chapter 4, I estimate larval duration in natural populations, and I measure the impact of temperature on larval duration in the laboratory and field. In Chapter 5, I investigate the effects of water temperature on larval size at settlement. I found that warmer nearshore temperatures significantly correlated with shorter brooding times of developing embryos, shorter field-estimated larval duration, and smaller larval settlers. Notably, the interplay between benthic reproduction, pelagic development, and temperature variability across space and time created counter-intuitive patterns in larval duration, size, and likely dispersal. Together, these findings point to the importance of reproductive timing in determining dispersal and population connectivity, and they highlight the need for extensive field measurements to quantify phenology and phenology shifts in benthic systems.

Radium and Mercury Dynamics in the Arctic: Investigating Terrestrial Inputs, Groundwater Discharge, and Chemical Cycling in a Changing Climate

Sat, 01 Feb 2025 00:00:00 GMT

Radium and Mercury Dynamics in the Arctic: Investigating Terrestrial Inputs, Groundwater Discharge, and Chemical Cycling in a Changing Climate Bullock, Emma Jacqueline The Arctic Ocean is distinctive due to its extreme seasonal variations in temperature and significant terrestrial inputs, including freshwater, carbon, nutrients, and toxins. Of particular concern is mercury (Hg) in its neurotoxic form, methylmercury (MeHg), which is already beginning to adversely affect Arctic human populations and wildlife. However, the region’s harsh conditions and remoteness have made conducting seasonal chemical and hydrological studies challenging. Tracers of boundary inputs, such as the radium (Ra) isotope quartet, offer potential for tracking and quantifying riverine and submarine groundwater discharge (SGD) of species like Hg into the Arctic Ocean. This thesis employs seasonal data and laboratory experiments to investigate the factors influencing terrestrial Ra inputs to the Arctic Ocean, quantifies SGD and associated Hg inputs to an Arctic coastal lagoon, and elucidates the chemical and geological factors influencing Hg cycling in Arctic groundwater. Using historical and unpublished datasets combined with new laboratory investigations, differences in inputs of riverine Ra isotopes between the North American and Eurasian land masses were identified. The findings revealed higher Ra fluxes from the North American continent, attributed to greater sediment loads and lower organic matter in rivers compared to those on the Eurasian land mass. Subsequently, Ra data from five extensive field campaigns to Simpson Lagoon, Alaska, provided insights into Ra cycling on a more localized scale. These campaigns offered the first seasonal perspective on supra-permafrost SGD along an Arctic coastline, suggesting that SGD fluxes may rival those of rivers along the Beaufort Sea coast. Concurrently collected Hg groundwater concentrations allowed for the development of the first estimates of Hg fluxes from groundwater to the Arctic Ocean. If these estimates hold true along the rest of the Pan-Arctic coastline, they could significantly alter our understanding of microbial MeHg uptake in the Arctic Ocean. Finally, sediment cores from Simpson Lagoon and two other locations along the Beaufort Sea coast were used to examine how changing groundwater conditions, such as changing salinity, temperature, and redox conditions, influence Hg cycling. These experiments, alongside findings from Simpson Lagoon groundwater, indicate that Hg cycling in recently thawed permafrost sediments involves a complex interplay between organic material, metal oxides, and sulfide species, with groundwater conditions and soil carbon content playing crucial roles in Hg mobilization.

Towards an Artificial Neuroscience: Analytics for Language Model Interpretability

Sat, 01 Feb 2025 00:00:00 GMT

Towards an Artificial Neuroscience: Analytics for Language Model Interpretability Gurnee, Robert Wesley The growing deployment of neural language models demands greater understanding of their internal mechanisms. The goal of this thesis is to make progress on understanding the latent computations within large language models (LLMs) to lay the groundwork for monitoring, controlling, and aligning future powerful AI systems. We explore four areas using open source language models: concept encoding across neurons, universality of learned features and components across model initializations, presence of spatial and temporal representations, and basic dynamical systems modeling. In Chapter 2, we adapt optimal sparse classification methods to neural network probing, allowing us to study how concepts are represented across multiple neurons. This sparse probing technique reveals both monosemantic neurons (dedicated to single concepts) and polysemantic neurons (representing multiple concepts in superposition) in full-scale LLMs confirming predictions from toy models. In Chapter 3, we identify and exhaustively catalog universal neurons across different model initializations by computing pairwise correlations of neuron activations over large datasets. Our findings show that 1-5\% of neurons are universal, often with clear interpretations, and we taxonomize them into distinct neuron families. To investigate spatial and temporal representations, we analyze LLM activations on carefully curated datasets of real-world entities in Chapter 4. We discover that models learn linear representations of space and time across multiple scales, which are robust to prompting variations and unified across different entity types. We identify individual "space neurons" and "time neurons" that reliably encode spatial and temporal coordinates. In Chapter 5, we use optimal sprase regression techniques to improve the sparse identification of nonlinear dynamics (SINDy) framework, demonstrating improved sample efficiency and support recovery in canonical differential systems. We then leverage this improvement to study the ability of LLMs to in-context learn dynamical systems and find internal representations which track the underlying system state.

OPTASAT: An Open-Source, Flexible Software Framework for Small Satellite Operations

Sat, 01 Feb 2025 00:00:00 GMT

OPTASAT: An Open-Source, Flexible Software Framework for Small Satellite Operations Murphy III, Thomas Joseph The unprecedented growth in access to space has created a corresponding growth in the number of spacecraft and the number of people operating spacecraft. This has meant that many of these operators are operating spacecraft for the first time. Gone are the days when the only operators of spacecraft were national governments, militaries, and massive corporations. The operators of small spacecraft today include many early-career individuals who need the tools to enable them to make strong decisions in the behavior of their spacecraft. The tools for operating spacecraft are often overlooked by teams focusing on the spacecraft themselves, but these operating tools are critical for mission success. Spacecraft operations tools have not developed in a similarly low-cost, widespread fashion as the spacecraft have. The best tools for modeling and understanding the situation of a satellite in space remain locked behind high barriers to entry including high cost, long training, and complex interfaces. In the same way that satellites have gone from the size of automobiles to the size of toasters, the software for operating them needs to go from expensive, complicated, high-performing suites to simple, flexible, approachable options that are accessible to the democratized space operators. New spacecraft operations staff need straightforward, direct interfaces which give them the knowledge of where their spacecraft is, where it will be, and what it will be able to do, and they need to know when all the options at their disposal are viable. Operators also need to be given the capability to adjust their software in whatever ways are necessary to tailor it to the particular parameters of their missions, to reflect the incredible variety of spacecraft and missions that exist today. A gap exists in spaceflight software. Users need software that can perform their mission planning tasks in the short term and to inform them of the upcoming parameters of their spacecraft which concern them, whether this is the spacecraft’s location, solar illumination, orientation, or any other property which is relevant to their particular mission. This software must also allow the users to be aware of the expected output of their sensors, especially imaging sensors, such that they may have an understanding of what they are imaging and what it ought to look like. Finally, this software must be open-source, enabling the user to audit the software and make changes to the software to customize it to their preferences, which may differ from anything the original software developer could have imagined. Such spaceflight software does not yet exist. This dissertation develops and presents OPTASAT, the Open-source Python Tool for Awareness of Spacecraft and Analysis of Telemetry, which provides an extensible, modular interface for incorporation of multiple tools which contextualize spacecraft data in a manner which maximizes usefulness for the operators. A priority is visualization of data to facilitate rapid understanding and distillation of the complexity of a spaceflight operation. This software has been released as a fully-featured, open-source software toolkit which performs the mission analysis components deemed most crucial to those who stand to benefit from it. This software is intended to fulfill the needs of small spacecraft missions. Several particular application cases are studied, including that of an Earth Sensing mission, and Astronomy mission, and modeling communications for a real laser crosslink mission. These case studies are evaluated for their ability to present the relevant information to the operator. For Earth Sensing, this involves displaying information regarding the spacecraft’s location with respect to the Earth, and enabling the selection of ground targets for imaging. For astronomy, the relevant information concerns the stars visible in the sky, and the spacecraft’s relationship to sources of interference like the Sun and Moon. For the laser crosslink example, we study the operator’s understanding of the spacecraft as they pass over a ground station and determine the operational configurations available for this communication. OPTASAT fills gaps in the field. OPTASAT presents users with a tool which is flexible and intuitive to use for understanding data from spacecraft in a way that is not currently available in the offerings on the market. Additionally, it takes functionality that is currently available in proprietary paid software and makes it available for free, in an open source offering that is accessible to everyone. OPTASAT will allow spacecraft operators (especially those operating spacecraft for the first time) to confidently know the state of their spacecraft, enabling them to make the best decisions for their satellites. This will reduce barriers to entry and smooth the learning curve, reducing the amount of overhead to new spacecraft operators. OPTASAT will be yet another step in the ongoing process of making space more accessible to a larger pool of users.

The Hidden Roots of Neoliberal Success in Agrarian Transformation: State Engagement, Farmer Professionalization, and Technological Interdependence in the Senegal River Valley

Sat, 01 Feb 2025 00:00:00 GMT

The Hidden Roots of Neoliberal Success in Agrarian Transformation: State Engagement, Farmer Professionalization, and Technological Interdependence in the Senegal River Valley Spielberg, Brian Jonars Besana Recent scholarship celebrates irrigated rice in the Senegal River Valley (SRV) as a success story. This is remarkable considering the SRV’s history of agrarian transformation, which critics characterize as incoherent, erratic, and self-destructive. How did this turnaround happen? How did good seeds emerge from bad soil? Conventional explanations point to enlightened market-based reforms and technological upgrading following state withdrawal from most agricultural activities. In other words, the SRV is portrayed as a triumph of neoliberalization. This dissertation offers an alternative, additive view. In Paper 1, I situate the SRV’s transformation in broad historical context, showing how notions of development, technological change, and poverty alleviation have evolved and the implications for what strategies are pursued. I illustrate how a popular contemporary development model— appropriate technology (AT) 2.0, an evolution of Schumacher’s 1970s AT 1.0—that valorizes smallscale technologies and market-led interventions is attractive in explaining successes like the SRV, even as it proves ultimately reductive. In Paper 2, I demonstrate how the state, despite policies curtailing its activities and a dominant narrative asserting its disengagement, continues to play an active role in the SRV. By imparting practical skills, such as pump operation, contract negotiation, and bookkeeping, state action helped farmers professionalize. A durable effect is a “we’re in this together” state-farmer mentality. When this relationship is tested, well-respected intermediaries, often religious leaders, intercede. In Paper 3, I show how farmers construct assemblages of resources, skills, and knowledge to achieve their goals. They rely on negotiation skills and social ties with local leaders, appealing to “public interest” couched in religious terms. In forsaking key aspects of the dominant assemblage to pursue alternatives, farmers exercise their agency and enhance market functioning by permitting flexibility, acknowledging technological interdependencies, and mitigating recurrent risks. This dissertation offers hope that successful agrarian development is possible in challenging, resource-constrained environments. Based on 11 months of fieldwork, I show how state and farmer actions bolstered market reforms, underpinning their success. In centering on-the-ground realities, I move beyond dominant explanations and neat theoretical classifications to reveal underreported but nonetheless fundamental processes and mechanisms through which development occurs.

Singlet exciton fission-enhanced silicon photovoltaics: Interfacial engineering, device design and spectroscopic technique development

Sat, 01 Feb 2025 00:00:00 GMT

Singlet exciton fission-enhanced silicon photovoltaics: Interfacial engineering, device design and spectroscopic technique development Nagaya, Narumi The growing global energy demand combined with resource and space limitations necessitate enhancements in crystalline silicon solar cells, which are the current dominant solar technology. However, their efficiencies have only increased incrementally over the recent 20 years, as they are starting to approach the theoretical efficiency limit. The main source of loss is thermalization, where energy in excess of the bandgap absorbed by silicon is lost as heat. Singlet exciton fission in organic molecules has been proposed to reduce these losses. By having the organic layer absorb the high energy light and transferring the triplet excitons generated from the singlet fission process to silicon, the photocurrent in this spectral region can be doubled, with the potential of raising the efficiency from the traditional limit of 29.4 % to up to 42 %. The greatest challenge with these devices has been to demonstrate an increase in the silicon photocurrent, a necessary condition to show that the technology is viable. Scientifically, there are three main components to this problem. The first is to successfully couple the triplet excitons to silicon. The second is that not much is understood regarding the exciton and charge carrier dynamics at this interface. Finally, the silicon solar cell architecture should also be considered to extract transferred carriers effectively. This thesis tackles these three parts from an interfacial materials, device architecture and spectroscopy approach. Using tetracene as the singlet fission layer and n-doped silicon, we show that defect-induced states in a thin interlayer of hafnium oxynitride that lie near the band edge of silicon are beneficial for triplet exciton transfer. We also identify that triplet-induced electric field-effect passivation is beneficial for the triplet sensitization process of silicon, and design a new bilayer interface consisting of a zinc phthalocyanine donor layer that introduces preferential near- silicon band edge states, and an ultrathin oxide chemical passivation layer. We then study various device architectures, confirming the importance of using a device designed to extract surface charge carriers efficiently, demonstrating the first enhancements in single-junction silicon solar cell external quantum efficiencies and photocurrent from singlet fission. Finally, we build and use advanced spectroscopy techniques and numerical frameworks to study exciton and charge carrier dynamics in singlet fission-sensitized solar cell materials, confirming that the triplet excitons are contributing to all the positive effects observed in the devices. These results have shown that singlet fission-sensitized silicon solar cells are a viable technology for enhancing silicon solar cell efficiencies beyond the conventional single-junction limit. This interface remains a rich area for fundamental scientific studies, involving coupling between molecular dark states to bulk silicon. We hope that the key findings can help direct research efforts towards scalable implementation of this technology, and stress that the fundamental understanding of the interface also has broad implications to other silicon technologies that can benefit from enhanced quantum yields, including photodetectors.

Algorithmic Advances in Range-Aided Navigation

Sat, 01 Feb 2025 00:00:00 GMT

Algorithmic Advances in Range-Aided Navigation Papalia, Alan A. This thesis contributes to the advancement of range-aided simultaneous localization and mapping (RA-SLAM) through algorithmic developments and real-world demonstrations. Broadly speaking, SLAM is the process by which an agent combines sensor measurements to simultaneously create a map of the world and localize itself within this map. SLAM has been called the ‘holy grail’ of field robotics, and in many instances it is a critical enabling capability for autonomous agents to operate in the real world. RA-SLAM is the specific case of SLAM which incorporates point-to-point distance measurements (e.g., distance measurements between an autonomous underwater vehicle and an acoustic buoy) into the inference process. The ability to leverage such measurements is desirable, as they can help in resolving ambiguities (e.g., am I in hallway A or B) and the relevant sensors are often low-cost and simple to integrate (and thus pose the potential to be widely deployed). However, there are theoretical challenges that have historically limited the reliability of RASLAM approaches. At the root of these challenges is the issue that a single range measurement does not uniquely determine the relative position between two points. In state-of-the-art RASLAM formulations, this ambiguity manifests as non-convexity in the maximum a posteriori inference problem. As a result of this non-convexity, standard local-search optimizers are highly dependent on quality initializations to obtain the correct state estimate. To address this issue of reliability, this thesis presents the first certifiably correct algorithm for RA-SLAM. This algorithm, Certifiably Correct RA-SLAM (CORA), is capable of (i) obtaining globally optimal solutions for many real-world RA-SLAM problem instances and (ii) providing certificates of correctness for these solutions. CORA leverages a novel semidefinite programming (SDP) relaxation of the RA-SLAM problem, which it solves efficiently using the Riemannian Staircase methodology. This methodology allows CORA to typically obtain globally optimal solutions faster than the existing state-of-the-art local solvers. These results expand our understanding of problems suited for efficient global solvers and highlight the key problem structures that appear necessary to develop and deploy such solvers, pointing towards exciting future directions in trustworthy model-based autonomy. We demonstrated the performance of CORA on a range of real-world RA-SLAM datasets, including a set of large-scale multi-agent experiments conducted as part of this work. In these experiments CORA reliably estimates agents’ trajectories in both single- and multi-robot settings. CORA gracefully scales to large problems consisting of multiple agents and tens of thousands of robot poses. These experiments not only validate CORA’s performance, but also fill an existing gap in open-source datasets available to the research community and provide practical insights to guide future deployments of autonomous navigation systems in large, complex environments.

Predicting Flood Risks to City Infrastructure Systems Utilizing Scalable, Time Sensitive Modeling

Sat, 01 Feb 2025 00:00:00 GMT

Predicting Flood Risks to City Infrastructure Systems Utilizing Scalable, Time Sensitive Modeling Boukin, Katerina Flooding is emerging as the most expensive and frequent natural hazard around the world. Floods are highly dynamic in nature and cause physical damage to our built environment, loss of life, economic damage, and major impacts to society. An example of this is the at-ground road system, which comprises 30-60% of a city’s area in the US, is highly susceptible to flood damage, while still needs to act as evacuation routes for local residents. Similarly, the underground built system is extremely vulnerable to flooding damage as well as life risk to anyone within it. With urban landscapes constantly evolving, accurately predicting flood propagation and extent is imperative to mitigate these risks, especially as floods worsen due to climate change. Historically, the focus of flood risk assessment through industry and academia has been on the coastal urban environment, assessing the impact of fluvial flooding. This resulted in many risk assessment tools that mostly caters to the infinite amount of flood water identified from a riverine or coastal fluvial flooding. As for the rain-driven impact, the common practice simply changed the flood modeling to pluvial oriented, keeping the rest of the risk tool components identical for the different flood mechanisms. For pluvial flooding, existing urban flood modeling tools such as SWMM and PC-SWMM are limited by their catchment-based approach, neglecting surface runoff dynamics and spatial-temporal flood impacts. Consequently, these tools fail to capture the full extent of rain-driven floods, underestimating their severity and impact on urban environments. Addressing this gap requires sophisticated simulations that account for rain event characteristics and city morphology, yet such simulations are computationally demanding and require detailed urban data. Currently, flood impact analysis tools lack specificity for pluvial flood risks and do not address the risks to various city systems beyond building damage. As a result, the contribution of pluvial floods to overall flood risks is underestimated, compromising infrastructure resilience. As flood model results are a critical component in flood risk assessments, the accuracy of spatial temporal urban flood results will allow the pluvial flood impact assessment to be simplified and the flood damage to the different urban systems will be quantified. This research aims to develop a scalable and streamlined method to accurately quantify the risks of rain-driven floods to urban infrastructure systems. It addresses three key questions: (1) To what extent does current practice underestimate pluvial flood impacts? (2) What are the impacts of pluvial flooding on pavement systems when incorporating spatial-temporal modeling? (3) What is the significance of modeling pluvial floods using urban underground spaces? Using advanced flood modeling and numerical soil-water infiltration techniques, this research will quantify damages and lifecycle impacts to pavement and underground spaces systems. The method will provide information on the spatial and temporal distribution of flood damage and will enable scaling up single-element assessments to system-wide impacts. This holistic approach will improve urban flood risk management, supporting informed decision-making and the development of resilient infrastructure systems.

Examining the placenta’s role in neurodevelopment in the context of maternal obesity

Sat, 01 Feb 2025 00:00:00 GMT

Examining the placenta’s role in neurodevelopment in the context of maternal obesity Gunter-Rahman, Fatima M. The placenta is a key organ determining fetal development and likely contributes to programming of long-term offspring health, in particular neurodevelopment. Various maternal exposures, such as psychosocial stress, diabetes, infection, and high body mass index (BMI) are associated with higher risks of impaired neurodevelopment in the offspring. One third of women in the United States are affected by maternal obesity (MO) during pregnancy, making it one of the most common exposures. We profiled the term placental transcriptome in humans using single-nucleus RNA-seq, comparing expression profiles in MO versus lean conditions, in each of the two faces of the placenta separately. On both sides of the placenta across several cell types, MO was associated with upregulation of hypoxia response genes. On only the maternal-facing side, hypoxia gene expression was associated with offspring neurodevelopment outcomes measured at multiple time-points, in the Genetics of Glucose regulation in Gestation and Growth (Gen3G) cohort, an independent pre-birth cohort with bulk RNA-seq from placental tissue. We leveraged Gen3G to determine genes that correlated with impaired neurodevelopment and found these genes to be most highly expressed in extravillous trophoblasts (EVTs). EVTs further showed the strongest correlation between neurodevelopment impairment gene scores (NDIGSs) and the hypoxia gene score. We validated these findings in EVTs in an independent single-cell RNA-seq cohort from second trimester placenta, and found that cultured EVTs have increased NDIGSs in response to exposure to hypoxia. These data suggest that hypoxia in EVTs may be a key process in the neurodevelopmental programming of fetal exposure to MO. Our work opens up new directions of research, such as exploring applications of antioxidants to potentially mitigate some of the offspring consequences associated with MO.

Strange Attitudes on Top

Sat, 01 Feb 2025 00:00:00 GMT

Strange Attitudes on Top Močnik, Maša This dissertation investigates how attitude verbs of belief and desire engage with embedded material of a similar nature. Chapter 1 looks at the (cross-linguistically unusual) Slovenian existential doxastic attitude verb dopuščati (‘allow for the possibility’) and the embedding of epistemic modal verbs under it. Chapter 2 looks at the (overall puzzling) want and its Slovenian counterpart hoteti, and at their behaviour with respect to embedded doxastic attitudes, epistemic adverbs, and epistemic adjectives. Chapter 3 looks at the (cross-linguistically unusual) Koryak variable-force variable-flavour attitude verb ivək (‘think’, ‘allow for the possibility’, ‘say’, ‘suggest’) and at how its apparent bouletic flavour (‘wish’, ‘hope’, ‘fear’) is derived with the help of covert desiderative components inside the embedded clause. Attitude verbs have the standard role as quantifiers over possible worlds (Hintikka 1962), parameters of evaluation are assumed to contain a set of worlds called the information state (Yalcin 2007; a.o.), which the attitude verb modifies and passes to the embedded clause, while the epistemic modal base is taken to be ‘local’, forming a subset of the information state (Mandelkern 2017, 2019a). Some of the overarching theoretical contributions are the introduction of a new parameter of evaluation (‘selected state’), which is crucial in modelling embedding under non-universal attitude verbs, and a refined view of epistemic modality. Subjective epistemic modality is proposed to involve a second constraint on the shape of the modal base, whose effect is to strengthen embedded necessity claims and help derive the infelicities observed in chapters 1 and 2. We also address the connection between beliefs and desires in the context of various desire interpretations (wants in chapter 2, hopes and wishes in chapter 3).

Simple Models for Complex Tropical Dynamics

Sat, 01 Feb 2025 00:00:00 GMT

Simple Models for Complex Tropical Dynamics Tuckman, P.J. Studying Earth's tropics is an essential part of understanding the climate, simulating the Earth system, and predicting the societal impacts of weather. In this thesis, we use a hierarchy of models -- including analytically tractable equations, simplified simulations, and full general circulation models -- to study tropical phenomena including the Hadley Circulation, the Inter-Tropical Convergence Zone (ITCZ), the South Asian monsoon, Pacific and ENSO seasonality, the Walker Circulation, and the modeling of the tropical energy budget. We begin with an examination of tropical SSTs and the ITCZ under warming, finding that the Hadley cells weaken and tropical SST gradients decrease in a warmer climate. The ocean's subtropical cells strengthen and transport more energy in a warmer climate, further flattening SST gradients. The ITCZ, meanwhile, increases in strength with warming because of the exponential relationship between humidity and temperature, and the presence of a dynamic ocean changes a single-ITCZ with a sinusoidal seasonal cycle to a double-ITCZ with a square wave seasonal cycle. Next, we study the ``monsoonal mode,'' an energy and precipitation anomaly triggered by the South Asian Monsoon that moves into the West Pacific during Northern Hemisphere autumn. The monsoonal mode is discussed as a possible underlying cause of the seasonality of the Pacific, i.e., that the West Pacific and ENSO both have seasonalities that favor one season despite being on the equator. To show this, ENSO seasonality is examined using simplified simulations and an energy budget of the Central-Eastern Equatorial Pacific. Similar techniques are then used to study ENSO events in warmer climates, and it is found that the Pacific zonal SST gradient and the Walker circulation, which are the sources of ENSO instability, weaken with warming, decreasing the magnitude of ENSO events. Lastly, we assess the energy budget of CMIP6 models. It is shown that all CMIP6 models have more energy input to the deep tropics than ERA5 reanalysis, and this bias is bigger in the Southern Hemisphere. The hemispheric asymmetry in this bias can be traced back to radiation absorbed by the atmosphere, which is associated with dust (for shortwave radiation) and total column water (for longwave radiation). As a whole, this thesis demonstrates the utility of studying complex problems with simple models and deepens our understanding of Earth's tropics.

Ponderomotive Forces in Pilot-Wave Hydrodynamics

Sat, 01 Feb 2025 00:00:00 GMT

Ponderomotive Forces in Pilot-Wave Hydrodynamics Evans, Davis J. Droplets bouncing on a vibrating bath may self-propel (or ‘walk’) via a resonant interaction with their self-induced pilot wave. In pilot-wave hydrodynamics (PWH), the spontaneous emergence of coherent, wave-like statistics from chaotic trajectories has been reported in several settings. Owing to the similarity of PWH to Louis de Broglie’s realist picture of quantum mechanics, the question of how such statistics emerge has received considerable recent attention. A compelling setting where coherent statistics emerge in PWH is the hydrodynamic analog of the quantum corral. When walking droplets are confined to a circular cavity or ‘corral’, a coherent statistical pattern emerges, marked by peaks in the positional histogram coincident with extrema of the cavity eigenmode. Stroboscopic models that idealize the drop’s bouncing dynamics as being perfectly resonant with their Faraday wave field have proven incapable of capturing the emergent statistics. In this thesis, we present new experimental and theoretical findings in a variety of pilotwave hydrodynamical settings where non-resonant bouncing plays a key role in the droplet dynamics and emergent statistics. First, we find that modulations to resonant bouncing influence the stability threshold of a Bravais lattice. Second, we demonstrate that resonant bouncing can be disrupted by the imposition of suboctave driving, which may be used to induce a rearrangement of bound states of bouncing droplets. We then proceed to an integrated experimental and theoretical study of the hydrodynamic corral, highlighting the role of non-resonant bouncing in the emergent statistics. We first introduce a new experimental method for simultaneously measuring the drop position and pilot wave height. We then report new measurements of the pilot wave and vertical bouncing dynamics. We demonstrate that the complex pilot wave arising in corrals may play the same role as suboctave driving in disrupting resonant walking. Our experimental findings motivate a new theoretical framework that predicts that modulations in the histogram emerge as a consequence of ponderomotive effects induced by non-resonant bouncing. We then connect the ponderomotive drift observed in hydrodynamic corrals to extant theories of quantum mechanics.

Quantum Computing from Graphs

Sat, 01 Feb 2025 00:00:00 GMT

Quantum Computing from Graphs Khesin, Andrey Boris While stabilizer tableaus have proven exceptionally useful as a descriptive tool for additive quantum codes, they otherwise offer little guidance for concrete constructions or coding algorithm analysis. We introduce a representation of stabilizer codes as graphs with certain structures. Specifically, the graphs take a semi-bipartite form wherein input nodes map to output nodes, such that output nodes may connect to each other but input nodes may not. Intuitively, the graph’s input-output edges represent information propagation of the encoding circuit, while output-output edges represent the code’s entanglement structure. We prove that this graph representation is in bijection with tableaus and give an efficient compilation algorithm that transforms tableaus into graphs. We then show that this map is efficiently invertible, which gives a new universal recipe for code construction by way of finding graphs with sufficiently nice properties. The graph representation gives insight into both code construction and algorithms. To the former, we argue that graphs provide a flexible platform for building codes particularly at small non-asymptotic scales. We construct as examples several constant-size codes and several infinite families codes. We also leverage graphs in a probabilistic analysis to extend the quantum Gilbert-Varshamov bound into a three-way distance-rate-weight trade-off. To the latter, we show that key coding algorithms, distance approximation, weight reduction, and decoding, are unified as instances of a single optimization game on a graph. Moreover, key code properties such as distance, weight, and encoding circuit depth, are all controlled by the graph degree. We give efficient algorithms for producing simple encoding circuits whose depths scale as twice the degree and for implementing logical diagonal and certain Clifford gates with non-constant but reduced depth. Finally, we construct a simple efficient decoding algorithm and prove a performance guarantee for a certain classes of graphs. These results give evidence that graphs are generically useful for the study of quantum computing and its practical implementations.

Underwater Semantic Simultaneous Localization and Mapping

Sat, 01 Feb 2025 00:00:00 GMT

Underwater Semantic Simultaneous Localization and Mapping Singh, Kurran Building semantically meaningful object level maps of underwater environments is crucial for enabling higher-level autonomy, fostering human-robot collaboration, and providing compressed map representations for bandwidth-constrained underwater communications, while localizing against such maps can improve the positioning accuracy of underwater vehicles by correcting for odometric drift. However, underwater semantic simultaneous localization and mapping (SLAM) has lagged behind analogous terrestrial and aerial semantic SLAM techniques largely due to the lack of large labeled underwater datasets and the challenging sensor modalities specific to underwater environments. To address these shortcomings, this thesis develops a range of methodologies to advance underwater semantic SLAM capabilities. First, self-supervised learning and visual foundation models are leveraged to detect and segment underwater objects in an open-set manner, i.e., objects need not be present in the training dataset to be detected. The machinery of the open-set object detection technique breaks several assumptions made by existing closed-set semantic SLAM methods. Thus, new methods for object representation and data association are proposed and demonstrated. A method to localize underwater objects is then developed through an analysis of the geometry of underwater monocular cameras and multibeam sonars. Finally, a formulation of open-set object-level place recognition as a graph matching problem is introduced. The formulation includes a method for calculating and tracking semantic uncertainty for open-set object detections. Experimental results on both underwater and terrestrial datasets demonstrate that the proposed formulation can be used for real-time accurate open-set object-based place recognition. In summary, techniques for underwater object detection, localization, and data association are introduced and integrated with probabilistic graphical models for open-set semantic SLAM. The proposed techniques are tested across a wide variety of scenarios, and are shown to generalize to terrestrial settings as well.

Organizational Forms and Practices: Essays on Implications for Frontline Workers and Performance

Sat, 01 Feb 2025 00:00:00 GMT

Organizational Forms and Practices: Essays on Implications for Frontline Workers and Performance Scott, Karen MacKenzie In three essays, this dissertation explores how organizational forms and workforce practices shape frontline work experiences and organizational performance. Using both quantitative and qualitative methods, I explore how frontline workers experience work and what factors shape their performance. In the first essay, I examine how workforce practices in nursing homes relate to organizational performance. Specifically, I evaluate performance on resident health outcomes for both pre-pandemic and COVID-19 conditions. Combining Federal and state administrative data sets with non-public data on early COVID-19 spread and mortality, I investigate the degree to which the organization of work for frontline workers predicted resident health as a measure of organizational performance for nursing homes. In a period of global stress on health and care systems, I seek to understand to what extent prepandemic predictors of performance remained important. When nurses spent more time with residents, residents experienced better care both before and during the pandemic. Yet contrary to expectation, the role of clinical outsourcing became more relevant during the pandemic, potentially reflecting greater workforce flexibility or targeted COVID-19 workforce support to facilities that outsourced nursing activities before the pandemic. These results depict how environmental changes and alternative performance measures call into question established relationships in the high-performance work systems literature. In the second essay, I use in-depth interviews and field observations to uncover the process of constructing ownership culture in an employee-owned firm. I demonstrate how workers co-create their own control system, supported by a high financial value of ownership, strategic managerial communication, peer pressure, and performance management. This critical case challenges the dominant view in the employee-ownership literature that success requires formal worker participation in decision-making. Further, it investigates the “black box” of culture-building in an employee-owned firm. The third essay builds on this understanding by evaluating the stated motives of individual worker-owners in a home care cooperative. The cooperative developed as a pilot initiative with non-profit partners to develop a home care organization that would provide quality jobs and quality care, while integrating immigrant workers. I traced the workers’ justifications for joining and participating in these cooperatives. Rather than aligning with expected motives from previous studies or with Worker Center motives, I find that these workers adapted motives to reflect their realities, such as multiple jobs and a lack of labor rights in practice. This analysis emphasizes the decoupling of workers’ experiences from stated organizational goals, emphasizing the importance of collecting workers’ perspectives. Taken together, these three essays contribute insights into how frontline workers shape organizational performance by interpreting organizational context, culture, and structure. Results indicate that organizational performance is not merely a function of workplace practices, but rather, directly influenced by frontline workers based on their individual motives and roles in workplace culture. These findings imply that by directly engaging with frontline workers’ motives, organizational leaders and policymakers can design organizations that improve work and performance.

Speech Therapy

Thu, 01 Feb 2024 00:00:00 GMT

Speech Therapy Hintikka, Kathleen Words can hurt. Most theorists who are working on speech and harm, even across disciplinary lines, agree to that much. There is less agreement, however, regarding the mechanisms by which speech causes or constitutes harm. By way of paying special attention to the dangers of ordinary, and to varying degrees, “socially acceptable" language, my dissertation, Speech Therapy, is a three-part exploration of the ways that speech plays significant roles in constructing and maintaining unjust conditions of domination and subordination both between social groups and within the broader society, even when its effects are unintended or go unnoticed. The first paper modifies a Gricean account of implicature to accommodate implicature in the interrogative mood. I then argue that interrogative implicature can help us make better sense of certain kinds of common microaggressions. In the second paper, I focus more explicitly on the kinds of harm and subordination that speech can cast on its targets even when the target is not around to hear it. I argue that all socially significant speech — speech about race, class, etc. — articulated from any standpoint, contain second-personal vocative hails. That is, all socially significant speech, even that not uttered second-personally, contains a second-personal norm that implicates both the speaker and members of the targeted social category, even when no member of the targeted social category is invited as an interlocutor by the speaker. The resulting view is a non-ideal, intersectional, and situated approach to second-personhood. The final paper is about how the things we do with language can alter the epistemic landscape of our communities. I argue that slurs, pejoratives, and misused epithets, a class of terms that I will refer to as demeaning speech, constitute a specific kind of epistemic oppression. My view is not that demeaning speech causes epistemic oppression, but rather that demeaning speech constitutes epistemic oppression. The oppression occurs in the mere uttering of these terms; the act of making it such that someone might have their testimony discredited in the future, of inflicting epistemic risk, is itself an epistemic injustice.

Expanding options for the mechanical characterization of biological materials

Sat, 01 Feb 2025 00:00:00 GMT

Expanding options for the mechanical characterization of biological materials Varner, Hannah Martin The mechanical properties of biological tissues change over time and with disease progression, and they provide important information regarding the limits a tissue can sustain before injury. Therefore, quantifying these properties in biological materials and their synthetic simulants could be instrumental for accurate medical diagnoses, treatment of disease, and prediction of traumatic injury survivability. Conventional methods of mechanical testing, such as uniaxial tension, compression, and nanoindentation, provide highly repeatable and reliable results for the stiff materials for which they were originally developed. However, the same cannot be said when these methods are applied to the characterization of soft and biological materials due to limitations of specimen size, fixturing capabilities, and sample preparation. Volume Controlled Cavity Expansion (VCCE) is a recently developed technique to measure local mechanical properties of soft materials in their natural environment. Through the highly controlled expansion of a fluid bubble at the tip of an injection needle, paired with simultaneous measurement of the resisting pressure, a local signature of a material's mechanical response can be obtained. This thesis presents the first systematic application of VCCE to biological materials. It begins by presenting a cautionary example of the limitations of soft material testing, focusing on the synthetic silicone and tissue simulant polydimethylsiloxane (PDMS). We find that the wide range of mechanical properties reported in literature are due to biases imparted by different testing methods. We then use VCCE to examine the elastic response of gelatin, whole blood clot and liver tissue, demonstrating with high repeatability that subtle mechanical changes occur within a matter of days as these tissues age. Finally, this work applies VCCE to investigate what happens to these materials after elastic expansion, and throughout a process of controlled damage. Biological materials are found to demonstrate toughening that does not appear in gelatin and PDMS. Because of these observed differences, we caution against using gelatin and PDMS for simulating the behavior of biological materials in extreme loading cases. Combining these findings, this thesis provides evidence that more widespread adoption of VCCE in mechanical testing would provide a path to better understanding of the mechanics of soft and biological materials, with implications in fundamental mechanics research as well has in biological and healthcare applications.

High Order Immersed Finite Difference Methods for Complex Domains with Moving Boundaries and Interfaces

Sat, 01 Feb 2025 00:00:00 GMT

High Order Immersed Finite Difference Methods for Complex Domains with Moving Boundaries and Interfaces Gabbard, James Moving domain boundaries and material interfaces are a hallmark of multiphysics systems such as fluid-structure interaction, alloy solidification, and multiphase flows. Simulating moving interfaces with traditional techniques requires a moving mesh that continuously adapts to the interface, which is costly and places restrictions on the interface motion. Immersed methods avoid these challenges by simulating moving geometries on a stationary Cartesian grid, locally altering the numerical method to account for boundaries and interfaces that are not grid-aligned. Most existing immersed methods have low-order spatial accuracy, requiring fine grids to generate accurate results. High order immersed methods can produce more accurate results at lower resolution, making them a promising tool for 3D simulations with tight error tolerances. However, the majority of available high order immersed methods have been numerical experiments developed for stationary 2D geometries and simple PDEs. In this thesis we demonstrate that high order immersed methods can be extended to complex nonlinear PDEs and moving 3D geometries, both of which are necessary to simulate practical engineering problems. We begin by introducing a boundary treatment that locally approximates PDE solutions with high order accuracy using a weighted least-squares fit, and show that the procedure remains valid for smooth 2D or 3D geometries satisfying a local curvature constraint. This boundary treatment is combined with a high order finite difference method to discretize the Poisson equation with up to sixth order accuracy. We then expand the scope of the method to include PDEs with immersed material interfaces, spatially-variable coefficients, vector-valued unknowns, cross-derivative terms, and nonlinearities. These techniques are applied to generate a sixth-order discretization of 2D nonlinear elasticity, demonstrating the applicability of high order immersed methods to complex PDE systems relevant in mechanical engineering. In the second half, we focus on large-scale 3D simulations with moving boundaries. We construct a third order immersed advection discretization with provable stability in one dimension, and show experimentally that the scheme remains stable in 2D and 3D domains. To treat moving boundaries, we introduce a general framework that allows high order immersed methods to maintain their accuracy in both space and time when paired with any explicit Runge-Kutta time integrator. We conclude by presenting results from massively-parallel high order simulations of the 3D advection-diffusion equation with moving boundaries on a multiresolution grid. Taken together, these results demonstrate that high order immersed methods can achieve the scale and complexity necessary to enable practical simulations that are difficult or impossible with traditional mesh-based techniques.

Floor Plan Design Collaborator: A Data-Driven Approach to Assist Human Architects in Design Exploration

Sat, 01 Feb 2025 00:00:00 GMT

Floor Plan Design Collaborator: A Data-Driven Approach to Assist Human Architects in Design Exploration Sung, Woongki After a long AI winter since the 1980s, artificial intelligence is now experiencing a renaissance due to enhanced computing power and access to vast amounts of data. Today, machines can talk, sing, and draw like human experts. Despite this progress, we are still far from the vision where human designers and AI collaboratively discuss and develop designs. This study argues that a data-driven approach holds great potential in the design process by quickly learning from existing examples and generating new alternatives for exploration. To support this claim, the study presents a generative framework that learns from existing examples and generates new designs. Specifically, the proposed framework employs Bayesian networks to encode site layout data and floor plan examples, generating new design examples through a Markov Chain Monte Carlo (MCMC) sampling procedure. Experiments on real-world examples demonstrate that the framework effectively summarizes the statistical information of given design examples and generates unseen examples based on the learned knowledge. The transparency of the data representation and the inner workings of the proposed framework facilitate an active feedback loop in the iterative learning and generation process between human designers and machines. Observations throughout the study reveal intrinsic limitations and potential improvements of contemporary optimization-based approaches from the perspective of both lateral and vertical design development.

Laboratory Astrophysics Studies of Magnetized Collisionless Shock Precursors and the ³He³He Proton Spectrum at the OMEGA Laser Facility

Sat, 01 Feb 2025 00:00:00 GMT

Laboratory Astrophysics Studies of Magnetized Collisionless Shock Precursors and the ³He³He Proton Spectrum at the OMEGA Laser Facility Johnson, Timothy Mark Laboratory astrophysics enables the study of astrophysical systems in the lab. There are broadly two types of laboratory astrophysics experiments: macrophysics and microphysics. Macrophysics experiments study a scaled down version of an astrophysical system while microphysics experiments create a small volume of matter with the same conditions as an astrophysical system. This thesis details work related to both macrophysics and microphysics laboratory astrophysics experiments. For the macrophysics contribution, collisionless shocks experiments were conducted at the OMEGA laser facility using the new gas jet platform. Collisionless shocks are shock waves formed through plasma processes when particle collisions are negligible. These shocks can form as bow shocks in the interaction between the solar wind and planetary ionospheres and can accelerate charge particles to high energies. In the experiment, a CH plasma flow collides with a hydrogen gas jet plasma to create a forming magnetized collisionless shock. Different diagnostics show a moving density jump, strong magnetic fields, and the acceleration of electrons. These observations coupled with magnetohydrodynamics and kinetic particle-in-cell simulations paint a complete physical picture of the forming shock in a configuration similar to the bow shock of Venus. Late time proton radiographs show a complicated structure which is studied for magnetic turbulence. Turbulence is important in several astrophysical systems, especially collisionless shocks where it dissipates shock kinetic energy and is essential for accelerating charged particles to cosmic ray energies. Magnetic power spectra extracted from proton radiography data show a break in the spectrum between the ion Larmor radius and the ion skin depth for high plasma β, a sign of kinetic turbulence. Large scale particle-in-cell simulations of high β turbulence also have this feature showing that the experimental data are consistent with high β kinetic turbulence. For the microphysics contribution, a new proton spectrometer is designed for measurements of the ³He³He proton spectrum. The ³He³He fusion reaction is the last step of the proton-proton I chain which produces the majority of the sun’s power. Previous experiments were not able to measure the ³He³He proton spectrum below 6 MeV. A new proton step range filter (SRF) spectrometer with a larger energy range is designed using a Monte Carlo tool. This tool uses Geant4 and is able to self-consistently apply the instrument response function. The new SRF design is validated and a method for analyzing experimental data using the Monte Carlo code is presented.

Shining a Light on the Nucleus: Photonuclear Measurements from Correlations to Charmonium

Sat, 01 Feb 2025 00:00:00 GMT

Shining a Light on the Nucleus: Photonuclear Measurements from Correlations to Charmonium Pybus, Jackson R. The atomic nucleus is comprised of a collection of nucleons (protons and neutrons), which are bound together by the nucleon-nucleon (NN) interaction that originates from Quantum Chromodynamics (QCD). While most nucleons experience the force from the rest of the nucleus as a single net “mean-field” interaction that binds them relatively weakly, a small but impactful fraction are in configurations called “Short-Range Correlations” (SRCs), in which they pair with another nucleon at very short distance to experience strong interactions, significant binding, and high momentum. Hard, high-energy scattering reactions in which an SRC pair is broken apart, knocking both nucleons out of the nucleus, provide the ability to probe the details of these SRC configurations in the nucleus. Previous measurements have had limited statistics and kinematic reach, and the theoretical tools available were insufficient to draw quantitative conclusions regarding the ground-state properties of SRCs. The studies described in this thesis represent the first global analysis of SRC breakup measurements in order to present a unified picture of SRCs within light- to medium-size nuclei. This includes the use of a novel theoretical framework, the Generalized Contact Formalism, which connects scattering cross-section measurements and the ground-state properties of the SRC pair, to quantitatively interpret a variety of electron-scattering measurements. This is brought to culmination by a report on the first measurement of SRC pairs via the use of hard meson photoproduction reactions, which, despite differing significantly from the mechanics of electron-scattering, is well-described under a common framework, pointing to a consistent and universal picture of SRCs across reaction channels. I also report on the first measurement of J/ψ photoproduction in the near- and below-threshold kinematic region, giving the first insights to the gluonic structure of bound nucleons in the large-x “valence” region and providing constraints on a gluonic “EMC effect”. In addition to these studies, I provide details on the search for Primakoff production of axion-like particles using the photoproduction data taken for this experiment, and I conclude by describing studies of nucleon spin structure measurements that will be performed at the forthcoming U.S. Electron-Ion Collider.

Acoustic scattering of spherical directional waves by smooth and statistically rough solid elastic cylinders

Sat, 01 Feb 2025 00:00:00 GMT

Acoustic scattering of spherical directional waves by smooth and statistically rough solid elastic cylinders Mursaline, Miad Al Realistic sonars radiate spherically spreading waves and have directivity. Therefore, they insonify a target over a finite number of Fresnel zones and span a continuum of oblique incident angles, even when the center of the beam is at normal incidence. These effects strongly influence both the overall scattered pressure levels and resonances. For example, because of the spreading of the beam and associated oblique insonification within the beam, normal modes associated with axially propagating guided waves are excited that would not have otherwise existed for an idealized incident plane wave. This thesis analyzes acoustic scattering by solid elastic cylinders insonified by realistic sonars both theoretically and experimentally. A theoretical model to predict scattering by arbitrary-length cylinders is derived based on the apparent volume flow accounting for the above-mentioned practical sonar properties, namely, spherical spreading and directionality. The formulation is first bench-marked against the formally exact T-matrix solution and tested against previously published laboratory data for finite cylinders. It is found that the formulation outperforms the T-matrix solution in predicting laboratory observations at near-normal incidence. Laboratory experiments are then conducted on arbitrary length smooth cylinders insonified by a directional sonar, with a small number of Fresnel zone excited, to evaluate the theory for monostatic as well as bistatic geometries. The formulation is found to outperform the classical scattering models in predicting the new measurements. For example, resonances associated with axially propagating guided waves excited at broadside incidence observed in the experiments are predicted by the proposed formulation but not by the classical models. The measurements are found to agree well with predictions in terms of overall scattering levels and resonance locations. In addition to testing the predictions, the bistatic laboratory observations presented herein substantiate the significant effects on scattering due to the properties of the incident field from practical sonars. The comparison between theoretical and experimental results is then extended for the more complex case involving statistically rough elastic cylinders with one-dimensional Gaussian roughness. The roughness is found to have a considerable impact on all aspects of scattering—overall levels as well as locations and shapes of resonances. General agreement is found between the theoretically predicted and measured ensemble averaged scattered pressure. Both the theory and data reveal two main observations in the ensemble-averaged scattered field: overall scattered pressure levels are seen to decrease, and resonance effects are diminished compared to the corresponding case of smooth cylinders. Effect of various statistical properties of the rough cylinder, namely, different root mean square (RMS) roughness for fixed correlation length and different correlation lengths for fixed RMS roughness on the scattered field are investigated. Finally, the fluctuations of the scattered field are analyzed using the derived formulation.

More than the sum of parts: deconstructing tissues in their spatial, temporal, and environmental contexts

Sat, 01 Feb 2025 00:00:00 GMT

More than the sum of parts: deconstructing tissues in their spatial, temporal, and environmental contexts Tzouanas, Constantine The human body is composed of ~37,000,000,000,000 cells, exquisitely organized into tissues delivering emergent functions beyond individual cells’ capabilities (e.g., the brain’s seemingly-effortless computations, the liver’s wide-ranging chemical processing). In my PhD, I studied how healthy tissues arise from properties and interactions of constituent cells, and how disease outcomes stem from dysregulation of underlying cellular parts. 1) To study how cells’ spatial organization shapes tissue function, I created photochemistry tools to discover gradients in how immune cells combat cancer across a tumor’s core vs. periphery. 2) To then explore spatially-structured tissues, I turned to tuberculosis (TB) granulomas: just centimeters apart, the immune system can kill bacteria in one granuloma or permit years-long bacterial survival in another. Reconciling this paradox, I discovered that bacterial killing needs coordinated signaling across immune cells, but TB-permissive granulomas structurally remodel to inhibit TB spread at the expense of “walling out” immune cells. 3) Connecting disease to lifestyle exposures, I determined tobacco smoking increases TB risk via blood-to-lung migration of TB-permissive cells. 4) Intrigued by past stresses seeding future dysfunction, I studied similar themes in adaptations to high-fat diets, discovering tradeoffs where individual liver cells promote their own survival at the expense of reduced tissue function and increased cancer risk. Through these studies, I dissected tissues and diseases with unprecedented resolution via single-cell multi-omics and mechanistic perturbations, defining the parts, interactions, and causal regulators that underlie tissue (dys)function.

Design and Manufacture of a Modular Continuous Unit Dose Pharmaceutical Lyophilizer

Sat, 01 Feb 2025 00:00:00 GMT

Design and Manufacture of a Modular Continuous Unit Dose Pharmaceutical Lyophilizer Burcat, Steven Pharmaceutical lyophilization (freeze-drying) enables long term storage and simplified transportation for aqueous vaccines and protein formulations. Modern industrial pharmaceutical freeze-driers rely on large batch and open loop formulation processing, limiting supply chains and resulting in variable quality products. This work describes the design and manufacture of a modular continuous lyophilization machine for pharmaceutical production. Additionally, the scaling and design methodology outlined in this work enables the development of both smaller systems for laboratory testing and larger machines to fit the needs and requirements of individual facilities. This machine introduces three new technologies to the pharmaceutical freeze-drying process. The first innovation is a continuous flow lyophilization topology which separates the lyophilization steps spatially rather than temporally. This layout allows product to travel through the system in smaller batches for increased product uniformity and quality control. The second innovation is a weight-based sensor for monitoring residual water content. This sensor enables in-situ monitoring of product during sublimation, and it resolves mass measurements as small as 5mg. The third innovation is the implementation of a thermal shock method of inducing controlled nucleation. The convective cooling and spatial non-uniformity within the machine allow vials to experience a 40°C temperature drop in less than 30 seconds. This nucleation front starts on the vial walls, rather than at the top surface of the solution in the vial, potentially increasing the water sublimation rate during drying compared to current nucleation methods. The machine designed and built for this work integrates into modern factory processes and can be scaled from the lab bench to a production line. The manufactured prototype demonstrates improvements on the production rate, flexibility, and quality of existing machines.

Synthesis and perception of sounds from physical interactions reveals auditory intuitive physics

Sat, 01 Feb 2025 00:00:00 GMT

Synthesis and perception of sounds from physical interactions reveals auditory intuitive physics Agarwal, Vinayak Object interactions – collisions, scraping and rolling – create many of the sounds that we hear in the world around us. These sounds are generated via lawful physical dynamics. Anecdotally, humans possess some intuitive knowledge of the physical generative processes underlying sound production, but little is known about the extent and nature of this knowledge. This thesis characterizes the auditory perception of physical object interactions, making three main contributions. First, we develop realistic contact sound synthesis tools, in part via large-scale measurements of object acoustics. Second, we show that humans solve ill-posed problem of inferring of object mass and damping by using internalized knowledge of the distribution of object resonances. Third, we provide evidence for “auditory intuitive physics” in which human listeners derive physical information through sound, maintain it over time in object representations, and compare it across sensory modalities.

Planning Beyond Crisis: The Promise of Insurgent Planning in Post-Disaster Mocoa

Sat, 01 Feb 2025 00:00:00 GMT

Planning Beyond Crisis: The Promise of Insurgent Planning in Post-Disaster Mocoa Osorio Botero, Juan Camilo During the evening of March 31, 2017, a catastrophic landslide engulfed the Colombian city of Mocoa, killing at least 335 people in roughly thirty minutes. Seventy others disappeared, and over one hundred people were reported injured across 48 neighborhoods, and roughly 1,500 housing units were destroyed. With a total of 22,000 people impacted, this catastrophe was the deadliest disaster affecting Colombia in recent decades. Yet, despite an alignment of major national political commitments, international cooperation, and a multi-million-dollar humanitarian budget, reconstruction plans have not been completed seven years later. Why? As the first comprehensive analysis of the landslide and its aftermath, this dissertation is a novel investigation into the competing forces that ultimately canceled the central reconstruction plan, demonstrating that the kind of disruption caused by the disaster mobilized new actors and new forms of agency. In contrast to the popular perception that this kind of lack of remediation suggests the failure of urban governance, the dissertation speaks to the success of activists who have neutralized the government’s reconstruction plan, which activists perceived as worsening the circumstances leading up to both the catastrophe and recovery. Distinguishing between the “landslide” and the “larger disaster,” the dissertation further explains the government’s proposed reconstruction plan within a history of violent extraction, dispossession and displacement. Framing an original case consisting of fifteen planning vignettes to trace actions, reactions and counteractions, I expose the reduction of the planning process as crisis urbanism. My research contributes to our understanding of variability among insurgent planning actors and their invented spaces for engagement in the context of disaster, by defining technocratic resistance as a valid form of dissent inside the government, and by proposing a new device for the study of insurgent planning called transformative spaces enabling local community’s right to plan. Drawing on contemporary debates on anti-crisis, risk and decolonial thought, the dissertation imagines an alternative paradigm for planning beyond crisis that enables radical community action through dissenting grassroots leadership. Keywords: crisis urbanism, technocratic resistance, insurgent planning, regenerative planning, anti-crisis, risk, decolonial thought

An Identity-Oriented Systems Engineering Framework for Complex Sociotechnical Systems: A case study of Zero Robotics

Sat, 01 Feb 2025 00:00:00 GMT

An Identity-Oriented Systems Engineering Framework for Complex Sociotechnical Systems: A case study of Zero Robotics Zhang, Yiyun Historical and ongoing discrimination of certain identity groups such as by racial, gender, social class, and other differences leads to persistent inequalities in various fields of society including socioeconomic, health system, political powers, education opportunities, etc. Technology however often entrenches or sustains the hierarchies and further strengthens these social inequalities. While there are many frameworks for studying complex systems, a framework with a focus on advancing social justice and an integration of technological and social considerations is missing. This work introduces the Intersectional Antiracist Technology Framework as a new tool and applies it to an existing complex system of Zero Robotics in STEM education. STEM education, with increasing importance in the modern world’s competitions, is one of the most popular methods to cultivate students’ interests and capabilities in solving complex problems. However, the disparities in access to quality STEM learning opportunities and inclusion in STEM activities remain significant challenges in promoting social equality. This work builds upon the systems engineering tools and uses the innovative Intersectional Antiracist Technology Framework to describe, explain, and evaluate an existing complex system of Zero Robotics. Zero Robotics is an education outreach program that is designed as an early intervention to enroll students in aerospace and related fields. The program aims to serve students across the pipeline and provide them with learning opportunities through interactions with a space robot. It is a perfect example of a complex sociotechnical system that has technological and social factors. Through the case study of Zero Robotics, data are collected through interviews, surveys, participant observation, and available documents. Qualitative program outcomes are assessed from student surveys before and after the Zero Robotics competition. This work is the first attempt to apply the Intersectional Antiracist Technology Framework to an existing complex system that is being managed by the author. The findings from this study demonstrate insights that can be gained about complex, sociotechnical systems by viewing them from multiple Stakeholder perspectives and blending the information about the technical and social design aspects.

thesis in the field of Chemical Oceanography: Marine iodine biogeochemistry: inorganic speciation, redox dynamics and organic complexation

Sat, 01 Feb 2025 00:00:00 GMT

thesis in the field of Chemical Oceanography: Marine iodine biogeochemistry: inorganic speciation, redox dynamics and organic complexation Ștreangă, Iulia-Mădălina Iodine holds significant importance across various disciplines, including medicine, industrial processes, organic synthesis, paleoclimatology, atmospheric chemistry and modern climate science. The ocean, as a major surficial iodine reservoir and the primary source of this element to the atmosphere, plays a central role in global iodine cycling. Despite significant progress, key aspects of iodine cycling in the marine environment remain poorly understood. This thesis leverages recent advances in high-precision techniques, including liquid chromatography and mass spectrometry, to enhance our understanding of marine iodine biogeochemistry. Detailed analyses of the major inorganic iodine species in seawater, iodide and iodate, were conducted in the oligotrophic waters of the North Pacific and the oxygen minimum zones of the Eastern Tropical Pacific. The observed distributions reflect the impact of both in situ and ex situ processes on dissolved iodine concentrations, offering valuable insights into the prevalence and extent of anoxic conditions within oxygen minimum zones. Iodate formation rates were investigated through surface seawater incubations using iodide-129, a long-lived radioisotope, as a tracer. The experimental results underscore the pivotal role of particles in mediating redox transformations between iodide and iodate, while also emphasizing the significance of iodine species with intermediate oxidation states in these processes. Building on this observation, a significant focus of this thesis is the characterization of dissolved organic iodine in the ocean. Two innovative methodologies for identifying dissolved organic iodine compounds are presented. The first approach focuses on labelling cultures of the cyanobacterium Synechococcus with iodide-129 to generate a diagnostic isotopic pattern in resultant dissolved organic iodine complexes. The second approach employs sequential purification and isolation of a target compound from a large-volume seawater sample collected in the North Pacific. Collectively, the findings presented in this thesis significantly enhance our understanding of iodine cycling in the marine environment, offering novel insights into the distribution and composition of both inorganic and organic iodine, as well as the rates and dependencies governing iodine cycling processes. Furthermore, the methodologies introduced here pave the way for future research to elucidate the mechanisms driving iodine redox transformations in seawater, refine the marine distribution of inorganic iodine, and advance the molecular characterization of dissolved organic iodine.

Leveraging Right and Left Ventricular Coupling for Optimization of Mechanical Circulatory Support

Sat, 01 Feb 2025 00:00:00 GMT

Leveraging Right and Left Ventricular Coupling for Optimization of Mechanical Circulatory Support Lamberti, Kimberly Kate Mechanical circulatory support devices have the potential for profound impact on cardiogenic shock patients. They enable volume propulsion and pressure gradient generation first by unloading and later by decoupling native cardio-vascular interactions, which reduces cardiac load and energy consumption while increasing organ perfusion in the face of disease. However, there is a potential price in that coupling evolved to optimize blood flow dynamics and the complex interplay between individual cardiovascular components and interposing organs like the lung. Disrupting native coupling with mechanical support risks decompensation if the heart and lung cannot tolerate these changes. One particularly concerning consequence of altered coupling is that upwards of 40% of patients with left-sided mechanical support face ensuing right heart failure, which requires urgent action and often is associated with even higher mortality rates. We hypothesized that better understanding of right heart function and the mechanisms of right heart (in)tolerance to left-sided support will improve device utility by aiding device selection as well as titration throughout a patient’s clinical course. In particular, we focused on right and left ventricular coupling, which consists of serial coupling across the closed-loop cardiovascular circuit, and parallel coupling that enables intracardiac interdependence and force transmission between the ventricles. Each interaction plays a critical role in a patient’s tolerance to mechanical support and optimal setpoint. We used a series of controlled porcine experiments to evaluate right and left heart coupling during mechanical support. In each set of experiments, we induced graded models of disease that range from health to progressive impairment, enabling evaluation of mechanical support across a spectrum of right and left heart states. Through these studies, we improved mechanistic understanding of the differences between right and left heart function, and how those differences dictate the response to left-sided support. Specifically, we found that pulmonary vascular compliance enabled a unique right heart adaptability to varied flow, but limitations in compliance due to disease yield right heart intolerance to support. We leveraged the indwelling pump to dynamically alter load in the system, creating a method to rapidly evaluate pulmonary vascular compliance adaptability and therefore predict the need for right-sided support. Finally, we created a metric using device-organ interactions for tracking right-left coupling over time, which can aid optimization of device speed based on relative right and left ventricular volume setpoints. Translation of these findings to the clinic could better inform use of mechanical circulatory support technologies with the goal of improving outcomes for cardiogenic shock patients.

Investigation of Long-timescale Behavior of Positive DC Streamer Coronas

Sat, 01 Feb 2025 00:00:00 GMT

Investigation of Long-timescale Behavior of Positive DC Streamer Coronas Strobel, Lee R. Positive DC streamers are filamentary low-temperature discharges that are relevant to many applications, including sterilization, ionic wind generation, agriculture and atmospheric electricity. Even when excited by a DC voltage, streamers in atmospheric-pressure air typically self-pulsate with a frequency of several kilohertz. The generally-accepted explanation for DC streamer self-pulsation is that it is driven by recovery of the electric field near the tipped anode, due to electrostatic removal of ionic space charge from the inter-electrode gap over inter-pulse timescales. However, this theory has not been validated, either experimentally or numerically. Most prior works investigating DC streamers have focused on the streamer propagation phase (a few tens of nanoseconds) - few have investigated longer timescales, including the bridging of the electrode gap by the streamer and the subsequent current pulse (hundreds of nanoseconds) and the period in-between streamer pulses, leading up to initiation of the next streamer discharge (hundreds of microseconds). The work presented in this thesis focuses on investigation of the longer timescales of positive DC streamer development in a tip-to-plane geometry, in particular beyond the streamer propagation phase, through the current flow and inter-pulse phases. This begins with an experimental study to measure the long-timescale development of the electric field inside a streamer corona using the E-FISH laser diagnostic technique. This shows some surprising results, which do not seem to be consistent with the theory of DC streamer selfpulsation being driven by electric field recovery at the anode. The near-anode electric field is not observed to recover during the inter-pulse period - instead, the near anode behavior seems to be dominated by a persistent glow discharge and a curious wave-like feature is observed in the electric field, traveling towards the anode on ionic timescales. This is followed by the development of a 1.5D reduced-order numerical model of a DC streamer, which is optimized for solving over long timescales via a ‘triple-stack’ of transient solvers. The model is able to fully resolve the boundary sheath layers of the plasma and is able to capture detailed behavior of the cathode sheath development during bridging via the use of a kinetic flux boundary condition for the charged species. This model is firstly applied to modeling the bridging and current flow phases of streamer development, and its prediction shows a good qualitative match to the behavior of the experimental current pulse. Parameter sweeps show that the streamer current pulse is sensitive to the assumed radial behavior and the rate of electron-ion recombination, but insensitive to the applied boundary conditions or secondary emission. The final section describes an extension of the 1.5D streamer model to simulate the streamer inter-pulse phase and initiation of a second streamer. It is shown that initiation of a second streamer can be predicted by a fluid model and that radial expansion of positive ions plays an important role; however, it has proven difficult to integrate that effect into the 1.5D model. The model results are consistent with streamer self-pulsation being due to electric field recovery; however, comparison with the results of the E-FISH experiment suggest there may be different mechanisms driving positive DC streamer self-pulsation, depending on the presence or not of a glow discharge on the anode.

Computational Modeling of Biological Function

Sat, 01 Feb 2025 00:00:00 GMT

Computational Modeling of Biological Function Khodaee, Farhan How biological function emerges from complex molecular patterns is a fundamental question in biology. Addressing this question requires a deep exploration of the concepts of genotype and phenotype, which serve as the foundation of this inquiry. This dissertation focuses on providing a quantitative approach through the lens of computation to dissect the dynamic relationship between genotype and phenotype. In particular, recent advancements in high-content genotyping methods, such as genome-wide association studies (GWAS) and single-cell RNA sequencing, have provided powerful tools for mapping the molecular basis of biological function, but also have introduced challenges due to the high dimensionality, vast combinatorial possibilities, and multimodal characteristics of the data. The overarching goal of this dissertation is first to provide a critical discussion on the theories of genotype and phenotype as they relate to biological function and propose new methods to map their relationship. Specifically, we present the integrated genetics framework designed to analyze and interpret the manifold of genotypes and their associated phenotypes simultaneously. We applied this approach to develop a multimodal foundation model for human transcriptomics at the cellular level. To further test the capabilities of this method, we apply it to dissect the aging process. The results of this study provide novel concepts and methods for analyzing the genetic data along with phenotypic information with higher resolution. Moreover, the results shed light on uncovered potential cross-tissue biomarkers that are undetectable through conventional gene expression analysis alone. Overall, this study aims to advance our understanding of the dynamic interplay between gene patterns and phenotypic manifestation and demonstrates the potential of computational modeling in uncovering new dimensions of cellular function and complexity.

Tracking carbon fluxes across ocean interfaces using dissolved gas observations

Sat, 01 Feb 2025 00:00:00 GMT

Tracking carbon fluxes across ocean interfaces using dissolved gas observations Traylor, Shawnee Nicole The cycling and exchange of carbon between Earth’s systems play a pivotal role in regulating climate, yet two major carbon fluxes remain poorly constrained: the biological carbon pump (BCP) and carbon release from Arctic permafrost. This thesis focuses on dissolved gases as tracers and drivers of these processes through both autonomous and field-based observations. It encompasses (i) improvements to sensor-based measurements of O₂, (ii) the use of these measurements to assess the strength of the BCP in two distinct export regimes, and (iii) isotopic approaches to carbon dioxide (CO₂) and methane (CH₄) dynamics at a coastal permafrost site. The first part of the thesis is centered around the NASA EXPORTS campaign and studies the BCP at two contrasting field sites. Using autonomous platforms, carbon export was evaluated at both sites and demonstrated that at the lower productivity site, a greater proportion of fixed carbon was routed to sinking particulate organic carbon (POC), while the higher productivity site resulted in near equal proportions of dissolved organic carbon production and sinking POC. These findings underscore the value of autonomous sensors in capturing spatial and temporal variability in oceanic carbon cycling. The second part of this thesis shifts focus to the Arctic, where rapid warming threatens to mobilize vast (~1,500 Pg) amounts of carbon currently stored in permafrost. This study presents observations from the spring thaw at a coastal Arctic site and demonstrated that even sites with high CH₄ and CO₂ concentrations drew less than 10% of their carbon source from ancient permafrost sources. The variability in CH₄ and CO₂ emissions reflects the complex interplay between hydrological changes, primary productivity, and microbial processes. The research highlights the need for regular monitoring of Arctic rivers, which integrate changes in the terrestrial system, as a potential early warning system for abrupt permafrost thaw. This thesis leverages the fundamentals of dissolved gas geochemistry to examine key climate-relevant biogeochemical cycles across diverse environments that are sensitive to global change. These insights contribute to refining Earth system models and emphasize the need for expanded monitoring to predict future shifts in global carbon cycling and climate dynamics.

A Lagrangian perspective of mesoscale biophysical interactions in the subtropical ocean

Sat, 01 Feb 2025 00:00:00 GMT

A Lagrangian perspective of mesoscale biophysical interactions in the subtropical ocean Jones-Kellett, Alexandra E. The most kinetic energy in the ocean is at the mesoscale, which includes highly dynamic physical perturbations that persist for months, a biologically relevant timescale for phytoplankton growth and bloom development. Importantly, mesoscale currents and the associated biological responses (i.e., biophysical interactions) are not spatiotemporally static, so they are difficult to characterize. In this thesis, we interpret phytoplankton observations in an objective Lagrangian manner, or with a frame of reference that follows the motion of water parcels experienced by drifting organisms. We build a Lagrangian coherent eddy tracking algorithm that identifies the boundaries of water masses trapped for a month or longer. Using this tool, we assess the variability of the lateral advective properties of eddies across the North Pacific Subtropical Gyre, finding that only half of the remotely sensed eddies identified from the traditional, Eulerian sea level anomaly method trap waters for these timescales. We then statistically compare satellite-observed chlorophyll-a anomalies associated with eddies that trap versus mix across their boundaries. Lagrangian coherent vortices have more anomalous biological signatures in the gyre, so we argue that the role of leaky eddies in altering biogeochemistry may be underestimated due to lateral dilution. We also highlight substantial regional and seasonal variability in the dominant biophysical interactions within the oligotrophic regime, helping to explain inconsistencies of in situ eddy observations across this region. Lastly, we show how the Lagrangian water mass histories of in situ samples shape the phytoplankton community in the open ocean, quantified with amplicon sequencing and internal genomic standards. In non-eddy waters, we found that cyanobacteria are advantaged over eukaryotic phytoplankton when lateral mixing is minimized for several months. In or near mesoscale eddies, where vertical perturbations are a source of new nutrients, eukaryotic phytoplankton gene abundance has no dependence on the lateral mixing histories. The results suggest dispersal and niche generation drive phytoplankton variability but in different ways in and outside eddies. This thesis emphasizes how Lagrangian tools reveal mesoscale structures (otherwise invisible with Eulerian reference frames) that trap, transport, and transform ecosystems, generating phytoplankton patchiness and variability in the surface ocean.

Addressing Challenges in Object-Based Robot Navigation and Mapping

Sat, 01 Feb 2025 00:00:00 GMT

Addressing Challenges in Object-Based Robot Navigation and Mapping Lu, Ziqi Developing fully autonomous systems that can safely traverse and interact with the environment has been a long-term objective in robotics. Many relevant tasks, such as planning and mobile manipulation, require the robot to possess an object-level understanding of the ambient world. In particular, it would be crucial to maintain a globally consistent objectbased map of the environment for these operations. Without external assistance – such as a prior map or a motion capture system – the robot needs to navigate and map the environment using an object-based SLAM system. This thesis is dedicated to addressing several key challenges in developing object SLAM systems. The first challenge arises from the ambiguity of object poses in single-view observations. When an object is observed from a single vantage point, it can often have multiple probable poses due to symmetry, occlusion, or perceptual failures. It would be difficult for an object SLAM system to incorporate such ambiguous measurements. To address this issue, we introduce an ambiguity-aware object SLAM method. We use Gaussian max-mixture models to represent and efficiently track the multiple object pose hypotheses, and gradually disambiguate the poses to construct a globally consistent object-level map. The second challenge is the performance degradation of neural networks when deployed in novel robot operating environments, commonly known as the domain gap problem. Specifically, when a pre-trained 6DoF object pose estimator is used in a novel environment, its pose predictions are often corrupted by outliers, and quantifying their uncertainties becomes difficult. Using these noisy predictions with unmodeled uncertainties as measurements in an object SLAM system can lead to significant estimation errors. To mitigate the problem, we propose a SLAM-supported self-training pipeline for domain adaptation of 6DoF object pose estimators. We exploit robust pose graph optimization (PGO) results to pseudo-label robot-collected images and fine-tune 6D object pose estimators. In particular, we develop an Automatic Covariance Tuning (ACT) method to model pose prediction uncertainties automatically during the PGO process. The third challenge is environmental changes. As changes occur in the scene, such as object insertion, removal, or rearrangement, the robot needs to efficiently detect these changes and update the map accordingly. While detecting and reflecting scene changes is relatively straightforward with handcrafted map representations like point clouds or voxels, it becomes significantly more difficult with learned radiance-field-based scene representations, such as Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) models. In this thesis, we develop a radiance-field-based 3D change detection method to identify 3D object-level scene changes. Our approach can rapidly detect object changes in cluttered environments represented with radiance field models from as few as a single post-change image observation. We also develop efficient update methods for NeRF and 3DGS models to reflect physical object rearrangements, guided by sparse post-change images. By addressing these challenges, this thesis advances the robustness and adaptability of object SLAM systems in real-world environments, paving the way for more reliable and autonomous robotic systems capable of complex interactions with the environment.

Essays on Sustainability in Agriculture and Food Systems

Sat, 01 Feb 2025 00:00:00 GMT

Essays on Sustainability in Agriculture and Food Systems Liu, Xinming Agriculture and food systems face severe challenges from climate change, population growth, and food insecurity. These unprecedented issues leave millions vulnerable to hunger and malnutrition, underscoring the urgent need for a transition toward sustainable agriculture and food systems. The first research stream in this thesis focuses on promoting sustainability in agriculture, particularly through contract farming. In Chapter 2, we model contract farming as a bi-level optimization problem for a farmer and a company. We analytically demonstrate that different contract structures offer varying incentives for farmers to invest in quality-improving efforts, resulting in different levels of quality for agricultural products. Empirical analysis of production-level data supports these model predictions. The second research stream examines sustainability in food systems, specifically addressing the issue of food waste. In Chapter 3, we explore the impact of online grocery shopping on household food waste. Using large-scale Nielsen Consumer Panel data and instrumental variable analysis, we establish a statistically significant causal relationship, showing that households with higher frequency of online grocery shopping experience lower waste per capita, a proxy of household food waste. These findings emphasize the role of digital platforms in fostering sustainable consumption and call for continued support for online grocery shopping to mitigate consumer-level food waste. In Chapter 4, we turn to retail-level food waste. We design and implement behavioral interventions aimed at reducing food waste in restaurant kitchens in Ghana. As a Sub-Saharan African country, Ghana faces both food waste and food insecurity. Through a six-week field experiment and a difference-in-differences analysis, we demonstrate that interventions focused on public- and private-interest lead to 9% and 19% reductions in food waste in kitchens, respectively. Follow-up surveys and further analyses reveal that this result may be related to the demographic/socioeconomic characteristics of workers (e.g., age and income), their perception of power distance within the management hierarchy, and their satisfaction with restaurant management.

Freight Distribution During Disasters: Measuring and Improving Operational Performance of Critical Systems

Sat, 01 Feb 2025 00:00:00 GMT

Freight Distribution During Disasters: Measuring and Improving Operational Performance of Critical Systems Rana, Shraddha The frequency and intensity of weather-related natural disasters have increased in the last őve decades. Moreover, the US faces more than a third of the disaster-related economic losses globally, majority of which are from storms. As the demand for distribution of essential freight increases during disasters, the physical and operational constraints decrease the capacity of the freight distribution systems. Accordingly, public and private-sector stakeholders seek disaster preparedness and response interventions to ensure timely and economic distribution of vital freight to the population in need. The goal of this thesis is to facilitate better strategic and tactical planning that results in higher operational performance of essential freight distribution systems during disasters. We study two critical freight distribution systems, namely, downstream fuel distribution and full truckload transportation of general freight. Truckload transportation plays a vital role in distributing relief supplies during emergencies, and fuel is required for humanitarian operations such as running generators, moving emergency response crews, and evacuation of the affected population. We collaborate with The US Federal Emergency Management Agency in response to multiple North-Atlantic storms and measure the operational performance of these systems under regular and disaster conditions, as well as identify public and private-sector interventions to make the performance better during future disasters. Our research contributes to the bodies of disaster modeling and management, fuel distribution, service procurement, and truckload procurement literature by, i) creating system level understanding of multi-server tandem cyclic queues with time-limited customers, ii) studying process improvement interventions for disasters, iii) quantifying the magnitude, geographical extent, timing, and duration of the causal effects of disaster conditions and consequent disaster relief activities on transportation procurement prices, iv) using datadriven analysis to design ŕexible truckload contracts that consider uncertainty in demand, and v) modeling dynamic-pricing where the buyer offers the price to service providers. In this thesis, we provide several actionable insights for public and private-sector stakeholders to manage freight distribution during future disasters. We identify which process improvement interventions are best suited for which type of downstream fuel distribution system, and which storage terminals should be prioritized under limited budget. We also measure how private-sector shippers should account for changes in truckload spot procurement prices during disaster episodes to manage their budgets and operational decisions. Moreover, we offer an alternate dynamic-priced truckload contract solution for public-sector shippers that deal with uncertain episodic demand in response to disasters. We demonstrate the impact of our research by implementing it to multiple real-life case studies in the US. Furthermore, our methodologies and results are generalizable to other geographical regions as well as other disaster conditions. Thus, we hope that they are used by public and private-sector actors to better manage essential freight distribution moving forward.

Natural Language Foundation Models in Medical Artificial Intelligence

Sat, 01 Feb 2025 00:00:00 GMT

Natural Language Foundation Models in Medical Artificial Intelligence Palepu, Anil Over the past decade, the transformative rise of deep learning, particularly large language models (LLMs), has inspired experts across diverse fields, including healthcare, to think deeply about how artificial intelligence (AI) can revolutionize their fields. In this time, general foundation models, rather than narrow and highly specialized task-specific systems, have begun to emerge as the dominant paradigm. In healthcare, AI systems are already seeing widespread implementation in a variety of real-world use cases, perhaps without adequate evaluation and validation. Indeed, their often impressive ability to process natural language, a crucial medium of knowledge and communication in medicine, suggests that many of these modern foundation models may hold immense promise in the healthcare space. However, there exists a need to better study and understand their strengths, limitations, and robustness, particularly in more realistic and clinically relevant settings. This thesis focuses on two key classes of natural language-driven foundation models --- Contrastive Language Image Pretraining (CLIP) models, and Large Language Models (LLMs) --- and investigates how such models can encode and deliver useful clinical knowledge, for tasks like chest x-ray interpretation, differential diagnosis, history taking, and clinical management. As a whole, this thesis aims to further our collective understanding of the potential of natural language foundation models in medicine, while emphasizing the need for significant further research to address real-world challenges and understand the scopes in which such systems can be implemented safely and efficaciously. In the first chapter, I provide an overview of some relevant background, including contrastive language-image pretrained models, large language models, and their evaluation in the medical domain. In chapter 2, we improve the CLIP architecture for chest x-ray interpretation through a novel regularization technique applied during pre-training, and use this model for the zero-shot identification of chest x-ray findings. In chapter 3, we examine the reliability of CLIP-style models. First, we evaluate their robustness to shortcut learning to understand the potential protective effects of text self-supervision. Next, we explore how conformal prediction can be used to control zero-shot classification performance and preempt compatible inputs for these CLIP-style models. In chapter 4, I describe the development of Articulate Medical Intelligence Explorer (AMIE), a conversational diagnostic AI fine-tuned with simulated medical dialogue. We evaluate the diagnostic capabilities of AMIE in two randomized studies with primary care physicians; first, in challenging clinicopathological conference (CPC) cases, and then in virtual text-based objective structured clinical examinations (OSCE). In chapter 5, we explore AMIE's management reasoning capabilities in two subspecialty domains: genetic cardiovascular disease and breast oncology. In these studies, we design domain-specific assessments for case management and compare AMIE's performance to generalists under subspecialist evaluation, as well as studying its potential assistive effect.

Persian Lessons: Islamic Art in America, circa 1876–1925

Sat, 01 Feb 2025 00:00:00 GMT

Persian Lessons: Islamic Art in America, circa 1876–1925 Goldberg, Roxanne This dissertation investigates the prehistory of academic Islamic art history in the United States through the lens of American cultural history. It shows that between the US Centennial in 1876 and the inauguration of the Pahlavi dynasty in Iran in 1925, aesthetic theory and American citizenship were debated in the United States through objects identified, regardless of actual provenance, as “Persian.” This cultural phenomenon coincided with the acceleration of the transnational market for Islamic art, including architectural tiles, single-page paintings, and hand-knotted pile carpets. Examining instances of collecting, classifying, displaying, and otherwise handling and beholding Islamic art within different scales of home (family, nation, and international Christianity) and spaces of pedagogy (the living room, commercial gallery, advertisement, schoolroom, voluntary association, museum, and world’s fair), "Persian Lessons" reveals that notions of Persian art were instrumentalized in the service of competing American identities and ideologies in the late nineteenth and early twentieth centuries. Through an analysis of published writings, museum archives, and government documents, the study shows how the art critic S. G. W. Benjamin, who also served as the first US diplomat to Iran in 1883–85, constructed an ideal of the Persian artist to champion liberal individualism and public art education. An investigation into the presence of Muslim prayer carpets in American Christian homes reveals that Sarkis Nahigian and other diasporic entrepreneurs from the Ottoman Empire became partners to middle-class women, who jointly turned the Oriental carpet into a symbol of obligation to the American nation. Lastly, an examination of visual and textual evidence recasts a collection of more than 20,000 objects—given to the Museum of Fine Arts, Boston, and William Hayes Fogg Art Museum of Harvard University by design pedagogue and museum patron-administrator Denman Waldo Ross between 1888 and 1935—as a tool of “training for citizenship.” Ross regarded Persian textiles and single-page paintings as value-neutral objects for the design education that he believed bolstered participatory democracy. The fifty-year history that this dissertation covers concludes in the late 1920s and '30s with the establishment of the first official positions in Islamic art history at universities and museums in the United States. "Persian Lessons" thus shows that the founding of Islamic art history as an academic discipline was not simply imported from Europe. Professionalization stabilized a half century of domestic engagement with Persian art as a polysemic guiding light for American culture and society.

Assessing Impacts of Digital Sketching on Concept Generation in Early Stage Design

Sat, 01 Feb 2025 00:00:00 GMT

Assessing Impacts of Digital Sketching on Concept Generation in Early Stage Design Das, Madhurima Digital design tools have become increasingly popular for facilitating designers with different steps of the design process because they can simplify or automate certain components of these steps. Computer Aided Design (CAD) tools have assisted designers with tasks such as modeling and visualizing products prior to production and easily creating engineering drawings for manufacturing. Artificial Intelligence (AI) tools are being explored as collaborators who assist designers with interpreting sketches, assessing user needs, and generating ideas. Digital sketching tools such as tablets are a popular way for designers to easily create drawings that include different colors and styles and create multiple drafts of a concept by copying and pasting elements from previous sketches. However, the introduction of new tools into the design process always has broader implications for the design process. For instance, using CAD tools too early in the process can lead to design fixation and result in designers thinking a concept is more refined than it actually is due to the high quality and polish of the visualization created. Many researchers are now investigating when and how the best way to use AI tools in the design process is, but all struggle with the associated ethical implications of using the right training data and ensuring that the results are validated due to the serious risks related to misuse of AI. This dissertation focuses on one such digital design tool: tablets that are used for sketching. In an effort to expand the discipline’s understanding of how tablet use for sketching may enhance or detract from the design process, this thesis describes a series of studies investigating differences in ideation sketch attributes between tablets and paper/pen. Several of these sketch attributes have been linked with success in design- for instance, creating more sketches during ideation is linked with having better eventual design outcomes. This work investigates how sketch quality and quantity is impacted by the tools used for a short high level brainstorming session as well as a more detailed engineering concept generation task. Subsequently, it explores differences in content or novelty of ideas generated using each medium. Finally, it examines ways in which designers’ ideas evolve throughout the ideation process on both tablets and pen and paper. These aspects of the ideation process are important to understand, especially if the use of tablets leads to different results. The first area of investigation is related to exploring differences in sketch metrics including quantity, quality, and understandability between different sketching tools. These metrics have been found to be related to longer-term design outcomes and perceived creativity of concepts, so understanding the effect of the tablet on these sketch metrics can provide an understanding of how using a tablet for sketching could enhance or detract from overall design performance. The first study in this section investigates differences between pencil, pen, and tablet sketches during a short concept generation exercise and finds that sketch quality was highest for pencil drawings and lower for pen drawings but that tablet drawings do not significantly differ in quality from either pencil or pen drawings. Subsequently, a longer engineering design specific concept generation exercise was conducted to compare tablet sketching to pen and paper sketching. Here, there were no differences found in sketch quantity or understandability between paper and tablet. However, sketch quality, smoothness, and proportion/accuracy were all found to be higher on pen and paper than tablet. The second area of investigation explores whether or not using a tablet influenced designers’ ideation patterns. For instance, does the ability to copy and paste result in designers creating more interrelated ideas during brainstorming instead of exploring a variety of different design directions? There were no major differences found in the overall quantity of concept evolution present between tablet sketching and pen and paper sketching. However, tablet sketches across an ideation session had statistically significantly more concept chaining (related ideas appearing in a row) than paper and pen sketches despite having a similar number of related ideas overall. Additionally, concept chaining patterns were different for design prompts that had more than one functional requirement since not all ideas addressed all parts of the design prompt. However, for these prompts, the results from the primary functional requirement exhibited the same concept chaining patterns with more chaining present for tablet sketching than paper and pen sketching. The final area of investigation explores how designers’ ideas themselves are influenced by the sketching tool used through explorations of concept novelty and concept evolution. One study investigated novelty differences in concepts generated on tablet vs paper and found no correlation between the sketching tool used and the novelty of concepts generated. A second study was conducted to specifically compare designers’ own understanding of the interrelatedness of their ideas with the interrelatedness that could be assessed from the functional similarity of their sketches. Here, designers’ and reviewers’ assessments were found to not be aligned. In other words, sketches as standalone design artifacts did not communicate the extent of interrelatedness of concepts that was clear to the designer. Furthermore, the sketching tool used (tablet vs paper and pen) does not influence the level of agreement between designer and reviewer assessments. As such, using a tablet for sketching does not enhance or detract from the level of interrelatedness represented in sketches. These results suggest that assessing visual or functional similarity from sketches alone, regardless of the sketching tool used, may be insufficient in understanding all the relationship between a series of concepts as understood by the designer. Overall, these results indicate that using tablets as sketching tools does not have a clear significant benefit or burden on designers during ideation. It does not appear to enhance designers’ creative skills when it comes to sketch quantity or novelty though it did result in lower quality sketches, which has implications for the perceived creativity of concepts. Tablets were found to exhibit more instances of concept chaining than paper and pen sketches, though this trend did not persist when designers assessed their own concepts. Finally, this dissertation demonstrates that it is critical to seek designer input in identifying similarities across sketches as functional similarity may not be aligned with designers’ own understanding of which of their ideas are related.

Sorption-based atmospheric water harvesting: from atoms to applications

Sat, 01 Feb 2025 00:00:00 GMT

Sorption-based atmospheric water harvesting: from atoms to applications Zhong, Yang Thirteen thousand trillion liters of water in the atmosphere is a natural resource found anywhere on the earth, and available to anyone. Sorption-based atmospheric water harvesting (SAWH) is the extraction of water vapor using sorbent materials across a broad spectrum of relative humidity, which opens new avenues to address water scarcity faced by two-thirds of the world’s population. SAWH technologies gained significant attention in 2017 with the development of a solar-powered system utilizing metal-organic framework (MOF) sorbents to extract water from the air. While groundbreaking, this proof-of-concept device produced only a few milliliters of water, far from sufficient to meet even a single person’s daily water needs. A large gap thus remains between laboratory discoveries and real-world applications. This thesis aims to advance the understanding of SAWH technologies from atoms to applications. It begins with a multiscale perspective on SAWH technologies towards real-world applications, addressing knowledge gaps across various length scales. Through this multiscale approach, we developed a framework that can bridge material innovations with device realization. At the molecular scale, the thesis seeks to address a fundamental challenge: the inability to directly observe water sorption processes. To overcome this long-standing challenge, we introduced the use of cryogenic transmission electron microscopy (cryo-TEM) to probe water sorption in nanoporous materials at the single-pore level. This approach allows us to image water sorption and material structures with atomic resolution. Owning to the high resolution and in situ capabilities of cryo-TEM, we resolved a partially water-filled state of MOF crystals and observed that water molecules tend to occupy the centers of pores and fill neighboring pores once adjacent ones are filled. This technique offers new insights into sorption mechanisms and holds significant potential for the development of new sorbent materials. Building on the material-device-bridging framework, we proposed a dual-stage device architecture inspired by multistage distillation in desalination, where condensation heat from one stage drives desorption in the next, increasing productivity and thermal efficiency. To guide materials selection based on operating conditions, a universal thermodynamic model is developed to predict the efficiency of sorbent materials given their sorption isotherms. Additionally, this analysis reveals practical strategies to improve devicelevel sorption kinetics and heat transfer performance, pushing the technology toward thermodynamic limits. At the global scale, the framework enables the optimization of material deployment tailored to diverse climatic conditions. The real-world impact is further demonstrated through a technoeconomic assessment, which illustrates SAWH technology’s competitiveness with bottled and tap water and pathways to further improve its cost-effectiveness. The thesis concludes with an outlook on future opportunities for SAWH technologies and a discussion of their societal and environmental impacts at scale, including their potential role in mitigating climate change.

Causal Inference with Survival Outcomes via Orthogonal Statistical Learning

Sat, 01 Feb 2025 00:00:00 GMT

Causal Inference with Survival Outcomes via Orthogonal Statistical Learning Xu, Shenbo The field of causal inference has recently made great strides in incorporating machine learning into confounding adjustment and estimation of heterogeneous treatment effects (HTE). However, there were some gaps regarding survival outcomes. First, overlap-weighted effect estimators based on machine learning nuisance models were not available for such outcomes. Thus, researchers wishing to mitigate bias and variance from poor overlap had to accept potential bias from nuisance model misspecification in its place. In Chapter 2, we fill this gap by proposing a class of one-step cross-fitted double/debiased machine learning estimators for cumulative weighted average treatment effects for both survival outcomes and competing risk outcomes. Our approach combines importance sampling, semiparametric theory, and Neyman orthogonality to resolve both model misspecification and lack of covariate overlap between treatment arms in observational studies with censored outcomes. We give regularity conditions for the consistency, asymptotic linearity, and semiparametric efficiency bounds of the proposed estimators. Through simulation, it is shown that the proposed estimators do not require oracle parametric nuisance models. We apply the proposed estimators to compare the effects of two first-line anti-diabetic drugs on cancer outcomes. Second, a wide range of machine learning methods (or ”learners”) for estimating heterogeneous treatment effects were not applicable to estimating effects on survival outcomes, particularly in the presence of competing risks. In Chapter 3, we fill this gap by developing several once-for-all (orthogonal) censoring unbiased transformations which convert time-to-event data into continuous outcomes, such that all HTE learners and oracle rates for continuous outcomes can be borrowed. Our approach not only reduces the pressing need to develop various HTE learners for censored outcomes and especially competing risks, but also fully leverage the state of the art of existing schemes. Through direct application of HTE learners to these transformed continuous outcomes, we obtain consistent estimates of heterogeneous cumulative incidence effects, total effects, and separable direct effects. We provide generic model-free learner-specific oracle inequalities bounding the finite-sample excess risk. The oracle efficiency results depend on the oracle selector and estimated nuisance functions from all steps involved in the transformation. We demonstrate the empirical performance of the proposed methods in simulation studies. An important application area for causal inference methods, and one which originally motivated my interest in the field, is drug repurposing. In Chapter 4, we apply the methods of Chapter 2 to investigate whether metformin, a diabetes medication, might also have unexpected beneficial effects on cancer. The analysis encountered three major challenges: poor overlap between treatment groups, model misspecification, and pre-cancer death as competing risks for cancer incidence. To resolve these issues simultaneously, we take balancingweighted total cause-specific effects, controlled direct effect, and separable effects as causal estimands and develop balancing-weighted double/debiased machine learning estimators for both cumulative incidence functions and restricted mean time lost, with all estimators satisfying Neyman orthogonality. Using the Clinical Practice Research Datalink (CPRD) data, we find that metformin revealed a preventive direct effect on cancer incidence over sulfonylureas. The results also demonstrate the advantage of choosing the average treatment effect for the overlap population as the target quantity. Finally, just as machine learning helps to automate nuisance model estimation for confounding adjustment and modeling effect heterogeneity, causally informed artificial intelligence (AI) and large language models (LLMs) might help to automate hypothesis generation for drug repurposing and surveillance opportunities. In Chapter 5, we explore this potential by developing a high-throughput screening approach to evaluate available drugs across multiple diseases. The screening methodology aims to identify drug-disease pairs with significant positive signals that could represent promising repurposing candidates, while also detecting pairs with negative signals that might indicate potential safety concerns–both being critical aspects for pharmacoepidemiology research. This systematic approach leverages the convergence of expanding healthcare data sources and modern data science advances to establish a data-driven framework for drug repurposing discovery and pharmacovigilance. To conclude, we discuss the limitations of the proposed methods and provide possible future research directions.

Reasoning over Hierarchical Abstractions for Long-Horizon Planning in Robotics

Sat, 01 Feb 2025 00:00:00 GMT

Reasoning over Hierarchical Abstractions for Long-Horizon Planning in Robotics Bradley, Christopher P. We aim to enable robots to act intelligently in complex environments not explicitly designed around them. In order to do so, robots can simplify decision making by forming hierarchical abstractions of their world, and planning within those representations. However, in reality, the types of abstractions robots are able to build are often poorly aligned with the planning problems they must solve, which limits how useful those abstractions can be in efficient decision making. For example, autonomous agents struggle in many real world scenarios, particularly when their environments are large, cluttered with obstructions, or beset by uncertainty. These factors often imply that decisions made at higher levels of abstraction may not be easily refined to low level plans, leading to backtracking during either search or execution. In this thesis, we consider contributions which improve the efficiency and quality of long-horizon hierarchical planning in robotics. Specifically, we propose approaches which explicitly reason about the imperfections of the abstractions available to robots during planning, and show how those methods can improve performance on a variety of tasks and environments. There are three primary settings for which we make contributions in this thesis. First, we will consider the problem of solving tasks in partially revealed environments, wherein our abstract plans cannot be known to be feasible until we attempt execution because the world is not fully known at planning time. To solve this problem, we first develop a high level planning representation which recognizes that actions that enter unknown space can either succeed or fail with some probability. The first contribution of this work is then to learn to predict the feasibility and cost of actions within that abstraction from visual input. We also describe a method for planning which uses these predictions, and we are able to show that our approach can generate plans that are significantly faster at completing tasks in unknown environments experimentally when compared with heuristic driven baselines. Next, we will discuss work in Task and Motion Planning (TAMP), where the world is fully known, but the problems require complex interaction with the environment to the point that we must intelligently guide search in order to find plans efficiently. We build upon our work in the first setting by once again learning to predict the outcome and cost of different sub-tasks within a TAMP abstraction. We further contribute a novel method to guide search in this setting for plans which minimize cost given our learned predictions, and demonstrate the ability to find faster plans than established TAMP approaches both in simulation, and on real world robots. In our final problem setting, we consider attempting to solve TAMP problems in real world, large-scale environments. To do this, we define an approach for constructing tractable planning abstractions from real perception using hierarchical scene graphs, ensuring that when we refine our abstract plans within these representations, the low-level trajectories still satisfy the given task’s constraints. A major contribution of this work is an approach for planning efficiently in these domains by pruning provably superfluous information from the world model. The unifying aim of the work in this thesis is to develop approaches which enable robots to solve complex tasks in large-scale, real world environments without human intervention. To that end, across all contributions, we demonstrate experimentally on real robots the importance of accounting for imperfections in hierarchical abstraction during planning.

New tools for Bayesian optimal experimental design and kernel-based generative modeling

Sun, 01 Sep 2024 00:00:00 GMT

New tools for Bayesian optimal experimental design and kernel-based generative modeling Li, Fengyi This thesis develops new computational approaches for two canonical problems in statistics and machine learning: optimal experimental design and generative modeling. Optimal experimental design (OED) is important to model development for science and engineering applications and beyond, especially when only a small number of observations can be taken or experiments performed, due to resource limitations. In the Bayesian setting, a useful criterion for the importance of candidate experiments is the expected information gain (EIG) from prior to posterior, or equivalently, the mutual information (MI) between candidate observations and the parameters of interest. Yet estimating EIG for a given design can be quite challenging in nonlinear/non-Gaussian models, and for high-dimensional parameters and observations. In the first part of the thesis, we introduce new methods for estimating EIG based on transportation of measure. Specifically, we use marginal and conditional density estimates, obtained with semi-parametric transport models, in a Monte Carlo estimator. The density estimates are obtained by solving convex optimization problems. This framework is also compatible with implicit models, where one can simulate from the likelihood or prior but the associated density functions are unknown. We identify the optimal scaling of sample sizes between the "inner" density estimation steps and the "outer" EIG estimation, and demonstrate the efficiency of these choices numerically. If the dimensions of the parameters or observations are high, however, direct density estimation becomes intractable. Here, we use gradient-based information bounds, obtained via log-Sobolev inequalities, to identify optimal projections of the parameters and observations, and then apply our transport-based EIG estimation scheme. We next study the problem of cardinality-constrained observation selection to maximize MI in non-Gaussian settings, i.e., choosing the most informative subset of k observations from a candidate pool of size n > k. Finding the exact solution is to this combinatorial optimization problem is computationally costly, so we resort to greedy approaches based on computationally inexpensive lower bounds for MI. Here we again use log-Sobolev inequalities to construct such lower bounds for certain classes of non-Gaussian distributions, and exploit these lower bounds within the combinatorial problems. We demonstrate that our method outperforms random selection strategies and Gaussian approximations in many settings, including challenging nonlinear design problems with non-additive noise. In the second part of the thesis, we turn our attention to generative modeling, which can be understood as the problem of drawing new samples from an unknown distribution, from which a fixed sample is available. Our approaches employ kernel-type algorithms based on diffusion maps. First, we propose an interacting particle system for generative modeling, based on diffusion maps and Laplacian-adjusted Wasserstein gradient descent (LAWGD). Diffusion maps are used to approximate the generator of the corresponding Langevin diffusion process from samples, and hence to learn the underlying data-generating manifold. LAWGD enables efficient sampling from the target distribution given the generator of the Langevin diffusion process, which we construct here via a spectral approximation via kernels, computed with diffusion maps. Our method requires no offline training and minimal tuning, and can outperform other approaches on data sets of moderate dimension. Second, we propose a generative model combining diffusion maps and Langevin dynamics. Diffusion maps are used to approximate the drift term from the available training samples, which is then implemented in a discrete-time Langevin sampler to generate new samples. By setting the kernel bandwidth to match the time step size used in the unadjusted Langevin algorithm, our method effectively circumvents any stability issues typically associated with time-stepping stiff stochastic differential equations. We demonstrate the performance of our proposed scheme through experiments on synthetic datasets of increasing dimension, and on a conditional sampling problem arising in stochastic subgrid-scale parametrization of a dynamical system.

Essays on Spatial Constraints and Gender Equality: the Impact of COVID-19 Lockdowns on Work-from-Anywhere Dynamics and Gender Equality in Job Searches

Sat, 01 Feb 2025 00:00:00 GMT

Essays on Spatial Constraints and Gender Equality: the Impact of COVID-19 Lockdowns on Work-from-Anywhere Dynamics and Gender Equality in Job Searches Labuzova, Tatiana This dissertation explores the intersection of spatial constraints and gender equality by leveraging the COVID-19 lockdowns as a natural experiment to study the impact of work-from-anywhere (WFA) dynamics on job search behaviors. The introduction of mandatory lockdowns drastically shifted the labor market landscape, prompting an increase in the demand for flexible work formats. Utilizing data from over one million job seekers on an online employment platform, this research examines how the sudden wide availability of remote work options influenced job search activities differently across genders. Using unique data from a large online job platform, a comparison of pre- and post-COVID-19 lockdown data shows that women significantly increased their engagement with geographically flexible job postings, reacting more strongly than men to the rise in remote job opportunities at both the job viewing and application stages. This shift also resulted in a narrowing of the wage gap in positions viewed and applied for during the post-lockdown period compared to pre-lockdown benchmarks. Notably, the study identifies variations in job search behavior among those likely constrained by domestic responsibilities. While differences in job posting views suggest an initial differential impact, such differences vanish at the application stage. Collectively, these results indicate that the pandemic-induced shift towards remote work has contributed to a gender-equalizing effect in the job market, including those navigating domestic labor constraints. This research not only highlights the transformative potential of WFA arrangements in promoting gender equality but also provides insights into the mechanisms that drive these changes within the labor market. Keywords: organizational studies, gender inequality, flexible working arrangements, hiring, applications processes, decision making, digital platforms.

A Systems-Theoretic Framework For Safety-Driven Development of System Architectures

Sat, 01 Feb 2025 00:00:00 GMT

A Systems-Theoretic Framework For Safety-Driven Development of System Architectures Poh, Justin Wei Siang Modern complex systems are increasingly expected to exhibit emergent properties such as safety and security even as they become more complex, interconnected, and reliant on software than ever before. Because of this evolution in the characteristics of these systems, the methods available today for developing system architectures no longer provide systems engineers with adequate design support. As a result, it is becoming increasingly challenging for systems engineers to develop system architectures that exhibit emergent properties like safety. This thesis addresses this problem by developing a safety-driven architecture development framework that enables the design of emergent properties such as safety into a system architecture from the beginning. The key idea is that the results from a hazard analysis process known as Systems Theoretic Process Analysis (STPA) should drive design decisions. The framework therefore starts with an initial STPA analysis of the system to determine how unsafe or undesirable behavior could occur. Structured and systematic processes are then provided to help systems engineers use the STPA results to develop the required control behavior of the system and explore possible system architecture options to implement that control behavior. This framework therefore enables systems engineers to make more informed early architectural design decisions driven by safety considerations. This framework is applied to an Urban Air Mobility (UAM) case study to demonstrate that it provides the necessary design support to enable the development and refinement of an air traffic management (ATM) architecture for UAM. When creating a system architecture, assumptions may also need to be made to mitigate the inherent uncertainties and lack of detailed information about the system at that early stage of design. However, these assumptions are used as the basis for design decisions, and it is important that they remain valid to avoid flaws in the architecture arising when underlying assumptions become invalid. Thus, this thesis also develops and demonstrates a supporting framework to help identify these underlying assumptions and ensure they remain valid both during system development and after the system is placed into operation. Modern complex systems are increasingly expected to exhibit emergent properties such as safety and security even as they become more complex, interconnected, and reliant on software than ever before. Because of this evolution in the characteristics of these systems, the methods available today for developing system architectures no longer provide systems engineers with adequate design support. As a result, it is becoming increasingly challenging for systems engineers to develop system architectures that exhibit emergent properties like safety. This thesis addresses this problem by developing a safety-driven architecture development framework that enables the design of emergent properties such as safety into a system architecture from the beginning. The key idea is that the results from a hazard analysis process known as Systems Theoretic Process Analysis (STPA) should drive design decisions. The framework therefore starts with an initial STPA analysis of the system to determine how unsafe or undesirable behavior could occur. Structured and systematic processes are then provided to help systems engineers use the STPA results to develop the required control behavior of the system and explore possible system architecture options to implement that control behavior. This framework therefore enables systems engineers to make more informed early architectural design decisions driven by safety considerations. This framework is applied to an Urban Air Mobility (UAM) case study to demonstrate that it provides the necessary design support to enable the development and refinement of an air traffic management (ATM) architecture for UAM. When creating a system architecture, assumptions may also need to be made to mitigate the inherent uncertainties and lack of detailed information about the system at that early stage of design. However, these assumptions are used as the basis for design decisions, and it is important that they remain valid to avoid flaws in the architecture arising when underlying assumptions become invalid. Thus, this thesis also develops and demonstrates a supporting framework to help identify these underlying assumptions and ensure they remain valid both during system development and after the system is placed into operation.

Explicit formulas for weighted orbital integrals for the inhomogeneous and semi-Lie arithmetic fundamental lemmas conjectured for the full spherical Hecke algebra

Sat, 01 Feb 2025 00:00:00 GMT

Explicit formulas for weighted orbital integrals for the inhomogeneous and semi-Lie arithmetic fundamental lemmas conjectured for the full spherical Hecke algebra Chen, Evan As an analog to the Jacquet-Rallis fundamental lemma that appears in the relative trace formula approach to the Gan-Gross-Prasad conjectures, the arithmetic fundamental lemma was proposed by Wei Zhang and used in an approach to the arithmetic Gan-Gross-Prasad conjectures. The Jacquet-Rallis fundamental lemma was recently generalized by Spencer Leslie to a statement holding for the full spherical Hecke algebra. In the same spirit, there is a recent conjectural generalization of the arithmetic fundamental lemma to the full spherical Hecke algebra. This paper formulates another analogous conjecture for the semi-Lie version of the arithmetic fundamental lemma proposed by Yifeng Liu. Then this paper produces explicit formulas for particular cases of the weighted orbital integrals in the two conjectures mentioned above.

Cooling with less: Design and simulation of multifunctional building components for a material-efficient, heat-resilient architecture

Sat, 01 Feb 2025 00:00:00 GMT

Cooling with less: Design and simulation of multifunctional building components for a material-efficient, heat-resilient architecture Gascón Alvarez, Eduardo As temperatures rise globally and the demand for housing intensifies, designing affordable buildings for heat resilience and with low carbon emissions becomes crucial. Conventional air conditioning (AC) systems, although often an effective and accessible cooling solution, are energy-intensive and typically fail to consider local climatic and urban contexts. This work alternatively focuses on the opportunity behind designing building components (such as slabs, blocks, roofs, or footings) for multifunctionality, integrating passive strategies and low-energy cooling systems within them in a material-efficient manner. Collapsing multiple functions into a single building component is typically regarded as a strategy that leads to better overall performance and reduced costs compared to implementing each function separately. However, the effectiveness of this strategy in cooling-dominated climates and in the context of the current climate crisis remains underexplored. The dissertation proposes new designs and evaluation methods for three multifunctional building components: multi-hollowed blocks (ceramic blocks with interior air pockets), shaped chilled slabs (shaped concrete slabs with embedded radiant ceiling systems), and integrated heat sinks (thermally activated concrete footings and roofs). Each component is designed to optimize a specific cooling strategy based on its context within the building and intrinsic material properties - thermal mass, radiant cooling, and ground/radiative cooling. Chapter 2 demonstrates how shape-optimized ceramic blocks can double the heat capacity of existing commercial solutions without additional material or reduce their weight by 33% while increasing the heat capacity by 23%. Chapter 3 presents slab geometries that achieve embodied carbon reductions of up to 50% relative to conventional prismatic floors while reducing operational carbon by 12-14%. Chapter 4 finds that buildings in temperate climates with a Floor Area Ratio (FAR) of up to 4.5 can meet 100% of the cooling demand exclusively through heat dissipation systems integrated into the building’s foundations and roof. Methodologically, this research puts together heat transfer theory and analytical models with state-of-the-art shape optimization methods; this effort results in a fast and accurate multi-objective simulation framework tailored for early design stages. This thesis provides, for the first time, validated methods and quantitative results that support the viability of multifunctional building components in cooling-dominated climates, optimizing the shape of walls, blocks, foundations, and roofs to improve their structural and thermal performance simultaneously, reducing their weight and improving buildings’ resilience to heat. From a climate adaptation perspective, this approach ensures that buildings are ready for extreme heat even when active systems are unavailable due to, for example, a power outage. From a carbon mitigation perspective, the presented results highlight the potential to reduce the whole-life carbon of buildings by shape-optimizing components for enhanced thermal performance and material efficiency.

Electrospray Thrusters in Chemical-Electric Multimode Propulsion for Small Satellites

Sat, 01 Feb 2025 00:00:00 GMT

Electrospray Thrusters in Chemical-Electric Multimode Propulsion for Small Satellites Bruno, Amelia R. Propulsion for small spacecraft is typically one of two modes, chemical or electric. These modes offer complementary propulsive performance: chemical propulsion provides high thrust and low specific impulse, while electric propulsion provides the inverse. As such, having access to both modes on the same spacecraft (i.e. multimode propulsion) is extremely useful. Unfortunately, the conventional propellants used by chemical and electric thrusters are highly incompatible, making this particularly difficult on small spacecraft that lack the mass, power, and volume to accommodate two separate propulsion systems. However, recent advancements in green monopropellants -- developed as less-toxic alternatives to hydrazine in chemical monopropellant thrusters -- have created a new family of ionic liquids monopropellants, making them the natural propellant for a highly compact form of electric propulsion known as electrospray thrusters. This presents a unique opportunity for a propellant to be shared between two propulsion modes, decreasing required mass and volume to be feasible for small spacecraft. This thesis examines the use of ionic liquid monopropellants in electrospray thrusters for a multimode chemical-electric propulsion system. This thesis focuses particularly on ASCENT, a high-maturity monopropellant with flight heritage in chemical thrusters. In this work, the performance of ASCENT in the MIT ion Electrospray Propulsion System (iEPS) is extensively characterized. Experimental work includes ion plume diagnostics, indirectly and directly obtained performance estimates, temperature-dependent performance estimates, and extended duration firing behavior. Preliminary studies of similar monopropellants are also conducted to assess their use in a multimode system. To support an upcoming technology demonstration flight, a new multimode-compatible iEPS thruster tank is designed, fabricated, and validated. The integration and operation requirements for this thruster in a flight-ready system are defined. Finally, the mission benefits of an ASCENT multimode system for CubeSats are compared against current commercial options using an Earth observation mission case study. This work finds that an iEPS thruster with ASCENT propellant has thrust of 9-15 µN, a specific impulse of 600-750 seconds, and a total efficiency of 18-22%, depending on current setpoint. We find that ASCENT is slightly volatile in high vacuum, which causes time-dependent losses in efficiency and specific impulse from gradual propellant evaporation. This volatility may also increase thruster lifetime by mitigating the risk of thruster failure by emitter flooding. This work also identified a modified version of ASCENT, created when the propellant is exposed to iron. This modified version produces a dramatically higher thrust and thrust-to-power compared to standard ASCENT. Additionally, flight-ready configurations of a multimode system are defined for 6U, 12U, and 27U CubeSats. A case study analysis found that the benefits of a chemical-electrospray multimode system are best realized at the 12U scale and above. Overall, this thesis provides critical insights on the performance, integration, and operation of electrospray thrusters with ionic liquid monopropellants. These results can then be used to enable a multimode propulsion system for small satellites.

Crafting Cannabinoid Capitalism: Health, Sustainability, and Regeneration in the United States

Sat, 01 Feb 2025 00:00:00 GMT

Crafting Cannabinoid Capitalism: Health, Sustainability, and Regeneration in the United States Rewegan, Alexander Nicholas This dissertation offers a critical exploration of cannabis legalization through an ethnographic study of small-scale "legacy" cannabis farmers in Humboldt County, California, as they navigate a complex transition from prohibition to commodity capitalism. I focus on their collective efforts to envision and practice “regenerative agriculture" as a response to both the historical injustices of prohibition and the compounding challenges of climate change. Drawing on history, STS, and the anthropology of food, agriculture, and medicine, I show how the logics of the war on drugs— rooted in carcerality, settler colonialism, and plantation agriculture—structurally and affectively persist in the socalled “post-prohibition” era, frustrating farmers’ efforts to resist monopolization and dispossession. Throughout, I attend to how the pervasive notions of “health,” “sustainability,” and “regeneration” are actively negotiated, modified, and put to use as material and symbolic tools in crafting medicinal, agricultural, and ecological futures. The Introduction weaves a tapestry of themes, histories, and theories that set the stage for the main ethnography. Through a blend of personal narrative, ethnographic vignette, and critical theory, it works to situate cannabis as a fluid and multifaceted object, highlighting people’s ambivalent hopes and cynicisms towards legalization. From alternative farming to molecularized biocapital, it articulates the intersecting influences of climate change, racial capitalism, and Indigenous sovereignties in ongoing projects to commercialize and legalize cannabis in a globally connected United States. Chapter One outlines my research methods and provides a social and narrative history of the study’s fieldsite, grappling with the anthropological complexities and complicities of studying working landscapes in a settler colonial “frontier ecology.” Chapter Two unpacks the shifting and embodied subjectivities of both farmers and workers as they reconfigure themselves in service of licensed production, highlighting sociocultural tensions and contradictions, the structural challenges of regenerative gardening, and the labor dynamics that shape these processes. Chapter Three analyzes how the inchoate and social nature of cannabis regulation both hinders and supports regenerative farming, emphasizing financial strain, and the ever-pervasive role that surveillance technologies are playing in cannabis governance. Chapter Four shifts to the harvest season, exploring farmers’ collective efforts to market their products through the concept of “drug terroir,” unpacking how their values and practices entangled with regional efforts to address wildfires and remediate leftover drug war infrastructures. Chapter Five moves off the farm and onto the topic of consumption as it historicizes the growing scientific literature about cannabis and pregnancy, demonstrating how carcerality continues to infiltrate maternal-fetal health science and conceptions of reproduction and health. The dissertation ultimately explores the ways in which American cannabis legalization often regenerates, rather than resolves, the legacies of prohibition and settler colonialism, while at the same time illuminating alternative and promising practices that might challenge these enduring forces.

Decadal to centennial-scale climate interactions across the Indo-Pacific region

Sat, 01 Feb 2025 00:00:00 GMT

Decadal to centennial-scale climate interactions across the Indo-Pacific region Wang, Shouyi An improved understanding of decadal to centennial-scale climate variability is critical for properly attributing recently observed low-frequency changes to internal climate oscillations and/or anthropogenic forcings as well as improving predictability of decadal variability. This thesis investigates ocean and atmospheric circulation changes and associated impacts within the tropical Indo-Pacific, where low-frequency changes in heat and freshwater impact the livelihoods of billions of people. Because the instrumental record is too short to investigate centennial variability, this thesis leverages numerical simulations and records from paleoclimate archives to provide insights into low-frequency tropical dynamics. In Chapter 2, we explore the dynamics that drive Indonesian Throughflow surface transport variability using a series of forced global high-resolution ocean simulations. We show that surface wind changes associated with Pacific decadal variability drive changes in the western boundary currents that modulate the Indonesian throughflow, consistent with mechanisms identified on interannual timescales. This work identifies a relationship between atmospheric circulation and transport through a key low-latitude passageway. Motivated by paleoclimate evidence of multi-year droughts in Southeast Asia, we investigate their potential drivers in Chapter 3 using an ensemble of coupled climate model simulations. These simulations illustrate that Indo-Pacific internal variability dominated Southeast Asian rainfall extremes during the last millennium, although the influence of volcanic eruptions was detectable. We found that multi-year pluvials were contributed by both Pacific and Indian Ocean modes, while droughts were largely only driven by Pacific Ocean impacts. Our analysis not only quantifies the role of internal and external drivers to Southeast Asia rainfall but also presents a probabilistic analysis framework that may be useful for water resources prediction. Lastly, in Chapter 4 we reconstruct the Indian and Pacific Walker circulations and the Indian Ocean Basin Mode by synthesizing tropical records (corals, tree-rings, and speleothems) of past ocean and atmospheric conditions to investigate basin interactions over the past four centuries. Our results demonstrate that IndoPacific climate was generally coupled on decadal-centennial timescales throughout the past four centuries but was notably decoupled in the early 19th century. Using climate models, we attribute this decoupling to a series of strong volcanic eruptions. Dynamically, we link this inter-basin decoupling to volcanically induced changes in hemispheric temperature gradients, which modulate the teleconnections across the Indo-Pacific. These past disruptions in basin interactions provide context for ongoing and simulated future decoupling under a high emission scenario, as global warming also alters interhemispheric temperature gradients. This thesis sheds light on the complex dynamics that drive ocean-atmosphere variability across the Indo-Pacific on decadal to centennial timescales.

Contact-aware and multi-modal robotic manipulation

Sat, 01 Feb 2025 00:00:00 GMT

Contact-aware and multi-modal robotic manipulation Zhao, Jialiang Intelligent robotic manipulation has advanced significantly in recent years, driven by progress in foundational cognitive models, sensor-fusion techniques, and improvements in actuators and sensors. However, most contemporary robotic systems still lack the ability to effectively recognize and understand contact dynamics, which are critical for performing manipulation tasks beyond basic pick-and-place operations. This thesis argues and proves that contact awareness is essential for the successful deployment of robotic systems, not only in structured environments such as factories but also in unstructured settings like domestic households. Achieving contact awareness necessitates advancements in three key areas: the development of improved contact-sensing hardware, the creation of more expressive frameworks for representing and interpreting contact information, and the design of efficient modality-fusion algorithms to integrate these capabilities into robotic action planning. This work addresses these challenges by (1) proposing novel mechanical designs that enable touch sensors to adopt more compact and versatile forms while enhancing their durability and manufacturability, (2) introducing a foundational representation learning framework capable of learning a shared tactile latent representation, which can be transferred across different sensors and downstream tasks, and (3) developing a compositional diffusion-based approach for action prediction that integrates tactile sensing signals with other perception modalities, thereby enabling learning across diverse environments and promoting policy reuse. Along the way, this thesis demonstrates that tactile sensors can be both compact and versatile, challenging common perceptions to the contrary. It also establishes that tactile sensing is indispensable not only for high-precision tasks, such as electronics assembly, but also for everyday activities, including cooking and tool usage.

Formally Verifying Secure and Leakage-Free Systems: From Application Specification to Circuit-Level Implementation

Sun, 01 Sep 2024 00:00:00 GMT

Formally Verifying Secure and Leakage-Free Systems: From Application Specification to Circuit-Level Implementation Athalye, Anish Hardware and software systems are susceptible to bugs and timing side-channel vulnerabilities. Timing leakage is particularly hard to eliminate because leakage is an emergent property that can arise from subtle behaviors or interactions between hardware and software components in the entire system, with root causes such as non-constant-time code, compiler-generated timing variation, and microarchitectural side channels. This thesis contributes a new approach using formal verification to rule out such bugs and build systems that are correct, secure, and leakage-free. This thesis introduces a new theory called information-preserving refinement (IPR) for capturing non-leakage in addition to correctness and security, implements a verification approach for IPR in the Parfait framework, and applies it to verifying hardware security modules (HSMs). Using Parfait, a developer can verify that an HSM implementation leaks no more information than is allowed by a succinct application-level specification of the device's intended behavior, with proofs covering the implementation's hardware and software down to its cycle-precise wire-I/O-level behavior. This thesis uses Parfait to implement and verify several HSMs, including an ECDSA certificate-signing HSM and a password-hashing HSM, on top of Ibex and PicoRV32-based hardware platforms. Parfait provides strong guarantees for these HSMs: for example, it proves that the ECDSA-on-Ibex implementation—2,300 lines of code and 13,500 lines of Verilog—leaks nothing more than what is allowed by a 40-line specification of its behavior.

Guiding Deep Probabilistic Models

Sun, 01 Sep 2024 00:00:00 GMT

Guiding Deep Probabilistic Models Garipov, Timur Deep probabilistic models utilize deep neural networks to learn probability distributions in high-dimensional data spaces. Learning and inference in these models are complicated due to the difficulty of direct evaluation of the differences between the model distribution and the target. This thesis addresses this challenge and develops novel algorithms for learning and inference based on the guidance of complex parameterized distributions towards desired configurations via signals from auxiliary discriminative models. In the first part of the thesis, we develop novel stable training objectives for Generative Adversarial Networks (GANs). We show that under standard unary-discriminator objectives, most of the valid solutions, where the learned distribution is aligned with the target, are unstable. We propose training objectives based on pairwise discriminators that provably preserve distribution alignment and demonstrate improved training stability in image generation tasks. In the second part of the thesis, we introduce distribution support alignment as an alternative to the distribution alignment objective and develop a learning algorithm that guides distributions towards support alignment. We demonstrate the effectiveness of our approach in unsupervised domain adaptation under label distribution shift. Recent works have shown that under cross-domain label distribution shift, optimizing for distribution alignment is excessively restrictive and causes performance degradation. Our algorithm, which is based on support alignment, alleviates this issue. In the third part of the thesis, we develop a novel approach to compositional generation in iterative generative processes: diffusion models and Generative Flow Networks (GFlowNets). Motivated by the growing prominence of generative models pre-trained at scale and the high training costs, we propose composition operations and guidance-based sampling algorithms that enable the combination of multiple pre-trained iterative generative processes. We offer empirical results on image and molecular generation tasks.

A Technology Platform for Enabling Next-Generation Vacuum Electronic Devices Based on Silicon Field Emitter Arrays

Sun, 01 Sep 2024 00:00:00 GMT

A Technology Platform for Enabling Next-Generation Vacuum Electronic Devices Based on Silicon Field Emitter Arrays Karaulac, Nedeljko As the demand for electronics with better performance and increased functionality continues to escalate, researchers are finding it more and more difficult to surpass the limitations of conventional transistors due to electron transport in solid-state. Nanoscale vacuum-channel transistors, in which the electron transport channel is vacuum instead of solid-state, offer a potential alternative device architecture beyond device scaling. Due to their ballistic transport and higher breakdown field, nanoscale vacuum-channel transistors are expected to show better performance in a wide variety of high-frequency, high-power, or harsh environment applications. Silicon field emitter arrays (FEAs) are a proven and mature technology that can be implemented as vacuum transistors, and they could also be used in vacuum integrated circuits. Many of the challenges regarding uniformity, reliability, and lifetime have been addressed in this technology. However, the scalability of the emission current remains a challenge. In this work, we develop a layout-independent fabrication process for silicon FEAs that improves the scalability of emission current with array size. The fabrication process begins by first fabricating field emitters everywhere across the wafer and then selectively etching field emitters to form individual arrays. Using this process, we present for the first time silicon FEAs with array sizes ranging from 1 μm2 to 1 mm2, and we obtain emission current ranging from 1 nA to 1 mA, which represents a range of six orders of magnitude. In order to facilitate design of future vacuum integrated circuits, we develop a circuit model for silicon FEAs based on measurements of the transfer and output characteristics. The circuit model is used to demonstrate a proof-of-concept inverter based on a silicon FEA and pull-up resistor that could potentially be fabricated as a vacuum integrated circuit. Lastly, we characterize and model the statistical variation in emission current to determine if it is feasible to build vacuum integrated circuits using the layout-independent fabrication process presented in this work.

Employing Magnetic Field Records at Key Moments in Planetary Evolution

Wed, 01 May 2024 00:00:00 GMT

Employing Magnetic Field Records at Key Moments in Planetary Evolution Mansbach, Elias N. The analysis of the paleomagnetic record in meteorites provides a unique and powerful viewpoint on early solar system and planetary evolution. Indeed, meteorites are the only tangible objects that bore witness to these important events, making their records particularly precious. In this thesis, I present my dissertation work that addresses how the meteoritic paleomagnetic record and additional records from materials provided by return sample missions can be used to study three stages in early solar system and planetary evolution: I) The protoplanetary disk; II) The initial melting on the first planetary bodies; and III) The early interior evolution of modern planets. I address Stage I through a paleomagnetic analysis of returned samples from asteroid Ryugu to determine the role of magnetic fields in stellar accretion in the distal solar system (Chapter 2). I address Stage II through a paleomagnetic analysis of the Acapulco primitive achondrite (Chapter 3) and micromagnetic modeling of the ferromagnetic mineral tetrataenite (Chapter 4) to eludcidate core formation on small bodies. Lastly, I address Stage III through preparation for paleomagnetic studies of future returned samples from the Perseverance rover to determine the lifetime and properties of the Martian dynamo (Chapters 5 and 6). I end with a brief conclusion and ideas for future work.

Leveraging Mechanics for Multi-step Robotic Manipulation Planning

Sun, 01 Sep 2024 00:00:00 GMT

Leveraging Mechanics for Multi-step Robotic Manipulation Planning Holladay, Rachel This thesis focuses on enabling robots to robustly perform complex, multi-step manipulation tasks, like chopping vegetables or wielding a wrench. Completing such tasks requires a robot to plan and execute long sequences of actions, where each action involves many connected, discrete and continuous choices that are critically impacted by constraints relating to force, motion and contact. To tackle this, this thesis contributes models and algorithms that exploit the physics and geometry of the world in order to address the dual challenges of long-horizon decision-making and acting under uncertainty. We apply this in the context of three domains: in-hand manipulation, forceful manipulation and briefly-dynamic manipulation. First, to reorient a grasped object, we develop a sampling-based motion planner to generate sequences of pushes that slide the object in-hand. We derive an abstraction for pushing to enable the planner to reason about frictional constraints. Second, we focus on forceful manipulation tasks, such as opening a childproof medicine bottle or twisting a nut on a bolt, where the robot's planning choices are impacted by the need to exert force. We define constraints that explicitly consider torque and frictional limits and integrate these into an existing task and motion planning framework. We leverage cost-sensitive planning to enable the robot to generate plans that are robust to uncertainty in the physical parameters. Finally, we frame planning with dynamic actions, like shoveling or toppling, as requiring the robot to reason about both action uncertainty and potential dead ends. We learn a simple action model and formulate a sample-based manipulation planner that guards against dead ends in the face of uncertainty. Throughout this thesis, we validate the practical applicability of our model-based approaches by evaluating them on real robots.

Approximation and System Identification Techniques for Stochastic Biomolecular Systems

Sun, 01 Sep 2024 00:00:00 GMT

Approximation and System Identification Techniques for Stochastic Biomolecular Systems Grunberg, Theodore W. Many biomolecular systems can be modeled as chemical reaction networks with a set of relevant species interacting via chemical reactions. When the molecular counts of the species are small, the inherent stochasticity in the occurrence of the reactions plays an important role in the behavior of the system. This stochasticity presents opportunities for system identification, since when a large population of cells is measured, one has many samples from the underlying distribution of the stochastic model. On the other hand, using the stochastic models of chemical reaction networks, given by continuous time Markov chains with countably infinite state spaces, creates computational and analytical difficulties when performing analysis or system identification. Therefore, approximate models that exploit timescale separation between different sets of chemical reactions to create reduced order models, or deterministic or diffusion approximations that approximate the continuous time Markov chain with an ordinary differential equation or stochastic differential equation respectively must be exploited. This thesis makes contributions in both directions, rigorously justifying such approximations as well as developing the theory to perform system identification on the approximate models.

Hybrid Magnonics in Antiferromagnets and Cavity Spintronic Devices

Sun, 01 Sep 2024 00:00:00 GMT

Hybrid Magnonics in Antiferromagnets and Cavity Spintronic Devices Hou, Justin T. Hybrid dynamic systems combine advantages from different subsystems for realizing information processing tasks in both classical and quantum domains. Magnons, the collective spin wave excitations in magnetically ordered materials, have recently attracted great attention for realizing hybrid dynamic systems. In this thesis, we develop hybrid magnonic systems with reduced complexity, improved scalability, and new functionality. In the first work, by utilizing van der Waals antiferromagnetic material CrCl3, we realize strong magnonmagnon coupling within a single material, simplifying the design of magnon-magnon hybrid systems which conventionally require two magnetic materials. Secondly, by utilizing planar microwave resonators, we realize on-chip, lithographically scalable, and Circuit Quantum Electrodynamics compatible magnon-photon hybrid systems. Strong magnon-photon coupling with three orders of magnitude reduction in spin number is demonstrated due to the reduced effective cavity mode volume. Moreover, the on-chip design, featuring substantial coupling strength, enables the integration of spintronic techniques to control the magnon subsystem dynamics via electrical currents. Along this line, in the third work, we theoretically propose a novel microwave oscillator device: a spin-torque-oscillator maser, which combines a spin-torque oscillator with a resonant cavity. This device aims to overcome the limitations of area, power, and linewidth inherent to traditional spin-torque nano-oscillator devices. In the fourth work, we experimentally realize a tunable magnon-photon hybrid system that leverages the spin-torque effect to electrically modulate magnon dissipation. We observe distinct linewidth modulation effects in systems with different cooperativities. Finally, we suggest methods to enhance the efficiency of magnon dissipation tuning while reducing power consumption, thereby laying groundwork for the development of spin-torque-oscillator masers. This thesis work serves as a foundation for future advancement of hybrid magnonic systems, highlighting their potential for both fundamental research and practical device applications.

Multimodal Representation Learning for Agentic AI Systems

Sun, 01 Sep 2024 00:00:00 GMT

Multimodal Representation Learning for Agentic AI Systems Andonian, Alexander Modern artificial intelligence (AI) is poised to transform the scientific process, from ideation and experimentation to peer review. Many researchers posit that emerging generalist AI “agents” will soon no longer be mere tools, but equal partners in scientific exploration. In this work, we contribute to this evolving landscape through converging lines of research focused on developing and evaluating more efficient and interpretable AI systems, spanning both vision and language domains, and their applications to scientific evaluation and review. Our research focuses on three key areas. First, we introduce a novel framework to enhance the efficiency and robustness of cross-modal representation learning methods. Our approach utilizes progressive self-distillation and soft image-text alignments to model the many-to-many correspondences found in noisy web-harvested datasets. Extensive evaluation demonstrates that our method consistently outperforms CLIP across multiple benchmarks, including improved robustness to natural distribution shifts. We extend this framework to zero-shot open vocabulary detection, introducing augmentation, architectural and self-training strategies for improving vision-text feature alignment. Evaluation on long-tail detection benchmarks demonstrates state-of-the-art performance, with competitive performance for unseen classes, as well as superior transfer to additional datasets. Finally, we present the Review Integrated Scientific Evaluation (RISE) benchmark, a novel framework for assessing AI performance in understanding, critiquing, and providing constructive feedback on scientific manuscripts. Our study compares AI-generated reviews against human expert evaluations, revealing both the promising capabilities and current limitations of AI in scientific peer review. The dissertation concludes by proposing future directions for AI-accelerated science, emphasizing the need for collaborative human-AI scientific communities and the development of evaluation methods for higher-level autonomous capabilities in scientific domains. Altogether, this work contributes to the ongoing discourse on AI’s role in scientific research and paves the way for more rigorous integration of AI systems into the scientific process.

A biogeochemical investigation of Trunk River Lagoon, Falmouth, Massachusetts

Wed, 01 May 2024 00:00:00 GMT

A biogeochemical investigation of Trunk River Lagoon, Falmouth, Massachusetts Dumit, Diana This thesis delves into the intricate dynamics of lipid biomarker creation, deposition and preservation within Trunk River Lagoon, encompassing sediments, microbial blooms, and mats. Through a multi-faceted approach, the research uncovers the interplay between natural processes and anthropogenic influences, shedding light on the evolutionary trajectory of this aquatic ecosystem. From ancient sediment records to contemporary microbial communities, each aspect offers unique insights into environmental changes and the implications for interpreting biomarker signals In chapter 2 we employ radiocarbon dating, stable isotope geochemistry, and lipid biomarker analyses on a 2-meter sediment core spanning 3000 years, revealing shifts from a freshwater to a brackish environment with evidence of anthropogenic contamination. In Chapter 3, biomarker analyses on active blooms unveil a diverse microbial community receiving organic inputs from various sources, including sewage. Moreover, lipid analyses reveals rapid sulfurization of organic matter in the water column. In Chapter 4, attention turns to the preservation of biolipids within modern microbial mats. Through detailed analysis, the study reveals primary diagenetic processes such as hydrogenation and sulfurization, highlighting the complexities involved in interpreting biomarker distributions accurately. Overall, this research underscores the necessity of comprehensive lipid analysis in modern environments to accurately interpret biomarker distribution and abundance. These findings not only advance our understanding of sedimentary records and biomarker signals but also emphasize the complex interplay between natural processes and anthropogenic influences in shaping contemporary aquatic ecosystems.

On the Acquisition of Formal Semantics in Statistical Models of Language

Sun, 01 Sep 2024 00:00:00 GMT

On the Acquisition of Formal Semantics in Statistical Models of Language Jin, Charles C. The increasingly impressive performance of recent large language models raises a crucial question: to what extent can such models, trained solely on text, develop an understanding of language grounded in the semantics of the underlying domain? Progress on this question carries significant practical and philosophical implications for the relationship between meaning, understanding, and the capacity to exhibit seemingly intelligent behavior. This thesis makes two primary contributions. First, it develops a scientifically rigorous approach to studying what statistical models of language can understand about language based on the formal semantics of programming languages. Specifically, it leverages the probing classifiers framework: training small classifiers to find encodings of program semantics within the model's internal representations. A main insight is that the clean separation between syntax and semantics in this domain allows for greater control in experimental design. It introduces two new techniques. The first, semantic probing interventions, is a general methodology for distinguishing whether the probe's measurements reflect (1) the learned representations of the language model encode semantics or (2) that the probe itself has learned to infer semantics from representations of pure syntax. The second, latent causal probing, is a formal framework for probing that provides a robust empirical methodology for studying whether language models are able to access the latent concepts that underlie the text they observe during training. A key innovation is to create a single structural causal model that unifies (1) the data generation process underlying the text used to train the language model and (2) the steps of a probing experiment. This makes it possible to conduct a causal analysis that intervenes on the data generation process to trace the influence of the latent variables in the training data through the model's internal representations. The second core contribution of this thesis consists of a series of experimental studies. Specifically, we train a language model on a synthetic grid-world navigation task, then probe the model's learned representations for encodings of the unobserved, intermediate world states. By leveraging the techniques we develop, the results deliver strong empirical evidence that statistical models of language are latent concept learners: capable of inducing the latent variables that underlie the generation of their training data, despite being trained only to model a conditional distribution over tokens.

Language Evolution for Parallel and Scientific Computing

Sun, 01 Sep 2024 00:00:00 GMT

Language Evolution for Parallel and Scientific Computing Churavy, Valentin Scientists, working on the biggest problems facing humanity today, write and run largescale computer simulations. It has been a decades’ long dream of both scientists and programming language designers to make the development for and usage of high-performance computing easier. Many attempts have failed, perhaps because this is a hard problem, perhaps because the social motivation and the required steps to achieve success have not come together, and perhaps solutions to date only solve part of the problem in essence never fully solving the problem. This thesis proposes that there is a combination of features necessary to form a solution. Starting from a bedrock that combines performance with high-level abstractions in a single language. The language needs to enable composable abstractions, or we are doomed to keep developing the single-shot applications of the past. These abstractions should enable code reuse for different forms of compute architectures, to allow users to keep up with the fluid landscape of accelerators. These abstractions should enable code reuse for different mathematical objects such as dense, sparse and structured matrices. These abstractions should enable code reuse for differentiable programming, to enable integration of techniques like sensitivity analysis and scientific machine learning. With the right methodology, these abstractions can compose with each other and specialize to the domain. I will demonstrate that the combination of high-level array-based abstractions and a lowlevel performance portable kernel programming framework form a potent combination for large-scale scientific computing. I will show its efficacy using real-world scientific codes. Furthermore, I will introduce a differentiable programming framework built on top of a general automatic differentiation engine operating on compiler level. The automatic differentiation framework outperforms state-of-the-art, is capable of synthesizing gradient functions from GPU kernels, and can differentiate a wide variety of parallel constructs. As the infrastructure supporting this language needs to be more sophisticated than those of yesteryear, new problems arise. This thesis solves some of these problems and demonstrates their solution on a fluid dynamics code used in climate modelling as one of many imaginable applications.

Composing Foundation Models for Decision Making

Sun, 01 Sep 2024 00:00:00 GMT

Composing Foundation Models for Decision Making Ajay, Anurag Recent advancements in conditional generative modeling have enabled models like DALLE and GPT-4 to generate high-resolution images and coherent text from brief prompts. However, developing a foundation model for decision-making is hindered by the scarcity and expense of collecting paired visual, language, and action data. To address this challenge, this thesis proposes a scalable alternative: a compositional model architecture that leverages separately trained expert models specializing in language, vision, and action. By reducing the need for extensive paired data collection, this approach maintains efficiency in solving novel decision-making tasks while mitigating the data scarcity problem. Our compositional foundation model employs a large language model for task planning, a video diffusion model to generate detailed video trajectories, and an inverse dynamics model to map videos into actions. We demonstrate the effectiveness of this approach in the context of table-top manipulation tasks. Furthermore, given the application of foundation models across various embodied agents, there is a growing need for systematically evaluating these models’ "common sense" understanding of the world. This evaluation is crucial for the successful deployment of embodied agents in real-world scenarios. To address this need, we introduce the first open-vocabulary benchmark for Embodied Question Answering (EQA). This benchmark assesses the foundation models’ ability to comprehend and reason about the world. In summary, by addressing data scarcity in developing foundation models for decision-making and establishing a benchmark for evaluating the reasoning capabilities of embodied agents, this thesis aims to advance the development of foundation models for decision-making.

Towards a Unified Framework for Visual Recognition and Generation via Masked Generative Modeling

Sun, 01 Sep 2024 00:00:00 GMT

Towards a Unified Framework for Visual Recognition and Generation via Masked Generative Modeling Li, Tianhong Recognition and generation are two key tasks in computer vision. However, recognition and generative models are typically trained independently, which ignores the complementary nature of the two tasks. In this thesis, we present a unified framework for visual data recognition and generation via masked generative modeling, and further demonstrate its superior power to address challenges across various applications. We will begin with MAGE, a novel framework that unifies image generation and recognition while achieving state-ofthe-art performance on both tasks. We then extend it into vision-language multi-modal training through ITIT, which utilizes unpaired image and text data to train models capable of high-quality, bidirectional image-text generation – the recognition power enables accurate image-to-text captioning, while the generation power enables realistic text-to-image generation. Moreover, inspired by the synergy between image generation and recognition observed in MAGE, we introduce RCG, a framework that enhances the quality of unconditional image generation to the same level of class-conditional generation, by using representations learned in a self-supervised manner to guide the generative process. Lastly, we introduce Reparo to address the challenge of packet loss in video conferencing with the help of masked generative modeling, enabling the reconstruction of lost video data without traditional error correction methods. This ensures high-quality communication even under conditions of substantial data loss. These works demonstrate the power of the proposed unified framework, to not only push forward the state-of-the-art in individual downstream applications but also to provide robust, versatile solutions adaptable to a wide range of real-world problems in computer vision and beyond.

Low-cost Agents with Language Perception and Dynamic Inference

Sun, 01 Sep 2024 00:00:00 GMT

Low-cost Agents with Language Perception and Dynamic Inference Pan, Bowen Designing efficient artificial intelligence agents presents significant challenges, particularly in terms of learning and inference costs. Traditional agents often suffer from high learning expenses due to their limited ability to generalize across diverse tasks and environments. Recent advances in large language models (LLMs) have shown strong generalization capabilities by leveraging high-level abstractions of the world through language. In this thesis, we propose leveraging language as a perceptual representation to enable LLM-based agents to perform vision-language navigation tasks with reduced data collection costs. We demonstrate that language not only facilitates the generation of efficient synthetic data but also serves as a bridge to minimize domain gaps between different environments. However, transformer-based agents are burdened with high inference costs, especially when handling long-horizon visual content. To mitigate this, we introduce two strategies: (1) reducing visual input redundancy through dynamic token selection, and (2) accelerating model inference using a memory-efficient Mixture of Experts (MoE) architecture. Together, these approaches offer a robust framework for enhancing both learning and inference efficiency in LLM agents.

Safe and Ethical Implementation of Intelligent Systems

Sun, 01 Sep 2024 00:00:00 GMT

Safe and Ethical Implementation of Intelligent Systems Dai, Zheng In the year 2024, the prospect of solving human level tasks using intelligent systems is no longer the subject of science fiction. As these systems play an increasingly critical role in our day-to-day lives, it becomes ever more important to consider the safety and ethics surrounding their implementation. This is a multifaceted challenge spanning multiple disciplines, involving questions at the regulatory, engineering, and theoretical levels. This thesis discusses three projects that span these levels. We first explore the problem of tracing causal influence from training data to outputs of generative models. In our exploration we encounter the phenomenon of unattributability, and consider its scientific and regulatory implications. We next tackle the challenge of designing a high diversity library of therapeutics that is depleted of dangerous off-target binders using intelligent systems, developing a suite of inference and optimization tools along the way. Finally, we derive universal bounds for the robustness of image classifiers that inform us of how safe these intelligent systems can be in theory. Together, these projects present a multilevel overview of the safe and ethical implementation of intelligent systems.

Maliciously Secure Computation, Theory and Practice

Sun, 01 Sep 2024 00:00:00 GMT

Maliciously Secure Computation, Theory and Practice de Castro, Leo Data analytics fuels countless innovations and reveals unparalleled insights, and these benefits only grow the more data is amassed. This has resulted in the size of datasets and the compute needed to manage them becoming too resource-intensive for even large companies to handle alone, fueling the rise of cloud computing and outsourced data management. A central problem with this outsourcing is security. How can parties ensure that an untrusted cloud is accurately running the prescribed protocol? More generally, how can two parties collaborate to run a computation over joint inputs, where both inputs remain private while still delivering the correct output? This thesis focuses on answering these questions by constructing secure computation protocols with low communication & computation overhead. The protocols in this thesis include several concretely efficient constructions of private information retrieval, a functional commitment scheme for all functions, and a general two-party secure computation scheme that comes within polylogarithmic factors of the optimal communication and computation complexity. In addition to their efficiency, all protocols presented in this thesis guarantee protection against worst-case, malicious adversaries.

Interactive Spin Dynamics in Magnon and Quantum Spin Systems

Sun, 01 Sep 2024 00:00:00 GMT

Interactive Spin Dynamics in Magnon and Quantum Spin Systems Hu, Zhongqiang Spintronics utilizes the intrinsic spin of electrons to design next-generation electronic devices, reducing power consumption and enabling innovative computing functions. Over the past decades, significant research interest has been directed toward two types of spin-based systems: collective excitations of spins, known as spin waves or magnons, in magnetic materials, and optically active spin defects as represented by nitrogen-vacancy (NV) centers in diamond, leading to the prosperity of magnonics, quantum sensing, and quantum information processing. As the understanding of dynamics in individual spin systems has deepened, recently there has been an increasing interest in the interactive dynamics within hybrid spin systems. This shift in focus reflects an increasing curiosity about how these complex interactions can be harnessed to further advance their microwave and quantum applications. However, several challenges persist, including the limited coherence length of magnons and the restricted frequency range of NV-based magnetometers, which will be tackled in this thesis. We first leverage the chirality of interlayer magnetic dipolar interactions to introduce an easily implementable system—antiparallel aligned magnetic multilayers—for realizing topological magnonic surface states and low-dissipation spin current transport in a tunable manner. We then expand the frequency window of NV-based magnetometers using nonlinear microwave-spin interactions, offering novel functionalities in quantum state control and sensing. We further exploit nonlinear spin dynamics by hybridizing NV centers with magnonic thin films, which not only amplifies the intensity of nonlinear resonance signals that are intrinsic to NV spins, but also enables novel frequency mixings through parametric pumping and nonlinear magnon scattering effects. We believe our study of interactive spin dynamics in hybrid systems involving magnons, quantum spin defects, and microwave photons help optimize these systems for a wide range of applications in both classical and quantum domains.

Modeling rational agents with limited capability

Sun, 01 Sep 2024 00:00:00 GMT

Modeling rational agents with limited capability Jia, Kai In many scenarios, players exhibit inherent limitations in various aspects of their capability to generate maximally rational play in strategic games. Modeling such capability limitations and elucidating their implications will advance our understanding of the strategic interactions among players. In this thesis, I study two novel settings where players have limited capabilities. I formalize a hierarchy of capabilities and study related equilibrium concepts, computational complexity, solution algorithms, and the impact of varying capabilities on game outcomes. The first limited-capability setting is limited-perception games. I focus on a class of oneshot limited-perception games. Such games extend simultaneous-move normal-form games by presenting each player with an individualized perception of the true game. Players’ payoffs are determined by the true game hidden from players. The accuracy of a player’s perception is determined by the player’s capability level, with a higher level corresponding to a more accurate perception. I study both capability-oblivious and capability-aware players. A capability-oblivious player does not know they have limited perception and therefore plays the optimal strategy of their perceived game. I present payoff bounds and other predictable behavior of capability-oblivious players in a special class of limited-perception games. A capability-aware player reasons with the set of possible true payoff functions and other players’ perceptions and incentives to maximize their own objective (e.g., the worst-case payoff) based on their limited perception. I present novel formalizations of simultaneousmove equilibria and show the hardness of equilibrium solving. I further present positive results that (i) an approximate equilibrium has a compact, tractable representation; and (ii) a few classes of zero-sum games can be efficiently solved. The aforementioned efficiently solvable zero-sum games are reduced to solving nonsmooth convex programs. To this end, I present the Trust Region Adversarial Functional Subdifferential (TRAFS) algorithm for constrained optimization of unstructured nonsmooth convex Lipschitz functions. Unlike previous methods that assume a subgradient oracle model, I propose the functional subdifferential, defined as a set of subgradients that simultaneously captures sufficient local information for effective minimization while being easy to compute for a wide range of functions. Intriguingly, the TRAFS design also incorporates game-theoretical thinking. In each iteration, TRAFS solves a zero-sum game between the optimizer and a local approximation of the objective function to guarantee progress. The optimizer has access to step vectors in a local ℓ2 -bounded trust region; the local approximation uses the functional subdifferential. TRAFS finds an approximate solution with an absolute error up to ϵ in O(1/ϵ) or O(\sqrt{1/ϵ}) 1/ϵ iterations depending on whether the objective function is strongly convex, improving the previously best-known bounds of O((1/ϵ)^2) and O(1/ϵ) in these settings. TRAFS makes faster progress if the functional subdifferential satisfies a locally quadratic property; as a corollary, TRAFS achieves linear convergence (i.e., O(log 1/ϵ)) for strongly convex smooth functions. In the numerical experiments, TRAFS solves twice as many problems compared to the second-best method and is on average 39.1x faster on problems solved by both methods. The second limited-capability setting is limited-strategy games where a player’s capability limits the strategies available to them. I work with a formalization where a player’s strategy space is defined as programs in a Domain-Specific Language (DSL). A player’s capability limits the size of programs available to that player. I focus on characterizing the impact of player capability on game outcomes. I study a new game model called McDncDa derived from network congestion games. I show that it is computationally hard to determine whether an McDncDa instance is capability-positive (i.e., whether increasing a player’s capability level leads to a better payoff). I then study a parameterized special class of McDncDa called MGMG. I show that MGMG is always capability-positive, and it is socially capabilitypositive (i.e., the sum of all players’ payoffs always gets better if every player’s capability level is increased by one) if some resources in the game have increasing returns to scale despite the existence of multiple equilibria.

Making Sense of Training Large AI Models

Sun, 01 Sep 2024 00:00:00 GMT

Making Sense of Training Large AI Models Ahn, Kwangjun Today, one of the most impressive applications of optimization is the training of large AI models. But currently such models are trained with ad-hoc heuristics at a very large computational cost, mainly due to lack of understanding of their working mechanisms. In this thesis, we conduct a systematic study of large-model optimization, crucially informed by practical applications. The first part investigates two interesting phenomena regarding optimization of Transformer-based models, one of the most popular architectures for language modeling. We investigate how training Transformer-based models can lead to remarkable properties such as in-context learning, and we further discuss the main challenges associated with Transformer training. The second part of this thesis focuses on understanding the Adam optimizer, one of the most popular algorithms for training large models. We offer a new view on Adam based on an online learning perspective that elucidates the importance of Adam’s algorithmic components. Building on this new perspective, we also prove that Adam achieves the optimal convergence rate in various non-convex optimization settings, both smooth and non-smooth settings. The third part of this thesis focuses on the unstable convergence phenomenon in training large models. We identify its main characteristics from first principles, and discuss its causes and implications for learning. We then discuss its connection to popular flat minima optimization algorithms, and initiate a formal study of them by defining a formal notion of flat minima, and analyzing the complexities of finding them.

Convex Network Flows

Sun, 01 Sep 2024 00:00:00 GMT

Convex Network Flows Diamandis, Theo This thesis introduces a new framework for flow problems over hypergraphs. Our problem formulation, which we call the convex flow problem, only assumes that the constraints on the flows over each edge are in some convex set. The objective is to maximize a sum of concave utility functions---one for the net flow at every node and one for each edge flow---subject to these constraints. This framework not only includes many classic problems in network optimization, such as max ﬂow, min-cost ﬂow, and multi-commodity flows, but also generalizes these problems to allow, for example, concave edge gain functions. As a result, our framework includes applications spanning a number of fields: optimal power ﬂow over lossy networks, routing and resource allocation in ad-hoc wireless networks, Arrow-Debreu Nash bargaining, and order routing through financial exchanges, among others. This problem has a number of interesting properties, including a 'calculus' of flow sets, an equivalent conic form, and a natural generalization of many classic network flow results. We develop an efficient algorithm for solving the convex flow problem by constructing a particular dual problem that decomposes over the edges of the hypergraph. This dual problem has a number of useful interpretations and admits a straightforward specification: the dual function and its gradient can be evaluated using only simple subroutines which often have closed-form solutions. These subroutines suggest a clean, easy-to-use problem interface, which we provide in the open-source software package ConvexFlows.jl, written in the Julia programming language. We discuss implementation considerations, including how to handle important special cases, and we provide a simple interface for specifying convex flow problems. We show that our solver is significantly faster than the state-of-the-art commercial optimization solver Mosek, even for small problems sizes with limited parallelization. Finally, we consider the nonconvex flow problem with fixed costs on the edges, i.e., where there is some fixed cost to send any nonzero flow over an edge. We show that this problem has almost integral solutions by a Shapley--Folkman argument, and we provide a simple modification of our original algorithm for this nonconvex problem. We conclude by discussing a number of interesting avenues for future work.

Parsimonious Principles of Deep Neural Networks

Sun, 01 Sep 2024 00:00:00 GMT

Parsimonious Principles of Deep Neural Networks Huh, Minyoung At the core of human intelligence lies an insatiable drive to uncover the simple underlying principles that govern the world’s complexities. This quest for parsimony is not unique to biological cognition but also seems to be a fundamental characteristic of artificial intelligence systems. In this thesis, we explore the intrinsic simplicity bias exhibited by deep neural networks — the powerhouse of modern AI. By analyzing the effective rank of the learned representation kernels, we unveil the observation that these models have an inherent preference for learning parsimonious relationships in the data. We provide further experimental results to support the hypothesis that simplicity bias is a good inductive bias for finding generalizing solutions. Building upon this finding, we present the Platonic Representation Hypothesis — the idea that as AI systems continue to grow in capability, they will converge toward not only simple representational kernels but also a common one. This phenomenon is evidenced by the increasing similarity of models across domains, suggesting the existence of a Platonic “ideal” way to represent the world. However, this path to the Platonic representation necessitates scaling up AI models, which poses significant challenges regarding computational demand. To address this obstacle, we conclude the thesis by proposing a framework for training a model with parallel low-rank updates to effectively reach this convergent endpoint.

Building Strategic AI Agents for Human-centric Multi-agent Systems

Sun, 01 Sep 2024 00:00:00 GMT

Building Strategic AI Agents for Human-centric Multi-agent Systems Jacob, Athul Paul This thesis addresses the challenge of developing strategic AI agents capable of effective decision-making and communication in human-centric multi-agent systems. While significant progress has been made in AI for strategic decision-making, creating agents that can seamlessly interact with humans in multi-agentic settings remains a challenge. This research explores the limitations of current approaches, such as self-play reinforcement learning (RL) and imitation learning (IL), and proposes novel methods to overcome these constraints. Modeling human-like communication and decision making is a crucial first step toward building effective strategic agents. The initial part of the thesis addresses this through two approaches. We start by developing a regret minimization algorithm for modeling actions of strong and human-like agents called piKL, which incorporates a cost term proportional to the KL divergence between a search policy and a humanimitation learned policy. This approach improves reward while keeping behavior close to a human-imitation learned policy, producing agents that predict human actions accurately while improving performance in the benchmark game of no-press Diplomacy. Then, we develop a procedure for modeling populations of agents that communicate with humans using natural language. Our sample-efficient multitask training scheme for latent language policies (LLPs) improves the reward obtained by these policies while preserving the semantics of language in a complex real-time strategy game. Building on these foundations, the second part of the thesis focuses on building strategic agents for human-centric multi-agent domains. The research introduces the DiL-piKL planning algorithm and its extension, RL-DiL-piKL, which regularize self-play reinforcement learning and search towards a human imitation-learned policy. These algorithms enable the training of Diplodocus, an agent achieving expert human-level performance in no-press Diplomacy. A significant milestone is reached with Cicero, the first AI agent to achieve human-level performance in full-press Diplomacy, integrating a language model (LM) with planning and reinforcement learning algorithms based on piKL. The final part of the thesis revisits language generation tasks, applying piKL to model pragmatic communication and improving LM truthfulness. It presents Regularized Conventions (ReCo), a model of pragmatic language understanding that outperforms existing best response and rational speech act models across several datasets. Furthermore, a novel approach to LM decoding is introduced, casting it as a regularized imperfect-information sequential signaling game. This results in the equilibrium-ranking algorithm, which consistently improves performance over existing language model decoding procedures.

Characterizing Variation in Healthcare across Time and Providers using Machine Learning

Sun, 01 Sep 2024 00:00:00 GMT

Characterizing Variation in Healthcare across Time and Providers using Machine Learning Ji, Christina X. Modeling healthcare decisions and their outcomes is a complex problem. In addition to being affected by patient characteristics, the prognosis can vary depending on when the patient is receiving care, and treatment decisions can vary depending on who makes the decisions. In this thesis, we consider two axes of variation in healthcare: over time and across providers. For both axes, we focus on identifying when variation exists, characterizing the patients who are affected by such variation, and addressing shifts due to this variation. The solutions we propose draw ideas from causality and dataset shift. In the first part of this thesis, we address these three aspects for variation over time. First, we create an algorithm that can detect when a model is affected by change over time and identify sub-populations where the model is more affected. We use our algorithm to perform a large-scale study of temporal shifts in health insurance claims. We demonstrate changes over time are prevalent in healthcare and examine case studies to better understand the drivers of such changes. Next, we examine how to learn a model that can perform well on current data. As data from the current time period is limited, we consider several methods that can leverage sequences of historical data to learn a good image classification model for the final time step. We build a benchmark for evaluating these methods on sequences constructed from synthetic shifts and validate our conclusions on a real-world dataset. In the second part of this thesis, we address similar questions for variation across providers. First, we create a statistical approach to test whether significant variation exists across providers. Our approach involves learning a model of treatment decisions with provider-specific random effects. We perform a case study on first-line type 2 diabetes treatment and find significant variation exists across providers. Then, we develop an algorithm for identifying regions of patients with the most disagreement between providers. We formalize this as a causal inference problem, where disagreement is defined by the causal effect of the provider on the treatment decision. We illustrate this algorithm on first-line type 2 diabetes and Parkinson's treatment decisions and uncover regions of variation that align with uncertainty in clinical guidelines. In the third part of this thesis, we build a tool for examining the effects of variation over time or across providers for individual patients. We use a large language model built on electronic health record concepts to generate patient trajectories. To enable interventions on time and provider, we introduce new tokenizations for these concepts. We also incorporate a structural causal model for patient visits to allow for generation of interventional and counterfactual trajectories. We hope the model in this part of the thesis can be used to answer additional questions about how patient trajectories would change if they were treated during a different time period or by a different provider.

Programmable Interactions between Optical Fields and Atom-like Systems in Integrated Circuits

Sun, 01 Sep 2024 00:00:00 GMT

Programmable Interactions between Optical Fields and Atom-like Systems in Integrated Circuits Larocque, Hugo Photons can interact with a wide variety of quantum systems and their ability to more easily preserve their coherence makes them ideal candidates for transmitting information between remote quantum information processors. Photonic integrated circuits (PICs), which can be manufactured with modern semiconductor fabrication, provide a platform in which such interactions can occur at scale. Implementing integrated devices enabling these interactions within programmable and scalable settings while preserving a sufficient amount of strength continues to be a general goal in quantum photonics. Here, we implement device designs and architectures that improve current limits on the programmability and scalability of three types of optical interactions. More specifically, we explore the use of programmable multimode interference as a means for unitary transformations onto a set of optical spatial modes, optical resonators for high-extinction coherent modulators driven by RF signals, and large-scale silicon photonics for interacting with hybrid integrated quantum dot emitters.

Intelligent Textiles for Physical Interactions

Sun, 01 Sep 2024 00:00:00 GMT

Intelligent Textiles for Physical Interactions Luo, Yiyue Human-environment interaction is a fundamental aspect of our daily lives, involving the constant use of our sensory and motor systems to extract, process, and communicate information. However, capturing, analyzing, and reproducing these interactions pose significant challenges due to their pervasive, variable, and prolonged nature, as well as their unique character for each individual. Despite these challenges, it is essential to develop systems that can accurately capture and reproduce human-environment interactions for a wide range of applications, including human behavior studies, health monitoring, human-computer interactions, and robot imitation learning. This thesis focuses on developing seamlessly integrated, scalable manufactured sensing and actuating systems, as well as advanced computational pipelines to capture, analyze, and reproduce adaptive ubiquitous physical human-environment interactions.

Sparse and Structured Tensor Programming

Sun, 01 Sep 2024 00:00:00 GMT

Sparse and Structured Tensor Programming Ahrens, Willow From FORTRAN to NumPy, tensors have revolutionized how we express computation. However, tensors in these, and almost all prominent systems, can only handle dense rectilinear grids of values. Real-world tensors are often structured, containing patterns which allow us to optimize storage or computation, such as sparsity (mostly zero), runs of repeated values, or symmetry. Specializing implementations for structure yields significant speedups, but support for structured tensors is fragmented and incomplete. The heart of the problem is coiteration, simultaneously iterating over multiple tensors in a program, where each tensor format may have different internal structure. As each combination of structures requires a unique coiteration algorithm, existing frameworks struggle to abstract over the design space, instead hard-coding support for a few programs and/or a few structures. In this thesis, we build an abstraction for coiteration, enabling us to support both a wide range of programs and diverse tensor structures. We use a language, looplets, to describe the structure of tensors in tensor programs. Looplets allow the compiler to generate code to coiterate over any combination of structured tensor formats. The looplets language decomposes loops over sparse and structured formats hierarchically. This decomposition simplifies compilation, allowing us to capture key mathematical properties (such as x∗0 = 0, which motivates sparsity) with simple term rewriting. Building on looplets, we introduce a new language, Finch, for general structured tensor programming. Finch makes it easier to compute with structured tensors by combining program control flow and tensor structures into a common representation where they can be co-optimized. Finch automatically specializes control flow to data so that performance engineers can focus on experimenting with many algorithms. Finch supports a familiar programming language of loops, statements, ifs, breaks, etc., over a wide variety of tensor structures, such as sparsity, run-length-encoding, symmetry, triangles, padding, or blocks. Finch reliably utilizes the key properties of each structure, making it easier to write and optimize structured tensor programs. In our case studies, we show that this leads to dramatic speedups in diverse applications, including linear algebra, image processing, and graph analytics. Our abstracted design makes it easier to extend Finch to new tensor structures and programming models. Finch has been separately extended to support a DSL for symmetry-aware tensor programs and to support real-valued indexing.

Paths to AI Accountability: Design, Measurement, and the Law

Sun, 01 Sep 2024 00:00:00 GMT

Paths to AI Accountability: Design, Measurement, and the Law Cen, Sarah H. Algorithmic systems are increasingly intervening on human interactions and decisions, from selecting the content users see on social media to helping hiring managers choose candidates to interview. In recent years, the falling barrier between humans and AI has sparked fears about AI’s capabilities and elicited questions about the role that algorithms and, increasingly, AI should play in our lives. As society continues working towards answering these questions, this thesis argues that we must construct paths to AI accountability by determining who owes responsibility to whom in the AI ecosystem, upholding these responsibilities, and enforcing them. Pursuing AI accountability allows us to innovate while still acknowledging that AI is a technology developed and wielded by human actors. Furthermore, by focusing on the responsibilities of human actors, this approach builds on existing social and legal frameworks of accountability. Within this vast, multidisciplinary research area, this thesis centers on three aspects of AI accountability: design, measurement, and the law. In Part I, we examine the importance of designing responsible AI systems from the ground up, which involves exploring definitions of responsibility, methods for achieving them, and the ramifications (e.g., trade-offs) of responsible design. As demonstrations of design, we study three different contexts. Each context builds on a notion of responsibility, and we investigate how these notions—which include trustworthiness, fairness, and social welfare—arise and interact. We provide formal definitions of each notion, discuss their implications, and propose interventions for achieving them. In Part II, we turn our attention to AI measurement: quantifying AI behaviors and effects through systematic observations and procedures. We illustrate the importance of AI measurement through three case studies: (i) a black-box audit for social media algorithms; (ii) an estimator and experiment design for individual treatment effect estimation in the presence of spillover; and (iii) a user study testing whether users adapt to their recommender systems. In this part, we show how measurement can play a crucial role in compliance testing, analyzing AI behavior, and producing evidence that can inform decision-making (e.g., policy). In Part III, we discuss how the law can align incentives with AI accountability as well as challenges in realizing AI accountability in practice. We center our discussion on two works. The first seeks to fill a gap in the law around AI that arises from AI’s unintuitive and opaque nature, and argues that AI decision-subjects have a substantive right in the age of AI that we term the “right to be an exception.” While the first work studies a gap in the law, the second tackles practical challenges in carrying out the law. It examines how lacking both transparency and access to AI systems can frustrate the ability to monitor, evaluate, and audit AI systems.

Utilization and Synthesis of Symbolic World Models for Safe, Generalizable, and Efficient Action

Sun, 01 Sep 2024 00:00:00 GMT

Utilization and Synthesis of Symbolic World Models for Safe, Generalizable, and Efficient Action Hunt, Nathan Reinforcement learning with neural networks has proven incredibly flexible at learning to act in diverse environments. Model-based RL techniques have helped to ameliorate the dependence on large quantities of data that these models normally have. However, despite their flexibility, neural world models have several drawbacks. Symbolic world models, in comparison, are easier to verify (e.g. for safety concerns), more compatible with domain-independent planning techniques, and able to be learned or adapted with more limited data. In this thesis, I will demonstrate these advantages of symbolic world models in three projects. The first, VSRL, shows how we can use a symbolic world model to ensure that an RL policy is safe during both training and deployment and promote safe exploration. The second, SPARSER, presents a hybrid domain planner which uses world models in a planning domain description language. It showcases how we can exploit the event structure in the world model to enable more efficient planning. In the final project, PWM, I will explore learning a world model directly from observations and actions gathered from interacting with an environment. We combine symbolic and neural synthesis techniques to enable efficient world model synthesis even from visual observations. Together, these projects demonstrate the versatility and value of symbolic world models.

The growth characteristics of indium antimonide as revealed by chemical etching and x-ray anomalous transmission.

Thu, 01 Jan 1970 00:00:00 GMT

The growth characteristics of indium antimonide as revealed by chemical etching and x-ray anomalous transmission. Miller, David Christopher. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Metallurgy and Materials Science, 1970; Vita.; Bibliography: leaves 122-127.

Photochemistry of organometallic compounds : generation and reactions of 16e- and 17e- intermediates

Sun, 01 Jan 1989 00:00:00 GMT

Photochemistry of organometallic compounds : generation and reactions of 16e- and 17e- intermediates Young, Kent Maxwell. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1989; Title as it appears in the M.I.T. Graduate List, June 1989: Photochemistry of organometallic complexes--generation and reactions of 16e- and 17e- intermediates.; Includes bibliographical references (leaves 164-166).

Forced vibrations of a single stage axial compressor rotor.

Sun, 01 Jan 1978 00:00:00 GMT

Forced vibrations of a single stage axial compressor rotor. Fabunmi, James Ayinde. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 1978; Includes bibliographical references.

Mapping the murine leukemia virus genome.

Sun, 01 Jan 1978 00:00:00 GMT

Mapping the murine leukemia virus genome. Faller, Douglas Vincent. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biology, 1978; Vita.; Bibliography: leaves 182-188.

Fundamental groups of algebraic stacks

Sat, 01 Jan 2000 00:00:00 GMT

Fundamental groups of algebraic stacks Noohi, Behrang, 1973- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2000; Includes bibliographical references (p. 57).

Magnetic scattering of neutrons by hexagonal cobalt

Tue, 01 Jan 1963 00:00:00 GMT

Magnetic scattering of neutrons by hexagonal cobalt Moon, R. M. (Ralph Marks) Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1963; Vita.; Includes bibliographical references (leaves 97-98).

Spatiotemporal Signatures of Elastoinertial Turbulence

Sun, 01 Sep 2024 00:00:00 GMT

Spatiotemporal Signatures of Elastoinertial Turbulence Yamani, Sami The addition of small amounts of polymers to a Newtonian solvent makes the fluid viscoelastic, and can lead to significant drag reduction in high-speed flows. The interaction of viscoelasticity and inertia in a dilute polymer solution results in the emergence of unique inertioelastic instabilities. The nonlinear evolution of these instabilities engenders a state of turbulence with significantly different spatiotemporal features compared to Newtonian turbulence, commonly termed elastoinertial turbulence (EIT). We explore EIT by studying the dynamics of low-speed submerged jets of dilute aqueous polymer solutions injected through a nozzle into a tank of quiescent water or polymer solution. In a free shear layer, fluid elasticity has a dichotomous effect on jet stability depending on its relative magnitude, creating two distinct regimes in which elastic effects can either destabilize or stabilize the jet. For small levels of elasticity an inertioelastic shear-layer instability emerges, in agreement with existing linear stability analysis of viscoelastic jets, which is independent of bulk undulations in the column of fluid forming the jet. The growth of this instability near the edge of the jet destabilizes the flow, advancing the transition to turbulence to lower Reynolds numbers and closer to the nozzle compared to a Newtonian jet. Increasing the fluid elasticity merges this shear-layer instability into a bulk instability of the fluid column. In this regime, elastic tensile stresses in the sheared polymer solution act like an “elastic membrane” that stabilizes the flow, delaying the transition to turbulence to higher levels of inertia and greater distances downstream of the nozzle. In a wall-bounded shear layer, a separate investigation shows that fluid elasticity generates a self-sustained inertioelastic travelling wave within the wall boundary layer under flow conditions at which a Newtonian wall jet remains completely laminar. The phase velocity of this travelling wave decreases as fluid elasticity increases, resulting in the stabilization of the jet. In the fully-developed turbulent state far from the nozzle, viscoelastic jets exhibit unique spatiotemporal features associated with EIT. The time-averaged angle of jet spreading and the center-line velocity of the jet are self-similar with distance from the nozzle, and the similarity scaling coefficients vary with fluid elasticity. The cascade of turbulent eddies has a universal frequency spectrum independent of fluid elasticity. This spectrum is characterized by a power law with an exponent of −3 that is different from the well-known Kolmogorov law with exponent −5/3 for Newtonian turbulence. EIT also modifies the Lagrangian coherent structures that develop in the turbulent flow. Increasing elasticity generates coherent structures that are larger and more elongated in the streamwise direction, consistent with the suppression of streamwise vortices by EIT. On a larger scale, the elongated coherent structures create a stochastic cycle in EIT that consists of active and hibernating turbulent states with alternating strong and weak turbulent fluctuations. Looking ahead, this new fundamental understanding of EIT can be leveraged to explore the potential of biopolymers as cheap and environmentally-friendly drag reducing agents replacing synthetic polymers made from petroleum oil. Biopolymers are typically semiflexible polyelectrolytes with rheological properties that can be adjusted over a wide range by varying conditions such as the solvent quality and/or the ionic strength. We study aqueous solutions of a typical long chain biomacromolecule (Xanthan gum) in canonical shear and extensional flows and quantify how the rheological properties can be tuned by changing the ionic strength of the solvent. In steady shear flow, increasing the biopolymer concentration dramatically increases both the zero shear viscosity and the extent of shear-thinning, while increasing the ionic strength of the solvent, decreases both the zero shear viscosity and the level of shear-thinning. In transient extensional flow, increasing biopolymer concentration increases the extensional relaxation time of the solution, while increasing the ionic strength of the solvent decreases this relaxation time. Based on our insights from this rheological characterization, we demonstrate that injecting a high inertia jet of aqueous biopolymer solution into quiescent environments at different levels of ionic strength can significantly modify the spectral characteristics of the inertioelastic instabilities that develop and lead to a change in the spatiotemporal signatures of elastoinertial turbulence. Our findings lay out a pathway for identifying the most promising biopolymers to serve as biodegradable drag reducing agents for marine vehicles operating in high salinity environments enabling savings in the cost of transport and future reduction in our carbon footprint.

Multiscale design of bioadhesive platforms for next-generation applications in surgery and healthcare

Sun, 01 Sep 2024 00:00:00 GMT

Multiscale design of bioadhesive platforms for next-generation applications in surgery and healthcare Wu, Sarah J. Bioadhesives—materials capable of adhering to biological tissues—hold significant promise as transformative tools in healthcare, offering the ability to repair tissues with ease and minimal damage. These materials present numerous opportunities in surgery and human-machine interfaces, creating a broad landscape of applications that has captivated clinical and scientific interest alike. Still, there remain open challenges surrounding their reliability, biocompatibility, usability, and versatility. These include weak adhesion with wet tissues, foreign body response, cumbersome application processes, and limited customizability. This dissertation presents a multiscale framework for addressing these obstacles, encompassing design strategies on the molecular, polymer network architecture, macroscale device, and application process levels. The implementation of this framework is demonstrated through the development of two pioneering bioadhesive platforms: (1) a multifunctional patch for minimally invasive surgery, and (2) a 3D printable bioadhesive for fabricating tunable, application-specific devices. Together, these platforms expand the design space for creating robust and versatile tissue repair solutions and biomedical devices.

Critical Material Recovery from Salt-Lakes and Spent Batteries with Membranes and Solvents

Sun, 01 Sep 2024 00:00:00 GMT

Critical Material Recovery from Salt-Lakes and Spent Batteries with Membranes and Solvents Foo, Zi Hao The sustainable extraction and recovery of critical metals such as lithium, cobalt, and rare earth elements are essential for advancing renewable energy technologies, electric vehicles, and modern electronics. This thesis addresses the significant environmental, economic, and logistical challenges associated with traditional methods of extracting these metals from primary sources like spodumene ores and continental salt lakes, and secondary sources like spent battery and magnet leachates. Conventional extraction processes from primary sources are highly energy-intensive, environmentally taxing, and pose substantial water usage concerns. In contrast, while secondary sources such as spent lithium-ion batteries offer a promising avenue to alleviate environmental impacts and secure a stable supply chain, they still pose challenges in terms of high chemical usage and waste acid management. This research focuses on advancing three innovative processes: nanofiltration, electrodialysis, and solvent-driven fractional crystallization, aiming to enhance the efficiency and sustainability of metal recovery from both primary and secondary sources. The thesis findings are supported by direct experimental measurements and extensive computation involving multi-ionic and mixed-solvent activity and fugacity coefficient models, fundamental molecular dynamics simulation, multicomponent continuum dynamics ion transport models across nanofiltration and ion exchange membranes, and techno-economic analysis of membrane and solvent processes. First, advancements in nanofiltration technology are explored to pre-treat salt-lake brines for improved lithium extraction efficiency and purity. Positively charged nanofiltration membranes demonstrate enhanced monovalent selectivity through Donnan exclusion, effectively removing multivalent cations and improving lithium purity in the feed brine. Our results show that the Li/Mg selectivity can be enhanced by 13 times with Donnan-enhanced nanofiltration membranes. Our experiments exemplify the Donnan-enhanced membrane’s ability to reduce magnesium concentrations to 0.14 % from salt lakes in a single filtration stage. This method not only increases the yield and quality of extracted lithium but also reduces the environmental impact by minimizing additional purification steps. Second, electrodialysis is investigated for the selective recovery of lithium from complex mixtures like battery leachates. This technique leverages ion mobility differences to retain lithium ions while separating other cations. Bipolar membrane electrodialysis further converts lithium chloride into high-purity lithium hydroxide and hydrochloric acid, which can be recycled, thereby supporting a circular economy in battery recycling. Experimental results demonstrate that selective electrodialysis can achieve ∼99 % lithium purity with 68.8 % lithium retention from Ni-Mn-Co battery leachates. The techno-economic analysis projects LiOH production costs between USD 1.1 to 3.6 per kilogram, approximately an order of magnitude lower than prevailing market prices. Third, the use of dimethyl ether (DME) in solvent-driven fractional crystallization is examined as an innovative method for extracting critical metals. DME’s properties allow for efficient water extraction from aqueous solutions, causing the crystallization of metals like cobalt and nickel. Our computational analysis reveals that DME-based solvent-driven water extraction can concentrate an input saline feed to 5.5 M and regenerate over 99 % of the DME using ultra-low-grade heat below 50°C, with a DME/water selectivity ratio of 125. This process ensures high purity and reduces post-processing needs, offering a more environmentally friendly alternative to traditional solvent extraction techniques. The findings of this thesis underscore the potential of advanced variants of nanofiltration, electrodialysis, and solvent-driven fractional crystallization technologies in promoting sustainable and economically viable critical metal recovery processes. By addressing the pressing issues of environmental degradation and resource scarcity, this research supports the development of a circular resource economy, where waste materials are continuously reused and recycled, contributing to a sustainable energy future.

Design and Modeling of a Catapulting Magnetic Transmission for Tuning Energy Storage and Release

Sun, 01 Sep 2024 00:00:00 GMT

Design and Modeling of a Catapulting Magnetic Transmission for Tuning Energy Storage and Release Thomas, Marcel Adam Craig The purpose of this work is to generate design rules and models for a catapulting magnetic leadscrew transmission. These rules and models empower scientists and engineers with the ability to tune energy storage and release, and thereby increase the peak specific power (power/mass) of an actuator. This enables rapid design and development of lightweight (< 0.5 kg), high peak power (>200 W) actuators. This has the potential to impact powered exoskeletons and force-controlled robotics for rehabilitation and strength augmentation of explosive movements such as locomotion, jumping, and throwing. This thesis provided the following scientific contributions: (i) the concept of a catapulting magnetic screw actuator, (ii) experimentally validated models that are useful for the design and optimization of the magnetic leadscrew, considering both magnetic and structural aspects, (iii) experimentally validated models of the catapulting event in a magnetic leadscrew, and (iv) use of these models in the context of a practical application, namely powered exoskeletons that may reduce the metabolic cost of walking. First, the catapulting magnetic screw is introduced. An equation of motion is derived and experimentally validated. The equation of motion demonstrates that the potential wells in the magnetic screw create a ripple in the power as a function of time. Then, despite the equation of motion being a nonlinear differential equation with no closed-form solution, bounds on the ripple magnitude and frequency are derived. This gives the slip force and the lead needed to meet a specified tolerance on power as a function of time. Then, a model is developed that enables rapid design of a magnetic screw that achieves a desired slip force. This model agrees with finite element analysis to within 10% error across varying each design parameter by multiple orders of magnitude. Then, given a magnetic screw, a structure is needed to be sufficiently stiff to keep the magnets from sticking together. Models of the magnetic stiffness matrix and structural stiffness matrix and simplifications thereof are given to ensure sufficient structural stiffness. Finally, the catapulting event may be too fast for a desired application, so it is shown how nonlinear springs may be used to meet requirements for powered exoskeletons that assist in walking.

Design and Evaluation of a Powered Series-Elastic Cycloidal Ankle (CyAn) Prosthesis

Sun, 01 Sep 2024 00:00:00 GMT

Design and Evaluation of a Powered Series-Elastic Cycloidal Ankle (CyAn) Prosthesis Du, Lucy W. The prevalence of major lower limb loss in the United States is projected to increase significantly due to rising rates of diabetes and obesity, highlighting an urgent need for advanced prosthetic solutions [1]. Individuals with lower limb amputations often face increased energy expenditure and secondary musculoskeletal conditions as a result of using conventional prosthetic devices [2]. These challenges underscore the necessity for innovative prosthetic designs that can enhance user mobility and comfort. A promising prosthesis solution are powered ankle-foot prostheses, which have the potential to provide biologically accurate push-off power, thereby offering significant benefits such as improved walking economy, increased mobility, and reduced impact forces on the user’s residual limb. However, existing powered prostheses often lack customization and fail to adequately meet the diverse and specific needs of individual users, which can limit their effectiveness and adoption. This thesis introduces a personalized, optimized, low-profile powered ankle-foot prosthesis, known as the Cycloidal Ankle (CyAn), designed to achieve biological ranges of motion and torque during level-ground walking. The CyAn employs a cycloidal drive transmission and a series carbon fiber spring to mimic tendon-like compliance, which enhances energy storage and return while maintaining a low build height to accommodate a broader range of users. The prosthesis device is capable of 25◦ of dorsiflexion and 41◦ of plantarflexion, and is capable of outputting at least 130 Nm of torque during walking, corresponding to biological ankle torque during level ground walking at 1.5 m/s for a 50th percentile male [3]. The CyAn prosthesis uses of a cycloidal drive transmission coupled with a series carbon fiber spring. This combination replicates tendon-like compliance and allows for a reduced build height without compromising the prosthesis’s range of motion or mechanical performance. The development of the CyAn prosthesis involved a comprehensive mechanical and mechatronic design process, encompassing modeling, optimization of electrical energy consumption, component selection, and benchtop and clinical evaluation. This thesis describes the detailed design and analysis of the CyAn prosthesis, including a parametric model for predicting device performance, fatigue life calculations, and mechanical integrity assessments of device components. Benchtop testing results confirm that the device successfully achieves the targeted performance metrics, demonstrating its capability to replicate natural gait mechanics. The clinical validation study was conducted with 3 participants with unilateral transtibial amputation at 3 different walking conditions: level ground at 1.5 m/s, uphill (+10◦ slope) at 0.8 m/s, and downhill (-10◦ slope) at 1.2 m/s. During the experiment, the subjects walked on an instrumented treadmill to regulate the walking speed while force and motion data were recorded. The results of these tests demonstrate the prosthesis design’s capability to replicate natural gait mechanics and kinetics, as well as insights into further improvements and adaptations. This thesis comprehensively details the mechanical and mechatronic design processes, encompassing modeling, optimization, component selection, and empirical evaluation of the CyAn prosthesis. This thesis presents the first of its kind rotary powered ankle-foot prosthesis, utilizing a cycloidal drive mechanism and a custom series carbon fiber spring. Compared to existing powered devices, the CyAn offers a lower device mass and increased biomimetic functionality, making it a cost-effective solution for improving mobility and quality of life for transtibial amputees. This research establishes a framework for developing customized prosthetic solutions that address the unique needs of individual users, with significant clinical results demonstrating the potential of the CyAn to improve health outcomes by normalizing biomechanics, increasing energy efficiency, and reducing adverse limb loading.

Towards Generalization of Models on Streets Imagery: Methods and Applications

Wed, 01 Sep 2021 00:00:00 GMT

Towards Generalization of Models on Streets Imagery: Methods and Applications Alhasoun, Fahad The domains relevant to urban planning have been disrupted by the proliferation of highly granular city data and the advancements in machine learning. However, machine learning models are susceptible to pitfalls constraining their deployment in many applications including domains related to urban settings. There is much to be addressed between the methods and applications before we can realize all potentials of machine learning to improve urban life. In this thesis, we focus on the use of streets imagery and classification problems. We start motivating the thesis with a case study where deep learning models are trained to predict street contexts (i.e. residential, park, commercial...etc) from streets imagery. We then shift gears and discuss a novel unsupervised domain adaptation method to address the drop in accuracy when models are tested outside the domain of the training data (i.e. a model trained on San Francisco and tested in Boston). We further our discussion with a proof of concept of a framework to develop more generalized models starting with a prototype of a system of streets imagery collection, labeling, and ending with how we approach generalization by breaking the problem into smaller prediction tasks to aid in more understanding of the interworking of the models.

Case studies in the modeling and control of continuous pharmaceutical manufacturing processes

Tue, 01 Jun 2021 00:00:00 GMT

Case studies in the modeling and control of continuous pharmaceutical manufacturing processes Maloney, Andrew John The pharmaceutical industry employs a myriad of modalities; ranging from small molecules to biologics such as peptides, monoclonal antibodies, bi-specific antibodies, and viral vectors. The manufacturing of these products is as varied as the products themselves. Small molecules are synthesized chemically; i.e. by a series of key chemical transformation, work-up, and recovery steps. Larger molecules can be isolated from naturally occurring sources (i.e. humans, plants, or other microorganisms), or produced via recombinant hosts such as Chinese hamster ovary (CHO), Escherichia coli, or Saccharomyces cerevisiae, with some products requiring both a recombinant host and transient transfection or infection with additional genetic material. Across these modalities, industry, regulatory agencies and academia are investigating technologies for improved quality, efficiency, capability, and consistency. Of these technologies, continuous manufacturing (CM) is of particular interest due to its ability to allow for reduced equipment sizing and footprint, improved environmental sustainability, and improved process control. This thesis supports the implementation of continuous pharmaceutical manufacturing through advanced modeling, simulation, and control as described in three independent case studies. The first work considers the development of a virtual plant for manufacturing of a small molecule active pharmaceutical intermediate (API) through four chemical transformation, workup, and recovery steps. The plant is used for uncertainty quantification, improved process design, and novel process control strategy development. The second work considers the production of small, globular proteins by the yeast Pichia pastoris. A model for copy number stability is developed and validated using data in open literature and data generated at MIT. The third work concerns the production of monoclonal antibodies (mAbs) using Chinese hamster ovary cells as a production host. Hardware considerations, lower level regulatory controls, and advanced process modeling and control for a heavily-instrumented mAb manufacturing testbed are discussed. Across this thesis, the benefits of systems-level analysis in the continuous manufacturing of pharmaceuticals is documented and demonstrated.

Inside the App Bureaucracy: The Use of Smartphone Apps in Public Service Delivery Organizations in Pakistan

Fri, 01 May 2020 00:00:00 GMT

Inside the App Bureaucracy: The Use of Smartphone Apps in Public Service Delivery Organizations in Pakistan Masud, Mohammad Omar Smartphone apps are being used in governments in developing countries for monitoring of frontline officials in delivery of public services. Development literature has expressed doubt about transformative impact of digital technologies on entrenched bureaucracies in developing countries. While smartphone monitoring apps have improved speed and reliability of information from the ground, we do not know how availability of such apps among large number of middle to low level officials affect work and practice in large government bureaucracies in a developing country. The dissertation looks at four in depth cases studies involving use of smartphone apps in Pakistan. The cases involve the anti-Dengue program in the city of Lahore, garbage collection agency in Lahore, crime mapping by Lahore police and school monitoring by the provincial school department in the province of Punjab (which includes Lahore). Using a detailed analytical framework, I trace out the evolution of the smartphone monitoring apps in each case starting from design and implementation and continuing to use of their data among multiple levels of the bureaucracy. Using Zuboff’s concept of informating and literature on accountability and performance in government organizations, I look at how design, implementation and use of smartphone monitoring apps and their data bring about changes in workflows and practices, among lower echelons of the bureaucracy without any major restructuring or reform, leading to greater responsiveness and performance orientation. The research reveals that low level officials are responsive to monitoring data because it gives salience to their work and is an objective performance measure in a challenging work environment. The research also shows that such behavior is contingent upon how effectively the organizations manage viewing and sharing of monitoring data with forums to discuss data with frontline officials. It also points out the importance of effectively managing a smart mobile data infrastructure to sustain emerging workflows and practices.

System-level Design, Fabrication, and Optimization of Sorbent-based Atmospheric Water Harvesting Devices

Sun, 01 Sep 2024 00:00:00 GMT

System-level Design, Fabrication, and Optimization of Sorbent-based Atmospheric Water Harvesting Devices Wilson, Chad T. Sorption-based atmospheric water harvesting (SAWH) has been demonstrated as a promising avenue to addressing the increasing problem of water scarcity, especially in arid inland regions where alternative technologies are limited. However, current sorbent materials are often limited in their applicability due to system integration and device design constraints. In this thesis, we present advancement of atmospheric water harvesting technologies in both the passive and active design space by leveraging a system-level approach to modelling and optimization of devices. First, we discuss SAWH device fundamentals in terms of heat, mass, and fluid transport, and identify key components which impact device performance for both passive (solar) and active (electrical/chemical) systems, as quantified by our proposed performance metrics. Next, we develop a coupled heat and mass transport model of a passive, solar-driven atmospheric water harvesting device and quantify the impact of system variables on device operation. We use this model to fabricate an optimal system that efficiently utilizes a hydrogel-salt composite sorbent for record passive water production in the Atacama Desert. Furthermore, we propose an underlying mechanism for observed system-level degradation of our hydrogel-salt composite and demonstrate successful lifetime elongation of the sorbent in SAWH operation. Additionally, we use our fundamental understanding of SAWH to design an active device for portable use. Highly compact, lightweight, and energy dense, this system operates independent of external environment conditions and produces more than 2 L/day of potable water. Finally, a generalized topology optimization approach is proposed for sorbent scaffolding structures to further improve system water output while reducing power consumption and packing of atmospheric water harvesting devices.

Towards Object-based SLAM

Sun, 01 Sep 2024 00:00:00 GMT

Towards Object-based SLAM Zhang, Yihao Simultaneous localization and mapping (SLAM) is a fundamental capability for a robot to perceive its surrounding environment. The research area has developed for more than two decades from the original sparse landmark-based SLAM to dense SLAM, and now there is a demand for semantic understanding of the environment beyond pure geometric understanding. This thesis delves into object-based SLAM where the map consists of a set of objects with their semantic categories recognized and their poses and shapes estimated. Such a map provides vital object-level semantic and geometric perception to applications such as augmented reality (AR), mixed reality (MR), robot manipulation, and self-driving. In order to perform object-based SLAM, the sensor measurements have to undergo a series of processes. First, objects are semantically segmented in the sensor measurements. This step is typically done by a neural network. As robots are often required to bootstrap from some initial labeled datasets and adapt to different environments where labeled data are unavailable, it is important to enable semi-supervised learning to improve the robot’s performance with the unlabeled data collected by the robot itself. Second, after the objects are segmented, measurements for each object across different views have to be associated together for downstream processing. Lastly, the robot must be able to extract the object pose and shape information from the measurements without access to the detailed object CAD models which are commonly unavailable. This thesis studies these three aspects of object-based SLAM, namely semi-supervised learning of semantic segmentation in a robotics context, data association for object-based SLAM, and category-level object pose and shape estimation. The thesis closes with a discussion of how these components can be integrated into a full object-based SLAM system in the future.

Ion Aggregation, Correlated Ion Transport and the Double Layer in Super-Concentrated Electrolytes

Tue, 01 Jun 2021 00:00:00 GMT

Ion Aggregation, Correlated Ion Transport and the Double Layer in Super-Concentrated Electrolytes McEldrew, Michael In the dilute regime, properties of electrolytes are well known and their mathematical descriptions are well established. The physical picture of dilute electrolytes, in which ions are pristinely solvated, fully dissociated, and immersed in an excess of structureless solvent medium, lends itself naturally to elegant and tidy mathematical descriptions. Owing in large part to their simplicity and physical transparency, these descriptions have guided our intuition of electrolytes for the better part of the last century. However, with the explosion of interest in super-concentrated electrolytes, particularly for electrochemical energy storage applications, theoretical descriptions of electrolytes within this regime are greatly needed. The physical description of superconcentrated electrolytes gets completely flipped from that of their dilute counterparts: ions have complex solvation structures, they are only partially dissociated, and they outweigh or even outnumber the solvent. This complex environment imparts unexpected properties to super-concentrated electrolytes. Understanding the origin of these unexpected properties could unlock the key design principles for the next generation of super-concentrated electrolytes. In this thesis, we develop simple, chemical-specific, theoretical models of superconcentrated electrolytes. First, we develop a continuum model of the electrical double layer in water-in-salt electrolytes that unravels the physics behind a potential mechanism for oxidative stability in WiSEs. We find that asymmetric ion solvation leads to very asymmetric water distributions within the double-layer. Next, we develop a thermodynamic model of ion aggregation and solvation in super-concentrated electrolytes. The model is deeply rooted in polymer-physics and treats the electrolyte as a poly-disperse mixture of branched ion clusters. In addition to cluster distributions and thermodynamics, our model predicts the onset of a percolating ion network, termed an ionic gel, at a critical salt concentration. We apply our model to two important classes of super-concentrated electrolytes: room temperature ionic liquids (RTILs) and water-in-salt electrolytes (WiSEs). For these classes, our model was able to be greatly simplified, as well as parameterized and validated by extensive molecular dynamics simulations. Furthermore, we consider the effects of extensive ion clustering and gelation on ion transport, electrochemical stability window and the emergence of nano-heterogeneity observed in super-concentrated electrolytes.

Radiatively Cooled Magnetic Reconnection Experiments Driven by Pulsed Power

Sun, 01 Sep 2024 00:00:00 GMT

Radiatively Cooled Magnetic Reconnection Experiments Driven by Pulsed Power Datta, Rishabh Magnetic reconnection is a ubiquitous process in astrophysical plasmas, responsible for the explosive conversion of magnetic energy into thermal and kinetic energy. In extreme astrophysical systems, such as black hole coronae and neutron star magnetospheres, radiative cooling modifies the energy partition by rapidly removing internal energy. In this thesis, we perform experimental and computational studies of magnetic reconnection in a radiatively cooled regime, previously unexplored in reconnection studies. The Magnetic Reconnection on Z (MARZ) experiments consist of a dual exploding wire array, driven by a 20 MA peak, 300ns rise time current generated by the Z pulsed-power machine (Sandia National Labs). The load generates oppositely-directed supersonic, super-Alfvénic, collisional plasma flows with anti-parallel magnetic fields, that generate a reconnection layer (Lundquist number SL ∼ 100), in which the total cooling rate far exceeds the Alfvénic transit rate [mathematical notation]. Two- and three-dimensional simulations of the MARZ experiments are performed in GORGON, an Eulerian resistive magnetohydrodynamic code. The simulations demonstrate the generation of a reconnection layer, which radiatively collapses, exhibiting a rapid fall in temperature, strong compression, and an increased reconnection rate consistent with theoretical predictions. The reconnection layer is unstable to the plasmoid instability, generating secondary current sheets separated by magnetic islands. High energy X-ray emission is generated predominantly by the plasmoids. The plasmoids also collapse radiatively, and the reconnection layer recovers a laminar large aspect ratio structure, which does not exhibit further plasmoid generation, indicating stabilization of the original plasmoid instability of the current sheet. The experiments confirm numerical predictions by providing evidence of plasmoid formation and strong radiative cooling. Experimental diagnostics directly measure the spatial, temporal, and spectral properties of radiative emission from the reconnecting system. The reconnection layer generates a transient burst of >1 keV X-ray emission, consistent with the formation and subsequent rapid cooling of the layer. Time-gated X-ray images show fast-moving (up to 50 km s−1) hotspots in the layer, consistent with the presence of plasmoids in 3-D resistive magnetohydrodynamic simulations. X-ray spectroscopy shows that these hotspots generate the majority of Al K-shell emission (around 1.6 keV), and exhibit temperatures (170 eV) much greater than that of the plasma inflows and the rest of the reconnection layer. The findings in this thesis are of particular relevance to the generation of radiative emission from reconnection-driven astrophysical events, and to the global dynamics of reconnection in strongly cooled systems. The MARZ experiments also provide a novel platform for investigating radiative effects in high-energy-density and laboratory astrophysics experiments, and for validation of radiation magnetohydrodynamic and atomic spectroscopy codes.

Light-induced States and Phase Transitions in Quantum Materials investigated by Photoemission Spectroscopy and Epitaxial Synthesis

Sun, 01 Sep 2024 00:00:00 GMT

Light-induced States and Phase Transitions in Quantum Materials investigated by Photoemission Spectroscopy and Epitaxial Synthesis Choi, Dongsung In condensed matter physics, a field of the study on phases of matter and their transitions, light-induced states and phase transitions have attracted significant attention due to their importance in both fundamental research and applications. This thesis will specifically delve into three compelling studies: (1) Floquet-Bloch states, photon-dressed Bloch states, were investigated in graphene. These states are generated by a time-periodic potential of light, closely related to the topic of Floquet engineering. (2) A light-induced insulator-to-metal transition was observed in Sr₂IrO₄, providing valuable insights into the fundamental characteristics of its ground states. (3) A light-induced topological phase transition (from a Z₂ topological insulator to a trivial insulator) was investigated in Bi-doped (Pb,Sn)Se thin-films. For these studies, we employed time- and angle-resolved photoemission spectroscopy (trARPES) and molecular beam epitaxy (MBE). Through in-depth investigation into these phenomena, this thesis seeks to contribute to the broader understanding of light-matter interactions in quantum materials.

Learning to Improve Clinical Decisions and AI Safety by Leveraging Structure

Sun, 01 Sep 2024 00:00:00 GMT

Learning to Improve Clinical Decisions and AI Safety by Leveraging Structure Chauhan, Geeticka The availability of large collections of digitized healthcare data along with the increasing power of computation has allowed machine learning (ML) for healthcare to become one of the key applied research domains in ML. ML for health has great potential in providing clinical decision-making support that can improve quality of care and reduce healthcare spending by easing clinical operations. However, the successful development of ML models in healthcare is contingent on data that is complex, noisy, heterogeneous, limited in labels and highly sensitive. In this thesis, we leverage the unique structure present in medical data along with the availability of external knowledge to guide model predictions. Additionally, we develop differentially private (DP) training techniques using gradient structure to mitigate privacy leakage. In this thesis, we develop methods on different medical modalities such as multivariate physiological signals of ICU patients, patient discharge summaries, biomedical scientific articles, radiology reports, chest radiography imaging and spoken utterances. We tackle tasks such as forecasting patient states, relationship extraction, disease prediction, medical report generation and differentially private model training. We begin the thesis by offering open source data processing and modeling frameworks, move towards improved interpretability of model predictions to develop clinician trust and finally investigate differentially private ML techniques to protect user data. First, we show that the use of aggregated feature representations based on clinical knowledge offers model robustness against evolving hospital systems. Second, we leverage external knowledge in the form of clinical concept extraction to significantly improve relationship extraction. Third, we leverage the rich information from reports associated with chest radiographs to develop highly accurate disease severity prediction models using contrastive learning. Fourth, we showcase that the report generation task offers competitive disease prediction capabilities, label efficiency and improved interpretability. Finally, we introduce novel methods for improved privacy-utility-compute tradeoffs for DP pre-training of large speech models. We highlight DP as an important component of model safety, necessitating its development in conjunction with AI safety approaches that will be pertinent in healthcare and beyond.

Combining Experiments and First-Principles Calculations to Understand and Engineer Metal Exsolution in Perovskites

Wed, 01 May 2024 00:00:00 GMT

Combining Experiments and First-Principles Calculations to Understand and Engineer Metal Exsolution in Perovskites O'Leary, Willis Exsolution processing has emerged as a leading new route to fabricate highly active and stable ceramic-supported metal catalysts for a wide variety of applications, including solid oxide fuel cells, solid oxide electrolyzers, catalytic converters, and chemical/fuel production. In exsolution, metal cations are exsolved to the surface of a perovskite oxide solid solution under reducing conditions. The result is a perovskite backbone decorated with partially embedded metallic nanoparticles. The stability and anti-coking properties of exsolved nanoparticles have driven growing interest in exsolution materials. However, even after two decades of intense research, key open questions remain regarding exsolution's precise mechanism and, consequently, how to rationally engineer the properties of exsolution nanoparticles. This thesis aims to address these questions through a combination of experimental work and first-principles atomistic modelling with the long-term goal of accelerating the commercialization of exsolution materials. We first investigate the impact of perovskite composition on the properties of Ni nanoparticles exsolved from bulk SrTi₀.₉₄Ni₀.₀₆O₃₋ subscript δ. We adjust the makeup of the Sr site, adding dopants of varying valence and ionic radii as well as vacancies, and measure how these changes modulate the surface density of the exsolved nanoparticles. We then use density functional theory (DFT) calculations to explain the observed trends, finding that the energetics of cation surface segregation and surface reduction control nanoparticle nucleation kinetics. This work provides valuable new insights into the exsolution mechanism, and, for the first time, introduces a quantitative model capable of accurately predicting the experimental exsolution properties of given perovskite composition from first principles. Next, we extend this quantitative model to capture the influence of the exsolution conditions on the properties of Ni nanoparticles, this time focusing solely on Ni exsolution from bulk Sr₀.₈La₀.₁Ca₀.₁Ti₀.₉₄Ni₀.₀₆O₃₋ subscript δ. We first measure the dependence of nanoparticle density on exsolution temperature and oxygen partial pressure. We rationalize the empirical trends using the LaMer theory for nucleation and our model previously developed to predict composition effects. This achievement points towards the first-ever method for first-principles prediction of a generic perovskite composition’s exsolution properties under varying reducing conditions. Thus, we make a major step towards fully in silico design of exsolution materials, greatly increasing their commercial attractiveness. Finally, we develop a novel, highly efficient DFT methodology to predict Raman signatures of point defects and apply this methodology to interpret SrTi₀.₉₄Ni₀.₀₆O₃₋ subscript δ’s complex Raman spectrum. Based on empirical and DFT-derived Raman spectra, we characterize SrTi₀.₉₄Ni₀.₀₆O₃₋ subscript δ’s defect chemistry and local structure. Our findings are a vital first step towards using Raman spectroscopy to study and screen exsolution materials. More broadly, our computational methodology supercharges Raman spectroscopy as a tool to characterize local structure in a wide range of technologically relevant material systems.

Understanding community changes in ecological systems: a probabilistic and geometric perspective

Wed, 01 May 2024 00:00:00 GMT

Understanding community changes in ecological systems: a probabilistic and geometric perspective Deng, Jie Regulating and predicting community changes in ecological systems represent fundamental challenges in science and engineering, particularly in systems subject to constant environmental perturbations (e.g., natural, in vivo, and in situ environments). Consequently, a central goal of ecological research has been to understand the processes of coexistence, invasion, and assembly in open systems that underlie changes in the composition of ecological communities. These changes can be induced by either natural events (e.g., viruses infecting humans) or human actions (e.g., fecal microbiota transplantation). Although previous studies have theoretically explored criteria for successful coexistence, invasion, and assembly under specific or fixed environmental conditions, the variable and often unknown environmental conditions in nature have left these criteria largely untested. The overarching goal of my PhD thesis is to provide a testable theoretical framework for the dynamics of coexistence, invasion, and assembly under environmental uncertainty (i.e., in nature or open systems). This framework, rooted in the generalized Lotka-Volterra model, adopts a probabilistic and geometric perspective to understand these dynamics. In particular, my thesis comprises three core projects. The first project develops probabilistic system-level measures to quantify the effects of third-party species on the coexistence of a pair (or subset) of species by integrating population dynamics models (i.e., the Lotka-Volterra model) with in vivo experimental data from fruit fly gut microbiota. Additionally, I test general heuristic rules based on the proposed probabilistic measures using in vitro soil and in vivo gut microbial communities. The aim is to predict how non-resident species (invaders) can alter resident communities and to assess the applicability of our probabilistic measures. The second project seeks to unify coexistence and invasion theories within a geometric and probabilistic framework that is testable. This unification enables us to predict and test the impact of interspecific interactions on invasion and exclusion probabilities without requiring detailed model parameterization or extensive datasets. The third project identifies the general principle governing the development of ecological systems under environmental uncertainty, which could assist in regulating or even predicting changes in ecological community compositions. This principle is validated across a broad spectrum of ecological scales, from large mammals to gut microbes, through publicly available data. I believe this thesis will bring us closer to understanding the processes that influence community compositions and their changes, knowledge that holds great potential for advancing bio-conservation, bio-technologies, and bio-medicine.

Cobordism of manifolds with w1, w2 and w4 vanishing.

Sat, 01 Jan 1966 00:00:00 GMT

Cobordism of manifolds with w1, w2 and w4 vanishing. Giambalvo, Vincent William. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1966; Bibliography: leaves 75-77.

Contribution to the methods of measuring stresses below the surface

Wed, 01 Jan 1947 00:00:00 GMT

Contribution to the methods of measuring stresses below the surface Safoglu, Recep Ali, 1920- Thesis: Sc. D., Massachusetts Institute of Technology, Department of Metallurgy, 1947; Vita.; Includes bibliographical references (leaves 183-184).

Local politics and industrial adjustment : the political economy of Italy in the 1980's

Sun, 01 Jan 1989 00:00:00 GMT

Local politics and industrial adjustment : the political economy of Italy in the 1980's Locke, Richard M., 1959- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Political Science, 1989; Title as it appears in the M.I.T. Graduate List, Feb. 1989: "Eppure Si Muove"--the political economy of industrial change in Italy.; Includes bibliographical references (leaves 285-310).

Solvent extraction of cobalt from thiocyanate solutions

Tue, 01 Jan 1957 00:00:00 GMT

Solvent extraction of cobalt from thiocyanate solutions Hard, Robert A. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Metallurgy, 1957; Vita.; Bibliography: leaf 84.

A neutron diffraction study of the field-induced diamagnetism in the semimetal bismuth.

Mon, 01 Jan 1979 00:00:00 GMT

A neutron diffraction study of the field-induced diamagnetism in the semimetal bismuth. Collins, Steven Robert. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1979; Vita.; Includes bibliographical references.

A neutron diffraction study of the magnetization in dilute Cu (Fe) alloys.

Tue, 01 Jan 1974 00:00:00 GMT

A neutron diffraction study of the magnetization in dilute Cu (Fe) alloys. Dickens, Michael Hugh. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1974; Vita.; Includes bibliographical references.

Xenobiotic metabolism and mutation in diploid human lymphoblasts

Fri, 01 Jan 1982 00:00:00 GMT

Xenobiotic metabolism and mutation in diploid human lymphoblasts Crespi, Charles L. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nutrition and Food Science, 1982; Bibliography: leaves 148-155.

Constrained stochastic climate simulation

Fri, 01 Jan 1982 00:00:00 GMT

Constrained stochastic climate simulation Curtis, David Carleton. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Civil Engineering, 1982; Bibliography: leaves 215-226.

Demand-responsive transit : problems and possibilities.

Sun, 01 Jan 1978 00:00:00 GMT

Demand-responsive transit : problems and possibilities. Ewing, Reid Harris. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Civil Engineering, 1978; Bibliography: leaves 255-264.

Kinetics of nitrogen solution from arc plasmas into liquid iron.

Sun, 01 Jan 1978 00:00:00 GMT

Kinetics of nitrogen solution from arc plasmas into liquid iron. Esimai, Charles Nduka. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 1978; Vita.; Includes bibliographical references.

Nanoscale Origins of Thermal Transport Phenomena for Hybrid Layered Perovskites

Sat, 01 Jun 2019 00:00:00 GMT

Nanoscale Origins of Thermal Transport Phenomena for Hybrid Layered Perovskites Dahod, Nabeel S. An exciting and fundamentally powerful modern methodology for materials development is the process by which artificial solids are rationally built piece-by-piece from nanoscale “building blocks”. Among the library of nanomaterials currently at the forefront of this pursuit, two-dimensional layered lead halide perovskites (2D LHPs), are of particular interest. These materials, solid crystals composed of alternating layers of atomically thin organic and inorganic subphases, possess novel optical and electronic properties that make them particularly suited for use in devices including solar cells, LEDs, flexible electronics, and even lasers. While significant early strides have been made in investigating charge carrier transport through dynamic models and sophisticated experiments, comparatively little attention has been given to understanding the manner in which the design of these nanostructured solids impacts their macroscopic thermal properties via thermal carrier (phonon) transport. This knowledge, however, is critical to addressing the thermal management constraints necessary to the design of reliable and stable devices. To this end, this dissertation seeks to elucidate the thermal stability and fundamental pathways for heat transport within 2D LHP artificial solids. I first present an experimental investigation into the thermal and structural stability of these 2D LHPs near room temperature using differential scanning calorimetry and x-ray diffraction. This analysis reveals near-room temperature melting transitions isolated to the organic component of the nanomaterials. The existence of such an isolated phase transition indicates thematerials behave thermophysically as composites, a hypothesis that is supported by the effective use of a lever rule in estimation of the heat capacity of the materials. I discuss the theoretical foundation and experimental construction of a frequency domain thermoreflectance technique to effectively measure the cross-plane thermal conductivity of 2D LHPs. This technique is then utilized to perform the first measurement of the thermal conductivity of 2D LHPs. This experimental study reveals that even in terms of their thermal transport pathways, 2D LHPs can be treated as composite materials. Specifically, lead bromide 2D LHPs exhibit structure-property relationships characteristic of ballistic phonon transport within isolated subphases and diffuse scattering at the organic-inorganic interfaces between layers. Finally, I report the first measurements of the vibrational spectrum for 2D LHPs via low frequency Raman spectroscopy. This probe identifies the persistence of bulk-like phonons even in the atomically thin 2D LHPs, in addition to identifying coherent acoustic phonons in lead iodide 2D LHPs potentially capable of carrying thermal energy across the organic-inorganic interfaces without scattering. Each of the observations made throughout this dissertation suggest the thermophysical representation of 2D LHPs as composite materials is a useful framework for understanding their thermal transport properties. That so many material properties can be effectively predicted simply from the bulk properties of the component phases is surprising given both the long-range order of the artificial solids and the sub-nanometer length scale of the individual component layers, and underlines the potential for intelligent engineering of the thermal properties of 2D LHPs without deleterious influences on the sterling optoelectronic properties.

Deep learning methods to study structurally heterogeneous macromolecules in vitro and in situ

Wed, 01 May 2024 00:00:00 GMT

Deep learning methods to study structurally heterogeneous macromolecules in vitro and in situ Powell, Barrett M. Proteins, RNA, and other biomolecules form complex 3-D structures that dynamically interact to carry out essential biological processes. These macromolecular complexes are often structurally heterogeneous, which is key to executing or regulating their specific biological functions. To understand the molecular mechanisms underpinning these biological functions, structural biologists aim to determine the 3-D structure of the relevant macromolecule or macromolecular complex. Most such structural insights use techniques that strip the macromolecule of its cellular context (i.e., in vitro) and, subsequently, report a single average structure. However, recent advances in cryogenic electron microscopy (cryo-EM) provide avenues to determine sets of heterogeneous structures from a single dataset, and simultaneous advances in cryogenic electron tomography (cryo-ET) enable the resolution of macromolecules in their native cellular environment (i.e., in situ). This thesis describes the conceptualization, implementation, and application of tomoDRGN, a deep learning method developed to resolve structurally heterogeneous macromolecules in situ. TomoDRGN builds on the well characterized cryoDRGN method, which facilitates analysis of heterogeneous structures by cryo-EM, to cryo-ET, where I show it efficiently learns an ensemble of unique 3-D volumes from the structurally heterogeneous dataset provided. I additionally describe the application of TomoDRGN to datasets of diverse macromolecules, highlighting its ability to resolve conformational and compositional heterogeneity and to identify rare yet biologically informative structural states. This thesis also details an approach and protocol for rapid structural characterization of bacterial ribosomes in situ, wherein tomoDRGN facilitates powerful upstream dataset filtration. Finally, this thesis provides a detailed protocol for the characterization of heterogeneous cryo-EM datasets with cryoDRGN and, in doing so, illustrates the types of new insights enabled by the cryoDRGN and tomoDRGN Deep Reconstructing Generative Networks.

The dynamic behavior of an ammonia synthesis reactor

Wed, 01 Jan 1964 00:00:00 GMT

The dynamic behavior of an ammonia synthesis reactor Eymery, Jean-Pierre. Thesis: Sc. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 1964; Vita.; Includes bibliographical references (leaves 215-217).

Many meson production in meson-nucleon collision in the chew-low formalism

Tue, 01 Jan 1957 00:00:00 GMT

Many meson production in meson-nucleon collision in the chew-low formalism Tarimer, Niyazi. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1957; Vita.; Includes bibliographical references (leaf 60).

Deformation processes of zirconium

Sat, 01 Jan 1955 00:00:00 GMT

Deformation processes of zirconium Rapperport, Eugene J. (Eugene John) Thesis: Sc. D., Massachusetts Institute of Technology, Department of Metallurgy, 1955; Vita.; Bibliography: leaves 99-101.

Pulsed laser ablation of calcified biological tissue : physical mechanisms and clinical applications

Tue, 01 Jan 1991 00:00:00 GMT

Pulsed laser ablation of calcified biological tissue : physical mechanisms and clinical applications Izatt, Joseph A. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nuclear Engineering, 1991; Includes bibliographical references (leaves 192-205).

German nuclear dilemmas, 1955-1965

Sun, 01 Jan 1967 00:00:00 GMT

German nuclear dilemmas, 1955-1965 Kelleher, Catherine McArdle. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Political Science, 1967; Vita.; Bibliography: leaves 665-686.

Predicting torsional fatigue crack growth (Mode III) in turbo-generator shafts

Fri, 01 Jan 1982 00:00:00 GMT

Predicting torsional fatigue crack growth (Mode III) in turbo-generator shafts Nayeb-Hashemi, Hamid. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1982; Includes bibliographical references.

Computational Approaches for Understanding and Redesigning Enzyme Catalysis

Wed, 01 May 2024 00:00:00 GMT

Computational Approaches for Understanding and Redesigning Enzyme Catalysis Karvelis, Elijah The remarkable specificity and catalytic efficiency of many enzymes make them attractive for applications ranging from therapeutics to chemical manufacturing. However, it remains challenging to identify the specific structural and dynamic mechanisms underlying the catalytic power of enzymes, which has limited our ability to re-engineer catalytic properties. In this thesis, I address these shortcomings by developing and demonstrating computational strategies comprised of techniques spanning statistical mechanics, machine learning, and protein design, and I apply them to the enzyme ketol-acid reductoisomerase (KARI), whose economic viability for the production of isobutanol would be strengthened by enhancing its activity on one of its two native substrates: 2-acetolactate (ACL). While computational enzyme redesign strategies for increased activity have traditionally focused on decreasing the energetic gap between the enzyme-substrate ground state and transition state, this thesis postulates and evaluates whether a more holistic treatment including the dynamics of complete turnover events could further elucidate properties affecting turnover efficiency and guide the identification of mutants with enhanced catalytic function. In the first study, we describe a novel redesign strategy for enhanced specific activity (turnover number) based on analysis of enzyme-substrate turnover dynamics. The approach combined statistical mechanical path sampling algorithms and machine learning methods to identify the structural characteristics of enzyme-substrate complexes primed for successful conversion of substrate to product, which were then energetically stabilized by mutating KARI. A subset of candidate mutants were tested using path sampling-based reaction rate constant calculations, and eight mutants were identified with computed improvements in turnover number of up to four orders of magnitude for the isomerization of ACL. Further analysis revealed structural mechanisms by which enhanced activity was attained. In the second study, we examine the effects of these same mutations on the isomerization of KARI's other native substrate: 2-aceto-2-hydroxybutyrate (AHB), and we find that the mutants selected for increased activity on ACL had varied levels of activity on AHB. These variations in mutant activity on AHB were explained by analysis of WT-AHB simulations, which showed that only some of the structural mechanisms related to enhanced ACL catalysis transferred to, and thereby facilitated, AHB catalysis. This thesis highlights the influence of conformational states that are visited during the dynamics of substrate turnover and their role on enzyme catalysis, and it furthermore suggests a framework with which researchers may consider and apply these effects when engineering catalytic function.

Designing Microporous Polymers for Separations

Thu, 01 Feb 2024 00:00:00 GMT

Designing Microporous Polymers for Separations Storme, Kayla R. In Chapter 1, we investigate the influence of side-chain length and dispersity in ring-opening metathesis polymerization (ROMP) polymers with pore-generating side chains. Macromonomers with four discrete monodispersities are separated and polymerized to produce bottlebrush polymers with monodisperse side chains. Each bottlebrush polymer is fabricated into a free-standing film. Pure-gas experiments are performed to explore the impact of dispersity and side chain length on gas separation performance. In Chapter 2, we evaluate the mixed-gas performance of a class of bottlebrush polymers described in Chapter 1. Gas sorption, diffusion, and CO₂-induced plasticization are reported. Competitive sorption effects are studied using 50:50 mixture of CO₂/CH₄. Separation performance at different compositions of CO₂/CH₄ is also explored. In Chapter 3, we incorporate nitrile functionality into the structure of a family of polymers with rigid, porogenic side chains described in Chapters 1 and 2. Statistical and block copolymers are synthesized to demonstrate the role of grafting density on separation performance and CO₂ plasticization resistance. Sorption experiments are performed to determine improvements to selectivity. In Chapter 4, we describe the optimized SN Ar synthesis of a poly(arylene ether) (PAE) that produces high molecular weight polymers. The synthesis of an analogous PAE with C-H functionality instead of C-F is also reported. Porosity and free volume are investigated in both PAEs. Separation performance is characterized and compared to other polymers with similar structural motifs.

A Game Theoretic Approach to Resilient Space System Design

Sun, 01 Sep 2024 00:00:00 GMT

A Game Theoretic Approach to Resilient Space System Design Jones, Michael P. There is a growing need for space missions that maintain performance in uncooperative or even adversarial environments. Space system designers must account for resilience to non-cooperative interactions in the design process while trading resilience and performance against cost. Prior academic work has considered resilience for system design, especially in the context of environmental factors; however, current literature does not include a space system design methodology that explicitly models non-cooperative, interactive threats to produce a more resilient design. To address this gap, a novel game-theoretic methodology is proposed to capture the interactive nature of non-cooperative systems at the strategic design level. The result is a two-player strategic design game in which the system under design and the threat system are both modeled as rational actors, and design options for the system architecture and threat system architecture are strategic choices for each actor. Performance, resilience, and cost metrics are calculated by an operational-level simulation of system-threat interactions. Tradespace and sensitivity analyses based on the results are used to evaluate the cost premium of adding resilience to the system and to demonstrate the strategic design choices that provide the most cost-effective means of increasing resilience to the modeled threats. The resulting methodology is presented through three case studies demonstrating the applicability of the methodology across multiple space mission applications. The first case study evaluates a low earth orbit (LEO) Satellite Communications (SATCOM) system design. The results show that perfect resilience (no drop in performance) to the modeled ground based jamming threat requires a 224\% cost increase and that additional satellites are a more cost effective means of increasing resilience than fewer, more capable satellites. The second case study focuses on a Global Navigation Satellite System (GNSS) and adds more fidelity to the physical model and the design choices available to both the threat and the system. A medium earth orbit (MEO) constellation that maximizes resilience to the modeled jamming and kinetic threats consists of 56 satellites in 7 planes, while in LEO this requires 819 satellites in 21 planes. For a LEO GNSS constellation to be more cost effective than a MEO GNSS constellation with the same level of resilience, the LEO system's first unit cost must be $\leq 1/10$ the MEO system's first unit cost. Cost based sensitivity analyses demonstrate how results are influenced by cost model estimates and show how program managers can use this methodology to guide program decisions as cost estimates improve over time. The third case study looks at a non-cooperative GEO mission through an abstracted two player game environment called GEO Patrol. This case study adds fidelity to the operational interactions by requiring complex decision making. Reinforcement learning is used with over 7 million games of self play to train a 2 hidden layer neural network to generate actions. Human-in-the-loop experiments verify the simulation results and improve understanding of the underlying system dynamics. Over 20 volunteers played 53 games representing 3 distinct scenarios. The average difference between the volunteer and simulation results is 5.1\%, verifying the simulation. The three case studies demonstrate how the methodology can be applied across disparate space missions with varying levels of model fidelity. Systems designers can apply the methodology to produce both quantitative and qualitative recommendations to ensure the final system is resilient by design.

Benefits of angularly-controlled field switching on the pulling-into-step ability of salient-pole synchronous motors

Thu, 01 Jan 1931 00:00:00 GMT

Benefits of angularly-controlled field switching on the pulling-into-step ability of salient-pole synchronous motors Edgerton, Harold E. (Harold Eugene), 1903-1990. Thesis: Sc. D., Massachusetts Institute of Technology. Department of Electrical Engineering, 1931; Includes bibliographical references (leaves [73]-[75]).

Leptonic and hadronic polarization in semi-leptonic inclusive weak and exclusive electromagnetic interactions

Thu, 01 Jan 1987 00:00:00 GMT

Leptonic and hadronic polarization in semi-leptonic inclusive weak and exclusive electromagnetic interactions Raskin, Alan Steven. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1987; Bibliography: leaves 220-223.

Organization of the marginal band of avian erythrocytes

Tue, 01 Jan 1985 00:00:00 GMT

Organization of the marginal band of avian erythrocytes Swan, Judith Ann. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biology, 1985; Bibliography: leaves 170-182.

A statistical approach to heavy-ion transfer reactions to the continuum

Tue, 01 Jan 1980 00:00:00 GMT

A statistical approach to heavy-ion transfer reactions to the continuum Karp, Joel Steven. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1980; Includes bibliographical references.

Multi-Proxy Records of Climate and Carbon Cycle Perturbations in the Paleozoic: Integrating Isotope Geochemistry and Sedimentology

Sun, 01 Sep 2024 00:00:00 GMT

Multi-Proxy Records of Climate and Carbon Cycle Perturbations in the Paleozoic: Integrating Isotope Geochemistry and Sedimentology Anderson, Noah Carbonate rocks are a valuable archive of past environmental conditions. To glean robust information from this archive, we must understand how carbonate sediments form, ensure our analytical techniques are optimized, and consider how inherently local deposition of sediments can communicate information about global changes in climate. Chapter 1 proposes a new conceptual model for the formation of ooids that suggests that these small carbonate grains could form while buried in the shallow sediment pile during certain intervals of Earth history. Chapters 2 and 3 calibrate the clumped isotope paleothermometer for calcite, dolomite, and apatite, resolving significant discrepancies in calculated paleotemperatures. Chapter 4 applies clumped isotope thermometry to Early Mississippian strata and demonstrates a ~5ºC global cooling and substantial ice volume expansion coincident with a major perturbation to the global carbon cycle. Chapter 5 examines the extent to which diagenesis and facies- and phase-specific effects drive a major Early Mississippian carbon isotope excursion. In aggregate, this thesis outlines a roadmap for assessing changes to climate and the carbon cycle for carbonate rocks in the Paleozoic.

Artificial Intelligence and the US-China Balance of Power

Tue, 01 Jun 2021 00:00:00 GMT

Artificial Intelligence and the US-China Balance of Power Chang, Benjamin Angel How will artificial intelligence affect the US-China balance of power? While a nascent literature debates whether AI may upend strategic stability or revolutionize the nature of warfare, existing discussions suffer from both imprecise conceptualization and scarce data. In three essays, this dissertation evaluates the impact of AI on the nuclear balance, the conventional balance, and long-term US-China competition more generally by focusing on deep learning, generating data through simulation and supply chain analysis. The first essay defends the focus on deep learning, then presents an end-to-end conceptualization of how its technical qualities translate into usefulness across different categories of modern military tasks, which in turn affect, when contextualized to the particular dyad under study, the strategic balances across different domains of US-China competition. At each analytic layer, the paper condenses deep learning’s effects into several generalizations, tying AI to existing debates in security studies and setting an agenda for future research. The second essay simulates US-China nuclear war in Python to assess AI’s impact on the strategic balance, focusing on the tracking of mobile platforms on land. It finds that AI reduces the total “effective counterforce area” – the area the United States would have to destroy with nuclear weapons, to carry out a splendid first-strike – by one to two orders of magnitude. Under low to medium alert, the simulation finds this would enable successful US nuclear counterforce. While countermeasures are available to China, the essay predicts heightened nuclear tensions as a result. Finally, the third essay exploits supply chain datasets to assess each side’s ability to bring AI-enabled autonomous weapons to bear in future conventional conflicts. I find that control over the production of advanced AI chips by the United States and allies almost certainly means the United States would better exploit such weapons, if they emerged as decisive in modern warfare, within at least the next ten years. Potential Chinese policy responses, such as cannibalizing its civilian sector or substituting with older chips, would likely fail for technical reasons.

Picophytoplankton of the Northeast U.S. Shelf: Community Composition and Dynamics

Fri, 01 Sep 2023 00:00:00 GMT

Picophytoplankton of the Northeast U.S. Shelf: Community Composition and Dynamics Stevens, Bethany Lynn Fowler Marine picophytoplankton are the most abundant primary producers in the ocean and are expected to be favored by the ongoing effects of climate change. Predicting the response of marine ecosystems to these changes requires mechanistic knowledge of picophytoplankton ecology. This thesis uses a combination of long-term monitoring, cruise data, population models, and high-throughput sequencing to investigate the dynamics of picophytoplankton across scales of space and time that are relevant both to the physiology of the individual cells and to the structure of the Northeast U.S. Shelf (NES), a productive and economically important coastal ecosystem. To identify the drivers of seasonal changes in picophytoplankton abundance, I first estimate daily division and loss rates for a nearshore community of picoeukaryotes over a 16-year period. I compare their cell concentrations, vital rates, and responses to environmental variables to those of the cyanobacteria, Synechococcus. Next, to reveal how these dynamics relate to changes in community composition, I analyze 9-years of monthly metabarcoding data and characterize taxonomic variability within the picoeukaryote assemblage. In the second half of this thesis, I explore spatial environmental variability and test the extent to which data from the nearshore observatory are representative of the picophytoplankton communities across the NES. I analyze flow-cytometry data collected from 22 regional research cruises, estimate daily Synechococcus and picoeukaryote division rates from underway data, and describe the distinct depth distributions of the two groups from subsurface samples. The major findings of this thesis are that, across the NES, the picoeukaryotes divide at much higher rates than the more abundant Synechococcus and are subject to greater top-down control from grazing or viral lysis. Both groups are light limited in the fall, temperature limited in the spring, and undergo earlier spring blooms in warmer offshore waters. For Synechococcus, the relationships between cell concentration, division rate and environmental parameters are consistent across the continental shelf, while the picoeukaryote community appears to be nutrient-limited farther from shore. Together, this work creates a detailed picture of the various controls on picophytoplankton abundance within a dynamic coastal ecosystem and advances our understanding of how picophytoplankton communities respond to environmental change.

Protein Folding, Host Cell Proteostasis, and Viral Evolution

Sun, 01 Sep 2024 00:00:00 GMT

Protein Folding, Host Cell Proteostasis, and Viral Evolution Yoon, Jimin Pandemics and epidemics caused by pathological RNA viruses, such as the 1918 influenza pandemic, the global AIDS epidemic, and the recent coronavirus pandemic, impose a severe burden on global health and the economy. A major challenge associated with developing effective antiviral strategies is the exceptionally high mutation rate of RNA viruses, which endows them with a remarkable capacity to adapt to selection pressures such as antibodies or antiviral drugs. Hence, it is critical to understand the molecular-level factors that can constrain and potentiate viral evolution. While mutations benefit viruses by generating the diversity required for evolution, they also threaten viral viability because the majority of non-conservative amino acid substitutions cause protein folding defect. Mutations that result in substantial protein folding defects cannot be tolerated, regardless of how adaptively beneficial the resultant protein variant otherwise would be. Importantly, in cells, protein folding is assisted by intricate networks of chaperones and quality control factors, termed proteostasis networks. When a substitution on a protein impedes its proper folding, proteostasis network components can triage the defective protein variant to chaperones for folding assistance, or to quality control factors for timely degradation. Interestingly, virtually all RNA viruses rely on their host’s proteostasis network components for viral protein folding. It follows that the host’s proteostasis network could play a prominent role in defining the sequence space accessible to an evolving viral protein. In this thesis, I address how host proteostasis networks shape viral protein evolution. First, I describe how the composition of the host cell’s proteostasis machineries chapes the accessible sequence space of human immunodeficiency virus envelope protein. Second, I focus on an important immune-escape variant of influenza nucleoprotein whose fitness depends on host chaperones, and reveal the underlying molecular mechanism of the chaperon dependence. Finally, I demonstrate how chaperone machineries can determine the host range of viruses, and provide potential pathways viruses can evolve to overcome this selection pressure. Overall, elucidating how protein folding and host cell proteostasis affect viral protein evolution would substantially improve our ability to accurately predict RNA virus evolution and host-switching, and may enable design of host-targeted therapeutics that reduce the adaptability of RNA viruses.

Experimental Constraints on Melting Processes in the Earth and Small Rocky Bodies

Wed, 01 May 2024 00:00:00 GMT

Experimental Constraints on Melting Processes in the Earth and Small Rocky Bodies Hoyos Muñoz, Susana The compositional and thermal evolution of rocky bodies in the Solar System is determined by the melt generation and crystallization processes in their interiors. This thesis investigates large-scale igneous processes in the Earth, the Moon, and the Angrite Parent Body using a multidisciplinary approach that integrates high-pressure experiments, geochemical analysis, and petrologic modeling. In Chapter 1, I examine the mantle source lithology of Hawaiian pre-shield tholeiitic volcanism through high-pressure equilibrium experiments and geochemical modeling. In Chapter 2, I define the crystallization sequence and petrogenesis of the young mare basalts collected by the Chang'e 5 mission and propose a model for melt generation in the Moon at ~ 2Ma. In Chapter 3, I estimate a minimum radius of ~1600 km for the Angrite Parent Body using near-liquidus equilibrium experiments. The implications for planet formation models of a differentiated moon-sized planetesimal accreting in the first 3 Ma of the Solar System are also discussed. In the fourth Chapter, I introduce a new experimental technique for studying melt migration in volcanoes, which allowed me to observe and describe the mechanisms that control melt migration in the upper crust. Together, these studies advance our knowledge of melt production and crystallization conditions in planetary interiors and provide fundamental insights into the geologic history of the Earth and other rocky bodies in the Solar System.

Biochemical Analysis of Poly(ethylene terephthalate) Film Degradation Kinetics of Engineered IsPETase Variants

Wed, 01 May 2024 00:00:00 GMT

Biochemical Analysis of Poly(ethylene terephthalate) Film Degradation Kinetics of Engineered IsPETase Variants Zhong-Johnson, En Ze Linda Plastic production and pollution have become a global crisis, with 79% of waste plastics landfilled in 2015 and only 12% recycled, demonstrating the need for rapid improvements in waste management and recycling technologies. Poly(ethylene terephthalate) (PET) is a major plastic polymer that is heavily investigated for enzymatic recycling. The presence of the ester bond in the polymer allows hydrolysis via serine esterases, such as cutinases and lipases. However, little is known about the surface reaction and how biochemical behavior might differ on a 2D solid surface compared to solution phase. Consequently, traditional solution-phase biochemical models, such as Michaelis-Menten, may not be directly applicable to kinetics of these enzymes, as the catalysis is occurring under a heterogeneous phase. To improve the fundamental understanding of the enzymatic reaction on the surface and derive an appropriate biochemical model for kinetic analysis, this thesis aims to develop a simple kinetic assay of PET biodegradation, identify mutations that positively impact product formation rates, and develop a novel biochemical model to analyze these mutations that fully describe the kinetic profiles observed for these enzymes. I developed a kinetic assay based on spectrophotometric measurements of UVabsorbance of the products in the reaction supernatant, as degradation products harboring the benzene ring will absorb between 240-280 nm. The method was found to be reliable to obtain relative measurements of initial reaction rates but cannot be used to determine the absolute concentration of products in the supernatant. I also developed a directed evolution assay of IsPETase using solid PET film substrates and found that mutation T116P improved maximum product accumulation by 30% based on kinetic studies and thermostability, while mutations S238N and S290P improved purification yield and thermostability. Finally, my collaborators and I found that the activity of IsPETase is impacted by surface crowding and developed a biochemical model to analyze the kinetic data of mutants. Based on the kinetic model, T116P reduced crowding susceptibility with no impact on activity, resulting in improved macroscopic degradation rates. In conclusion, crowding tendency may become a major property to be targeted for enzyme engineering to improve solid-substrate depolymerases for industrial applications.

Measurement of the spectrum of resonance fluorescence induced by a monochromatic field.

Thu, 01 Jan 1976 00:00:00 GMT

Measurement of the spectrum of resonance fluorescence induced by a monochromatic field. Wu, Frederick Yung-Fung. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 1976; Vita.; Includes bibliographical references.

Essays on attention and creative thought

Sun, 01 Sep 2024 00:00:00 GMT

Essays on attention and creative thought Wang, Jocelyn Yuxing In the mental life of an ordinary person, creative thoughts, as well as other non-rigid forms of thought, such as mind wandering, are both pervasive and important for our cognitive endeavors. The goal of my dissertation is to provide a theory of these non-rigid forms of thought by understanding some of the cognitive mechanisms that underlie them, as well as to understand how these underlying mechanisms contribute to our epistemic lives more generally in all kinds of reasoning. Chapter 1 (based on co-authored work with Azenet Lopez) begins with a puzzle that arises from research on mind wandering: since during mind wandering we plausibly prioritize the information relevant to the concurrent tasks less, why does mind wandering sometimes improve rather than impair concurrent task performance? I resolve the puzzle by rejecting the standard conception of attention, according to which the more focused one’s attention is, the better it is at improving task performance. I instead argue that certain tasks are better performed with a more diffuse rather than focused mode of attention. I offer a conception of "diffuse attention" that generalizes from external to internal forms of attention and conceptualize mind wandering as an instance of it. Chapter 2 turns to provide an account of creative thinking, which is closely related to mind wandering. I argue that previous accounts in philosophy about the generation of creative thought are incomplete due to overlooking the role of what I call “memory gists”. Memory gists are memory contents that represent more abstract or qualitative features that are extracted from the specific, surface level features in the memory representations that were initially encoded in memory. I argue that generating and using memory gists in memory search enables highly creative people to form connections between memory contents that are not usually associated with each other by revealing their commonalities shared in their gists. Moreover, I argue that different mechanisms underlie online and offline generation of memory gists: the former involves the mode of diffuse attention that I conceptualized in Chapter 1, while the latter involves memory consolidation during sleep or wakeful rests. The active role that memory plays in creative thinking raises some questions about how to conceptualize the function of memory in our epistemic lives more generally. I explore this topic further in Chapter 3, where I reject the traditional view in epistemology that memory merely functions to preserve previously acquired information, such as information acquired through perception. I argue instead that one of the functions of memory is to improve our understanding of what was represented in the contents that we previously acquired. This is possible thanks to the fact that during memory consolidation, our memory system further processes previously acquired information, and generates representations about relationships between different components of the subject under consideration. My work thus contributes to the ongoing project of understanding memory as an active process instead of merely performing the role of storing information, and highlights understanding as one of the epistemic values that memory generates.

Constraints on vowel-zero alternations in Hungarian

Sun, 01 Sep 2024 00:00:00 GMT

Constraints on vowel-zero alternations in Hungarian Takács, Dóra Kata I analyze a large set of Hungarian nominal stems whose last vowel alternates with zero in certain contexts (Vago (1980), Siptár & Törkenczy (2000)): e.g. bokor [bokor], bokr-ok [bokr-ok]. I argue that the mechanism underlying these alternations is syncope, departing in this from earlier work (Vago (1980), Abondolo (1988), J. Jensen & Stong-Jensen (1988, 1989), Törkenczy (1995), Abrusán (2005)) which assumes epenthesis or metathesis. My research focuses on which stems fall into this closed group of vowel-zero alternating stems. I show that there is an interaction between phonological processes that repair phonotactically illicit consonant clusters – like voicing assimilation, gemination, affrication – and vowel-zero alternations. I present a proposal relying on underspecification that correctly predicts that these phonological processes block vowel-zero alternations. The grammar that generates this result includes a ranking schema where the constraint triggering syncope (referred to below as Syncope) is outranked not only by the Markedness constraints that define illicit CC-clusters in Hungarian but also by the faithfulness constraints that are normally violated in the repair of such clusters. The general ranking I will argue for is: (1) Markedness (*CC for various CCs) » Faithfulness to Cs » Syncope » Max V I also present results from a nonce word experiment, which confirms that Hungarian speakers are aware of the systematic restrictions my analysis characterizes. The broad significance of the work is to document a large-scale conspiracy (Kisseberth (1970)) whereby permissible CC clusters emerge in at least two ways: through direct action of repair processes (assimilation or merger of two Cs into one) and through blockage of the syncope process that could yield the inputs to such repairs.

Co-optation of B Cell Developmental States in Malignancy and Autoimmunity

Thu, 01 Feb 2024 00:00:00 GMT

Co-optation of B Cell Developmental States in Malignancy and Autoimmunity Ramseier, Michelle L. Transcriptional states provide a useful lens for understanding the diversity of cell identity and function. Cell state is regulated through both cell-intrinsic and -extrinsic mechanisms, and can diversify through tightly regulated transitions. However, perturbations to these regulatory mechanisms facilitate shifts in cell phenotypes and function that, gone unchecked, can disrupt homeostasis and drive disease. The ability of dysregulated transcriptional states to integratively represent underlying intrinsic and extrinsic drivers of aberrant cell survival and function nominates their potential as prognostic and therapeutic targets in disease. Here, we establish the therapeutic significance of cell state through the lens of pathologies that dysregulate B cell development and maturation. B cells develop and mature through tightly regulated cell-intrinsic and -extrinsic transcriptional state transitions restricted by stage-specific survival, proliferative, and apoptotic dependencies. We thus utilize B cell development and maturation as model systems to study how perturbations to cell-intrinsic and -extrinsic regulators can result in the pathologic emergence of aberrant developmental states enabling dysregulated survival and proliferation. In each chapter, we apply single-cell RNA-sequencing to define heterogeneous B cell developmental states in malignancy and autoimmunity, and uncover underlying signaling perturbations linked to their dysfunctional transcriptional regulation. We consider how these aberrant developmental states are driven by mutational perturbations to dysregulated cell-intrinsic signaling in BCR-ABL1 B cell acute lymphoblastic leukemia (B-ALL), cell-extrinsic signaling in CTLA4-deficient T cell-mediated follicular B cell blocks, and integrative mutational and niche-specific survival in mantle cell lymphoma (MCL). Finally, we identify how these aberrant developmental states shift or resolve upon targeted therapeutic intervention in each disease context. Collectively, this work demonstrates how cell states are intrinsically and extrinsically regulated, inform aberrant survival in disease, and demonstrate promise as therapeutic targets.

Germanium on Silicon Integrated Photonics for the Mid-Wave Infrared

Fri, 01 Sep 2023 00:00:00 GMT

Germanium on Silicon Integrated Photonics for the Mid-Wave Infrared Morgan, Rachel E. This thesis presents the development of a Germanium-on-Silicon (GOS) integrated photonics platform for the mid-wave infrared (MWIR) wavelength range. Integrated photonics applies nanofabrication approaches with optical materials in order to miniaturize and improve the robustness of optical systems. Most integrated photonics development occurs in the near-infrared for telecommunications applications, but there is increasing interest in expanding the technology to other wavelength ranges. The MWIR wavelength range has applications in environmental sensing, industrial monitoring, and communications. This work develops low-loss waveguides, a passive component library, integrated modulators, laser integration design, and systems analysis for the 2-5 µm wavelength range. Low-loss waveguides are demonstrated with losses of 0.6-2.5 dB/cm without top cladding. A detailed study of top cladding materials is conducted including niobium pentoxide, hafnium dioxide, epitaxial silicon, and others. Of these materials, niobium pentoxide offers the best performance with measured losses as low as 3.49±0.3 dB/cm. A passive component library is designed based on waveguides with and without top cladding, developing building-block components such as couplers, splitters, ring resonator filters, and Mach-Zehneder interferometers. Air-clad ring resonators demonstrate narrow-bandwidth filtering with a recorded extinction ratio of >20 dB, full-width half max (FWHM) of 0.7 GHz, and unloaded Q factor of >190,000. Integrated phase shifters are designed based on the plasma-dispersion effect and the thermo-optic effect. A plasma dispersion effect modulator is designed for forward-bias operation at 4.6 µm wavelength with predicted half-wave voltage (V subscript 𝜋) of 0.5 V, length (L) of 525 µm, voltage-length product (V subscript 𝜋L) of 0.027 V·cm, and speed of 58.4 MHz. A reverse-bias plasma dispersion effect modulator at 4.6 µm wavelength is designed with predicted V subscript 𝜋 of 4 V, L of 3 mm, V subscript 𝜋 L of 1.24 V·cm, and speed of 3.2 GHz. Thermal phase shifters fabricated out of 400 µm long metal wires have a predicted power required for a 2𝜋 phase shift (P₂ subscript 𝜋) of 410 mW without top cladding and a P₂ subscript 𝜋 of 100 mW with Nb₂O₅ cladding. Designs for flip-chip integration of quantum-cascade lasers (QCLs) are presented. Coupling between the QCL and the GOS waveguides is simulated and input tapers are designed. Test structures for QCL-waveguide coupling and external cavity QCL demonstration are designed. The predicted coupling from the QCL into a GOS airclad waveguide is 10-30%. The components designed in this work can be combined to develop photonic integrated circuit (PIC) designs for applications in the MWIR wavelength range. To demonstrate this, design analysis for a gas-sensing lidar transmitter and a MWIR spectrometer are presented. The gas-sensing lidar is predicted to provide a sensitivity to N₂O of <1% with a much smaller mass compared to existing free-space optics satellite lidar transmitter designs. The MWIR spectrometer has a predicted spectral resolution of 0.2 nm.

Site-specific chemical and topological modifications to augment mRNA therapeutic potential

Sun, 01 Sep 2024 00:00:00 GMT

Site-specific chemical and topological modifications to augment mRNA therapeutic potential Aditham, Abhishek J. Synthetic mRNA has emerged as a promising therapeutic platform for the treatment of a wide variety of diseases. Despite clinical demonstrations of mRNA for SARS-CoV-2 vaccines, mRNAs remain limited in application by their susceptibility to nucleases and overall short expression lifetime in vivo. We investigated the site-specific installation of chemical and topological modifications into therapeutic mRNA to augment their expression in cell cultures and mouse models. We began by developing messenger oligonucleotide-conjugated RNAs (mocRNAs), which are mRNAs ligated to modified oligonucleotides that contain 3’ nuclease-resistant modifications. We show that mocRNAs are subject to slower deadenylation and enhance therapeutic protein expression in cell lines and primary cell cultures. We expanded on this technology by creating mRNAs with chemically branched poly(A) tails, or multitail mRNAs, which increase the density of modifications at the 3’ end of mocRNA and further stabilize mRNA against deadenylation. In conjunction with increased nuclease resistance at the 3’ terminus, we developed a strategy to enhance translation initiation on circular mRNAs (circRNAs). We developed QRNAs, which are circRNAs that possess an unnaturally-linked inverted 7-methylguanosine (m7G) cap. QRNAs substantially outperform conventional circRNAs, given the low translation initiation efficiency of IRES compared to cap-dependent initiation. Ultimately, our studies exploring the chemical and topological space of mRNA demonstrates the value of site-specific chemical and topological modifications for designing future generations of designer mRNA-based therapeutics.

Precision measurement of the W boson mass with the CMS Experiment in pp collisions at √s = 13 TeV

Sun, 01 Sep 2024 00:00:00 GMT

Precision measurement of the W boson mass with the CMS Experiment in pp collisions at √s = 13 TeV Yang, Tianyu Justin The mass of the W boson, mW, is an important fundamental constant of nature, which is also potentially sensitive to a plethora of physics beyond the Standard Model. In this thesis, we discuss the precision measurement of mW with the CMS detector at the LHC in proton-proton collisions at √s = 13 TeV. The phenomenology of W bosons produced in pp collisions, the CMS detector characteristics, and other relevant factors are examined to justify the overall strategy to measure mW from the muon transverse momentum and pseudorapidity spectrum [formula] in the W → µν channel with a part of the 2016 data corresponding to an integrated luminosity of 16.8 fb⁻¹. Dedicated studies aiming to reduce systematic uncertainties related to the muon transverse momentum calibration, the muon reconstruction and background rejection efficiencies, and the modeling of the W boson production and decay kinematics are presented. A profiled maximum-likelihood fit of MC templates to observed data incorporating over 4,000 nuisance parameters is employed to extract the central value and the total uncertainty on m_W. The result of this measurement is m_W = 80, 360.2 ± 2.4 (stat.) ± 9.6 (syst.) MeV,= 80, 360.2 ± 9.9 MeV, which is consistent with the standard model prediction m(SM/W) = 80, 354.5 ± 5.7 MeV.

Just Doing My Job: Normative Dimensions of Social Roles

Sun, 01 Sep 2024 00:00:00 GMT

Just Doing My Job: Normative Dimensions of Social Roles Wells, Eliza “What should I do?” Often, our answers make reference to our social roles: we ask what we should do as lawyers, citizens, or parents. But this confronts us with problems. Consider a would-be whistleblower, a wife challenging the gendered division of household labor, or the conflicted police officer Javert from Les Misérables. These agents feel there is a genuine conflict between morality and the norms of their role. While many philosophers treat social roles as incidental to our moral lives, this dissertation aims to do justice to this experience of roles’ normative force. I argue that doing so prompts revision to orthodox views of role-occupants’ reasons for action, blameworthiness, and responsibility for structural injustice. In Chapter One, I develop a new account of how social roles generate normative reasons for occupants to comply with role norms. I argue that agents’ reasons to comply with their role norms depend on how those norms contribute to functioning social practices. In addition to its claims about the structure of normative reasons, my view delivers a striking upshot in cases of conflict. While popular accounts of role normativity often maintain that moral considerations can cancel roles’ normative force, my project suggests a radically different conclusion: role-occupants have good reasons to comply even with norms that result in conflicts with what they morally ought to do. Social roles generate genuine normative conflicts. While many role-occupants find conflicts between roles and morality distressing, many others seem not to notice that there is a conflict at all. Consider the oft-maligned excuse: “I was just doing my job.” Chapter Two defends an epistemic variant of this excuse. I argue that agents who comply with roles’ deliberative norms may—for good reason—bracket morally relevant considerations. As a result, they may be non-culpably ignorant of wrongdoing. On some views, this can excuse them from blame. But even denying that moral ignorance exculpates is compatible with accepting role-occupants’ excuses. Such views often emphasize being motivated by the right reasons. But because role compliance is often justifiable, ignorance need not be blameworthy indifference to the right reasons. The upshot is a novel position in the debate about moral ignorance as an excuse. We might worry that this unduly lets role-occupants off the hook. If, as I argue, roleoccupants can have good reasons for acting immorally, and they can sometimes be blameless even when they do act wrongly, does that prevent us from taking those wrongs seriously? I grapple with this problem in Chapter Three. Drawing on theories of structural injustice, I argue that roles’ normative character actually generates responsibilities for justice. Because role performance both affirms and instantiates unjust structures, role-occupants bear responsibilities that can only be discharged by changing what their actions mean and do. This vindicates the widespread but philosophically puzzling view that agents ought to direct efforts towards injustices they participate in intimately, even when they could make a greater impact elsewhere. It also means that role-occupants are not off the moral hook. Ultimately, we each bear responsibility to create a more just social world.

Amplifying signals in the tumor microenvironment for drug development and diagnostics

Thu, 01 Feb 2024 00:00:00 GMT

Amplifying signals in the tumor microenvironment for drug development and diagnostics Martin Alonso, Maria Carmen The advent of molecular biology and next-generation sequencing has significantly transformed our understanding of cancer and the delivery of cancer care. These advancements greatly accelerated the pace of biological discovery leading to the development of targeted therapies and immunotherapies, which have resulted in unprecedented survival benefits for patients. Additionally, they have also materialized in how disease is diagnosed and monitored, which increasingly involves genomic profiling of circulating tumor DNA (ctDNA) molecules in liquid biopsies such as blood. Despite the promise offered by these innovative therapies and monitoring tools, their broad integration into clinical practice faces important challenges. Widespread adoption of emerging therapies demands enhanced tools to successfully identify therapeutic targets and to restrict their potent activity to cancer cells. At the same time, improving the sensitivity of ctDNA-based tests, that remains limited by the scarcity of ctDNA in blood, holds the key to unlock the full potential of liquid biopsy across many important clinical applications. This thesis addresses these critical challenges by combining the unique opportunities posed by secreted molecules in the tumor microenvironment (TME) with engineering principles of signal amplification. Tumors are intricately tied to their local and systemic microenvironments for tumor progression, and key to orchestrating such interactions are secreted molecules. Targeting these abundant molecules that are accessible extracellularly and oftentimes even systemically, offers significant advantages over traditional approaches that target confined, scarce and occult malignant cells within tissues. In Part I of this thesis, we propose exploiting the catalytic activity of tumor-associated proteases in the local TME to selectively deliver potent therapies to cancer cells while sparing healthy tissues. Leveraging advancements in high throughput screening and in deep learning, we contribute important tools for the effective design of conditional drugs that require the cleavage of a protease substrate to unleash drug cytotoxicity. In Part II, we address challenges in ctDNA detection by introducing liquid biopsy priming agents - DNA-binding proteins and nanoparticles - that transiently attenuate endogenous ctDNA clearance routes. Priming agents synthetically amplify ctDNA levels in blood to greatly improve the sensitivity and the robustness of liquid biopsies. Our approach marks a paradigm shift in how we think about the limit of detection of molecular diagnostics and holds promise for other circulating biomarkers and beyond oncology. Collectively, this thesis presents a TME-centric perspective of cancer, coupled with engineering principles of signal amplification, to reframe therapeutic and diagnostic paradigms in oncology, with far-reaching implications across all stages of cancer management.

Structural case on adjuncts

Sun, 01 Sep 2024 00:00:00 GMT

Structural case on adjuncts Jou, Eunsun This dissertation investigates how case is assigned to nominal adverbials dubbed durative and multiplicative in Korean. These adverbials express the duration of an event, or the number of times an event is repeated. In transitive, unergative, and unaccusative constructions, the adverbial is marked with accusative case. In psychological predicate constructions, the adverbial is marked with nominative case. Interestingly, in passive and inchoative constructions (grouped together under the term nonactive), the adverbial allows both nominative and accusative case. I derive these patterns from a specific model of Voice, and a model of successive-cyclic Dependent Case. I first argue in favor of a Voice system that treats passive and inchoative constructions as syntactically equivalent: whether a nonactive construction is passive or inchoative is determined by the feature specification on Voice (Kallulli 2007). Furthermore, this nonactive Voice head introduces an implicit agent (for passives) or causer (for inchoatives), which can be optionally realized as a PP. This agent/causer at Spec, VoiceP competes with the theme argument to move to Spec, TP. Hence, there are two different structures that can arise in nonactive constructions. The other constructions that do not show case optionality lack this competition. In transitive, unergative, and unaccusative constructions, there is no implicit agent/causer at Spec, TP to compete with the theme argument. In psychological predicate constructions, the experiencer argument introduced at Spec, ApplP acts as an intervener and blocks the theme argument from competing with the implicit agent/causer. My model of successive-cyclic Dependent Case explains how the different structures result in different case patterns. It is a revised version of Levin’s (2017) original model, whereby case evaluation occurs not only at the end of the syntactic derivation but at the Spell-out of each phase. However, my version of the model involves a more relaxed locality constraint for dependent case assignment. I demonstrate how my model can not only derive the case marking patterns of durative and multiplicative adverbials, but can also account for other case phenomena in Korean such as case stacking and multiple nominative constructions.

Assessing the Intersectional Risks Associated with the Full Life Cycle of the U.S. Housing Stock

Thu, 01 Feb 2024 00:00:00 GMT

Assessing the Intersectional Risks Associated with the Full Life Cycle of the U.S. Housing Stock Manav, Ipek Bensu This work presents the most comprehensive framework to date to assess the intersectional risks associated with design and policy decisions regarding the built environment. This framework is applied to decisions regarding the selection of hazard mitigation measures to apply, households to prioritize in hazard mitigation grant programs, and construction materials to use in efforts to reduce societal greenhouse gas (GHG) emissions. To study these decisions, a computation inexpensive method is developed to compute expected damages associated with each individual building in a community with hurricane wind exposure. This method is applied to study the cost burden of expected damages on each individual household. Later, this is integrated into building life cycle assessment (LCA) to incorporate hazard vulnerability into building embodied emissions. Lastly, building LCA is extended to inform the sectoral environmental footprint (SEF) of construction material sectors. Together, the model results of this work show that expected damages are currently underestimated, socially-vulnerable groups are likelier to be priced out hazard repairs, and ignoring use and end-of-life stages leads to ignoring the largest portion of building life cycle emissions as well as the largest contributors of the SEF of construction materials. By reevaluating the performance of the housing stock under each metric, strategies are proposed to prevent monetary damages, redistribute the cost burden of remaining monetary damages, and couple considerations for climate mitigation and climate adaptation by promoting disaster risk reduction as a pathway towards GHG abatement.

Habit Formation and Political Persuasion: A Behavioral and Statistical Approach

Fri, 01 Sep 2023 00:00:00 GMT

Habit Formation and Political Persuasion: A Behavioral and Statistical Approach Tohidi Kalorazi, Amir This thesis explores the complex dynamics of human behavior across diverse contexts, integrating perspectives from behavioral science and statistical analysis. The central focus of this study revolves around the analysis of repetitive behavior in various scenarios including shopping, social media use, and news sharing. The initial study investigates the influence of habits on the in-store shopping experience. By leveraging store closures as a disruptive event, we examine how these closures prompt individuals to alter their purchasing patterns. We propose that such disruptions encourage people to engage in more deliberate decision-making processes, leading them to explore alternatives that they might have previously overlooked due to established habits. Employing a difference-in-differences framework, we estimate the causal impact of habits on brand loyalty. Our findings reveal a significant role of habits, with households exhibiting stronger habits experiencing a temporary disruption in their shopping routines following store closures. Over time, these households appear to develop new habits in different stores, resulting in lasting changes in preferred brands. This suggests that the formation of shopping habits can lead to suboptimal consumer behavior. These insights have practical implications for businesses, including pricing strategies, advertising approaches, and product placement within stores. The second study introduces an innovative methodology for quantifying habitual behavior in the context of social media usage. Interactions with social media platforms often yield psychological rewards, fostering the development of habitual behaviors driven by cue-response associations. By leveraging entropy as an implicit measure of behavioral regularity, this study aims to uncover the intricate relationship between habit formation and digital routines. Through empirical analyses, we establish the validity of the entropy metric, demonstrating its effectiveness in capturing distinct behavioral patterns beyond mere frequency. Our results highlight the nuanced connection between entropy and future app engagement, indicating a positive association for lower entropy values and a significant decline for excessively irregular patterns. These findings contribute to theoretical understanding of habitual behavior and offer practical insights for managing digital habits. Ultimately, this work advances our comprehension of how habits manifest in the digital realm and provides a robust tool for predicting long-term user behavior. The third study delves into the intricate interplay between individuals' beliefs and their ability to anticipate the persuasive impact of climate change news articles. The central aim is to determine whether climate change deniers or believers possess varying capacities to predict the persuasive consequences of articles emphasizing climate change severity. Through a series of surveys, we gather predictions about the impact of such articles on climate change deniers. Surprisingly, findings reveal discordant predictions: deniers anticipate a backfire effect among peers, climate believers foresee negligible effects. We rigorously test these predictions with a randomized survey experiment involving deniers, uncovering an unexpected positive opinion shift towards climate change after article exposure. Notably, this effect does not translate into discernible changes in stated or revealed support for climate change actions. In the context of the pressing climate challenge, our study offers insights to inform targeted communication and interventions that foster consensus and meaningful action.

The Ethics within Metaphysics

Sun, 01 Sep 2024 00:00:00 GMT

The Ethics within Metaphysics Impagnatiello, Michele Odissea This dissertation consists of three chapters at the intersection of ethics and metaphysics. In the first chapter, I put forward a new theory of personal identity, give arguments for it, and defend it from objections. In the first part, I argue that the two most prominent theories of personal identity, the psychological theory and the physical theory, do not satisfy some constraints on any acceptable theory: that personal identity be all-or-nothing, determinate, principled, and substantive. I then put forward a new theory, the phenomenal theory, on which personal identity is determined by the uninterrupted continuity of a stream of consciousness. I argue that this theory does satisfy all the desiderata, and is as such a better theory. In the second part, I argue that the phenomenal theory also solves the problem of fission cases, because there are no cases of phenomenal fission. In the third and last part, I consider the objection that, on the phenomenal theory, we do not survive interruptions of consciousness such as sleep; I argue that this objection doesn’t succeed in refuting the theory. In the second chapter, I generalize a debate about laws of nature to the domains of metaphysics and ethics. Patterns in the natural world lead us to the postulation of laws. A metaphysical dispute arises as to whether these laws are mere summaries of the mosaic (as the Humean would have it), or whether they govern the mosaic (as the Anti-Humean would have it). In this paper, I first argue that similarly, patterns in the metaphysical and ethical facts should lead us to the postulation of metaphysical and ethical laws, which are the proper subject of metaphysical and ethical inquiry. Then, I argue that the Humean/Anti-Humean debate also arises when it comes to metaphysical and ethical laws. Finally, I argue in favor of the Anti-Humean conception of metaphysical and ethical laws, both adapting standard arguments used in the debates about laws of nature, and with new arguments specific to metaphysics and ethics. In the third chapter, I investigate conflicts between ethics and metaphysics. Sometimes, a metaphysical theory has revisionary ethical consequences: for example, some have thought that modal realism entails that there are no moral obligations. In these cases, one may be tempted to reject the metaphysical theory on the grounds that it conflicts with commonsensical ethics. This is an ethics-to-metaphysics inference. My claim is that this inference is in general irrational, and that the fact that a metaphysical theory has highly revisionary ethical consequences is no reason at all to reject the theory. I argue for this claim on the basis of general epistemic principles about the transmission of justification, and what makes for a good argument. Furthermore, I argue that my account can explain why a certain narrow class of ethics-to-metaphysics inferences are rational.

Multidimensional profiling of the Toxoplasma gondii proteome

Fri, 01 Sep 2023 00:00:00 GMT

Multidimensional profiling of the Toxoplasma gondii proteome Herneisen, Alice Lydia Universally, external signals are transduced and propagated in cells by secondary messengers. In the asexual and replicating stages of apicomplexan parasites, these pathways initiate and sustain transitions within the lytic cycle responsible for parasite spread and pathogenesis. Among these early-branching parasitic protists are the etiologic agents of the widespread, persistent, and deadly human diseases malaria (Plasmodium spp.) and toxoplasmosis (Toxoplasma gondii), making the understanding of these parasite signaling pathways of global importance. Although components of secondary messenger signaling pathways are conserved among apicomplexans and higher eukaryotes, 800 million years of divergence from existing model organisms precludes identification of parasite-specific secondary messenger responses or a priori reconstruction of their signaling pathways. This thesis addresses that gap. I have adapted state-of-the-art proteomics methods to study the proteome of the model apicomplexan T. gondii across multiple dimensions: abundance, stability, time, and space. Chapter 2 describes how I employed thermal proteome profiling to identify the target of an antiparasitic compound, thereby enhancing our understanding of parasite calcium signaling pathways. In a conceptual leap, I applied this method to systematically identify calcium-responsive proteins on the basis of biochemical interactions with this second messenger in Chapter 3. From this analysis, the protein phosphatase PP1 emerged as an unanticipated calcium-responsive phosphatase along with dozens of novel proteins belonging to this critical signaling network. Signaling pathways communicate to orchestrate complex cellular processes, yet in apicomplexan parasites they are often studied in isolation. In Chapter 4, I identify a node linking three key second messenger pathways in T. gondii: calcium, cyclic GMP, and cyclic AMP. The apicomplexan-specific kinase SPARK regulates the AGC kinases PKG, PKA C1, and PKA C3, which together control transitions within the asexual cycle of this important family of parasites.

Phonetic Faithfulness in Phonological Opacity

Sun, 01 Sep 2024 00:00:00 GMT

Phonetic Faithfulness in Phonological Opacity Kim, Yeong-Joon This dissertation presents a novel approach to phonological opacity, which is grounded in new findings regarding substantive restrictions on the patterns of opaque interactions. The central thesis posits that phonological opacity functions to preserve the phonetic properties specified in the input of a phonological operation. Specifically, it argues that inputs are enriched with phonetic auditory features, and surface opacity emerges as a result of processing these enriched inputs. This proposal can be detailed as follows. First, processes that become opaque are initially biased by certain phonetic markedness conditions. Second, these phonetic biases, encoded in the phonetically enriched inputs, are mapped onto the nearest phonologically contrastive sounds to satisfy the requirement of phonetic faithfulness, resulting in surface phonological opacity. This hypothesis yields a testable prediction: only phonetically natural processes, which possess an appropriate phonetic markedness condition, can become opaque. The results of typological surveys encompassing 87 counterfeeding and 65 counterbleeding interactions across languages support this prediction, revealing that opacified processes are subject to a narrow range of markedness conditions, such as coarticulatory assimilation (e.g., palatalization) and durational adjustments (e.g., segmental weakening). Other types of phonological processes, particularly non-natural ones, are only rarely, if ever, opacified. This asymmetry in the patterns of phonological opacity underscores that opaque interactions are not independent of phonetic substance. In addition to this main finding, it is also shown that the current proposal offers additional advantages in explaining phonological opacity. First, it successfully accounts for various non-typical opaque interactions such as feeding opacity and stress misapplications, alongside counterfeeding and counterbleeding interactions. The proposal also integrates various phonological phenomena, such as compensatory lengthening, coalescence, and incomplete neutralization, within the framework. Second, learning simulations using a weighted constraint version of the proposed model demonstrate that intermediate hidden structures, such as phonetically enriched inputs, can be learned when the mappings between abstract inputs and surface representations are established.

Technological Innovation and Integration of Whole Brain Imaging, Olfactory Stimulation, and Correlative Microscopy in Larval Zebrafish

Sun, 01 Sep 2024 00:00:00 GMT

Technological Innovation and Integration of Whole Brain Imaging, Olfactory Stimulation, and Correlative Microscopy in Larval Zebrafish Swain, Corban N. Achieving a deep understanding of the brain is a cross-disciplinary endeavor that requires the investigator to consider biomolecular, electrical, and sensory interactions across time and space at many scales. This understanding is important because a deeper understanding of the brain precedes advancements in efficient computing, generalizable frameworks for learning, and, of critical importance, the understanding and treatment of neurological diseases. Towards this end, this thesis presents novel approaches and technologies for whole-brain imaging, olfactory stimulation, and correlative imaging---i.e. the utilization and registration of multiple imaging modalities within a single sample. The overall objective of this thesis research is to not just create technologies, but to integrate them to enabler richer and more contextual understandings of the larval zebrafish's brain. In this work we show novel light field microscopy algorithms that allow us to reconstruct 3D images from 2D micrographs with improved resolution to enable high-frame-rate recordings of whole-brain neural activity. We describe the designing and building of the first known system for multi-directional olfactory stimulation of larval zebrafish with up to ten separate odor channels. We demonstrate an optimized expansion microscopy-compatible immunostaining protocol for whole-mount zebrafish which preserves registration epitopes to move towards the neuron-level alignment of structural and functional data. And, finally, we showcase a set of proof-of-concept experiments and analyses which demonstrate our ability to integrate olfactory stimulation, whole-brain calcium imaging, behavioral recording, and structural staining in individual larva.

Optimizing resource allocation in large communications satellite constellations

Wed, 01 May 2024 00:00:00 GMT

Optimizing resource allocation in large communications satellite constellations Pachler de la Osa, Nils Satellite communications are becoming a key technology for maintaining connectivity in a world driven by information. In the recent years, established players (such as SES and Telesat), as well as new competitors (such as SpaceX and Amazon) have proposed constellations able to serve hundreds of thousands of users, using thousands of satellites. While the orbital configuration of each design is different, the next generation of satellite communications relies on highly flexible digital payloads, such as phased array antennas, on-board processing, and adaptive modulation and coding schemes. Several approaches have been proposed to deal with the complexity of the added flexibilities at the spacecraft level. Nevertheless, how to address the flexibilities at the constellation level, critical to operate the next generation of systems, remains an open question. This dissertation develops optimization-based decision-making frameworks for designing and operating the next generation of communication constellations. In particular, novel methods for the Beam Shaping, User Grouping, Satellite Routing, Frequency Assignment, and Gateway Routing problems are proposed, tailored for large non-geostationary orbit constellations with satellites at multiple altitudes, referred to as hybrid systems. The methods leverage optimization to find an optimized set of decisions that maximize capacity and quality of service and minimize necessary ground infrastructure, all while avoiding interference. The proposed methods are then combined, tested, and evaluated using existing constellation designs under representative operational conditions with hundreds of thousands of users. The reported results prove that the proposed techniques are able to multiply by two the capacity of these systems, with favorable trade-offs in quality of service and necessary ground infrastructure. By testing existing designs, it is concluded that the number of satellites, as well as the link quality are the main drivers of performance. Furthermore, the analysis shows that hybrid constellations offer advantages over other designs, thanks to the combination of high quality links on low altitude satellites, and high coverage on high altitude satellites. Additionally, this dissertation studies the optimal proportion of satellites across various altitudes in hybrid LEO-MEO constellations. Results show that hybrid constellations are desirable when the cost of MEO and LEO satellites are comparable and interference is minimal.

Theoretical Design of Molecular Nanostructures for Exciton Control

Thu, 01 Feb 2024 00:00:00 GMT

Theoretical Design of Molecular Nanostructures for Exciton Control Castellanos, Maria A. Organic semiconductors comprised of strongly-coupled chromophores harness control of delocalized excitations, or excitons, via programmed molecular structures. The dynamics of these excitons enable energy and information transfer within molecular networks, positioning chromophore assemblies as ideal candidates for a number of technologies such as solar energy conversion, nanoelectronics, and quantum computing. Despite significant advancements, there exists no universal model that can explain the dependence of exciton photophysics on molecular morphology. This thesis employs mathematical and atomistic models to contribute key physical insights into the interdependencies between chromophore spatial organization and exciton dynamics, shaped by inter-chromophore couplings and interactions with the thermal bath. In the first part, a Frenkel Exciton-based model is introduced as a strategy for studying exciton evolution between precisely arranged chromophores. In Chapter 2, I develop a novel approach to map unitary quantum computing operations to Hamiltonians describing excitonic circuits in the presence of a model bath. Then, Chapter 3 scales this framework to complex quantum algorithms represented by explicit molecular systems. Finally, Chapter 4 presents an innovative molecular approach for directing exciton flow via geometrical phase in tightly-bound chromophore arrays. The second part delves into the intricacies of exciton interaction in densely packed molecular systems arranged within DNA scaffolds. Chapter 5 combines molecular dynamics and quantum mechanical calculations, further validated by experimental results, to study the interplay between long-range electrostatic and short-range charge transfer interactions. Chapter 6 then correlates this interplay with geometrical configurations derived from the DNA scaffolding. This thesis culminates in Chapter 7, which introduces a computational pipeline designed to leverage the precise control over excitons afforded by macromolecular frameworks, paving the way for custom-tailored DNA-based excitonic circuits.

High field dynamic nuclear polarization methods: Microwave sources and mechanisms

Thu, 01 Feb 2024 00:00:00 GMT

High field dynamic nuclear polarization methods: Microwave sources and mechanisms Mardini, Michael In thirty years of active development, dynamic nuclear polarization (DNP) has emerged as a forefront technique for expanding the scope of solid state nuclear magnetic resonance. For the most part, and particularly at high fields, these advances have come with continuous-wave microwave irradiation and the introduction of nitroxide-based biradicals exploiting the cross effect mechanism. In this thesis, I argue that this approach is not necessarily optimal and report progress towards arbitrary-waveform DNP, in the construction of a suitable solid-state microwave source, and the use of narrow-line monoradicals exploiting the Overhauser effect. My colleagues and I have also investigated the Overhauser mechanism through selective deuteration of radicals, leading to a relatively simple modification which yielded a significant increase in Overhauser enhancement. Finally, I detail studies of two unexplored DNP mechanisms in trityl: the three-spin solid effect and resonant mixing. With solid-state microwave sources and Overhauser radicals, DNP is now more accessible as we can achieve reasonable enhancement without the need for a gyrotron. Moreover, as amplifier and resonator technologies continue to develop, it is likely that pulsed DNP will emerge at high fields and overtake continuous-wave DNP in absolute sensitivity enhancement as well.

Methods for the Study of Galactofuranose in Mycobacteria

Thu, 01 Feb 2024 00:00:00 GMT

Methods for the Study of Galactofuranose in Mycobacteria Taylor, Katherine I. Despite the energy costs associated with deploying furanose sugars over their more stable pyranose counterparts, galactofuranose is prevalent across nature from commensals to human pathogens. However, it is conspicuously absent from human cells, thus establishing its biosynthetic pathways as important potential drug targets. Our knowledge about the biological roles of galactofuranose in cells is hampered by a dearth of methods by which to study it. Access to glycans containing galactofuranose is limited by the unfavorable equilibrium between pyranose and furanose forms of the sugar, leading to low-yielding and synthetically arduous routes to galactofuranose glycans and its high-energy nucleotide sugar donor, used in biochemical experiments to probe the activity and kinetics of galactofuranose glycosyltransferases. Moreover, study of carbohydrate structures within the cell is limited by the lack of methods to selectively modify glycans with functional handles. Finally, study of the biosynthetic machinery for galactofuranose biosynthesis and inhibitors thereof is limited by their relatively weak affinities for their ligands, providing a challenge for selective chemical probes. In this work, we describe three methods to address these challenges and expedite the study of galactofuranose-containing glycans, their biological function, and their biosynthetic machinery. First, we developed a method to produce the rare high-energy sugar donor UDP-galactofuranose in situ for facile preparation of the mycobacterial galactan utilizing the sugar mutase UDP-galactopyranose mutase. We used this method to generate up to 10 milligrams of polymer and demonstrated that it could be selectively functionalized. Second, we leveraged the rapidly expanding set of biosynthetic probes of the mycobacterial cell wall to rapidly characterize intracellular distances between distinct layers of the cell wall in Mycobacterium tuberculosis model organisms Corynebacterium glutamicum and Mycobacterium smegmatis using fluorescence resonance energy transfer. We evaluated strains with varying galactan structures and compared our data to previous characterization of the cell wall to assess the method’s utility. Finally, we characterized the kinetics of a mild electrophile, the squaric ester, and assessed its utility in selectively binding and modifying a key galactofuranosyl transferase involved in mycobacterial cell wall biosynthesis. Taken together, these findings present a suite of methods to expedite the exploration of galactofuranose structure and function within a relevant pathogen and lays groundwork for further study of galactofuranose across other organisms.

Engineering Mechanical Counter Pressure Spacesuits and Compression Garments: Active Pressurization and Design for Mobility

Wed, 01 May 2024 00:00:00 GMT

Engineering Mechanical Counter Pressure Spacesuits and Compression Garments: Active Pressurization and Design for Mobility Kothakonda, Akshay Extravehicular activities (EVA’s) are an essential and integral part of human exploration of space, with their use ranging from performing scientific experiments on a planetary surface to assembling space stations. Spacesuits must provide an astronaut with the conditions necessary to survive an EVA for several hours and to enable them to carry out these complex tasks. One of the main factors impeding the effectiveness of EVA operations is the stiffness of the spacesuits, which is inherent in gas pressurized suits. While there have been several engineering advances in improving the mobility of gas pressure suits, a mechanical counter pressure (MCP) suit seeks to significantly improve mobility and minimize metabolic workload by replacing gas pressure with contact pressure of a tensioned fabric against the body. Although a marked improvement in mobility of MCP suits over traditional gas pressurized suits was demonstrated in the 1970’s with the Space Activity Suit, engineering challenges remain before this such a suit can be used operationally. Applications of the MCP suit concept extends to compression garments for athletic and medical use. This thesis seeks to address some of the fundamental requirements of an MCP suit. These include providing uniform MCP of 29.6 kPa over the body, minimizing mechanical work during suited movements, and enabling easy don and doff. While the thesis focuses on the single degree-of-freedom arm section of the suit, the work can be extrapolated to the entire body. The bidirectional actuation of two-way Shape Memory Polymers (2W-SMP) is leveraged to both provide MCP and allow for easy don/doff. This is achieved by reversing actuation via thermal stimulus. Two designs of MCP suit as an assembly of suit fabric, 2W-SMP, and elastomers are conceived and analysis is carried out to select the more feasible design. On the selected design, analysis is conducted to select a 2W-SMP with maximum MCP for a given donning effort. Two types of suit fabrics are analyzed in the design- the woven fabric and the jersey knit fabric. Nonlinear finite element analysis (FEA) models that can be used to analyze the deformation of these fabrics under suited movements have been developed. Results from these simulations are hoped to aid in designing the fabric in a such a way that sustains circumferential tension of 2 the 2W-SMP and minimizes mechanical work during movements. While the former would require the fabric to be stiff along the circumferential direction, the latter can be achieved by aligning the compliant axes of a given fabric with the directions of first principal strain. The process of estimating the optimum fabric pattern will be iterative and the resulting pattern may comprise of a composite of different fabric types with varying parameters. Mapping skin Lines of Non-Extension (LoNE’s) informs contours for inextensible cables in such a way that they do not impede movements. These cables may form part of suit life support system. This thesis focuses on developing tools such as methodology for sizing SMP’s, fabric models, and LoNE’s and as such does not use those tools to arrive at an optimum suit design. Utilization of these tools towards suit design is one of the future tasks in this work. Additionally, the author expects that future research efforts in this area at large will benefit from these tools. The thesis includes an introduction of the problem and the motivation, background and literature review of relevant concepts, a deep dive into analysis and tests on shape memory polymer materials and their use for compression devices, development of fabric numerical models, and finally a discussion of the work and a summary of contributions.

Modeling the Future Space Debris Population and Orbital Capacity

Wed, 01 May 2024 00:00:00 GMT

Modeling the Future Space Debris Population and Orbital Capacity Jang, Daniel Increased investments and technological advances in satellite manufacturing and launch services have led to a newly vitalized Low Earth Orbit (LEO) environment. Megaconstellations consisting of hundreds to hundreds of thousands of satellites have been proposed, with SpaceX’s Starlink satellite constellation now reaching more than 5400 operational satellites. This denser LEO environment underscores the urgent need for models to predict and manage the risk of collisions and the sustainable use of space. Many models have been proposed over the years to quantify the risk of collisions between resident space objects, including the seminal paper by Kessler that described the runaway conditions for which LEO could become unusable. In this thesis, the development of the MIT Orbital Capacity Analysis Tool (MOCAT) is described along with conclusions and insights. MOCAT is a novel open-source approach to evaluating the LEO environment and comprises of a Source Sink Evolutionary Model (SSEM) and a Monte Carlo (MC) method. The SSEM simplifies the complex dynamics of space-object interactions into deterministic equations, focusing on the long-term evolution of orbital populations across different altitude shells. The simplified nature of the SSEM allows for computational efficiency, which enables optimization routines such as the exploration of equilibrium solutions for LEO carrying capacity. The improvements to the SSEM in this work through binning in the physical dimension as well as inclusion of Delta-V dynamics from the collision dynamics increases the fidelity of the SSEM. In comparison, MOCAT-MC offers a comprehensive means to simulate the individual interactions between RSOs. The MOCAT-MC tool propagates the orbits of low-earth orbit objects and models their interactions including collisions and explosions, and provides insights into the evolving trends of the LEO population. Of particular note is the computational efficiency of the model, which is essential for managing the complexities inherent in orbital dynamics and the potential large number of objects centuries into the future. Validation results and a range of simulations, including no-future launch scenarios and the launch of proposed megaconstellations totaling more than 80,000 active payloads are explored, resulting in millions of trackable objects. Despite the much fewer megaconstellations planned at the higher altitudes, even a small fraction of failures in post-mission disposal or collision avoidance maneuvers result in an outsized effect on orbital debris accumulation. MOCAT-MC is able to simulate Lethal Non-Trackable (LNT) objects, which comprise the vast majority of the orbital population today. These lethal non-trackable object population will only grow as more payloads and debris are launched into orbit and increase the collision rate. The effect of these objects are modeled and discussed. These two models offer different approaches to modeling the future orbital environment each with its strengths and weaknesses. Validation against existing models in literature shows the utility of MOCAT in informing future space traffic management and constellation design. The MOCAT tool has been created such that researchers can use a common model that is validated, robust, and efficient, allowing for advancement in our ability to forecast and mitigate the risks associated with the increasing density of LEO while advocating for a more sustainable approach to space exploration and utilization.

Using Principles from Cognitive Science to Analyze and Guide Language-Related Neural Networks

Wed, 01 May 2024 00:00:00 GMT

Using Principles from Cognitive Science to Analyze and Guide Language-Related Neural Networks Tucker, Mycal Natural language, while central to human experience, is not uniquely the domain of humans. AI systems, typically neural networks, exhibit startling language processing capabilities from generating plausible text to modeling simplified language evolution. To what extent are such AI models learning language in a “human-like” way? Defining “human-like” generally may be an impossible problem, but narrower definitions of aspects of human-like language processing, borrowed from cognitive science literature, afford metrics for evaluating AI models. In this thesis, I borrow two theories about human language processing for such analysis. First, human naming systems (e.g., a language’s words for colors such as “red” or “blue”) appear near-optimal in an informationtheoretic sense of compressing meaning into a small number of words; I ask how one might train AI systems that behave similarly. Second, people understand and produce language according to hierarchical representations of structure; I study whether large language models use similar representations in predicting text. Thus, in this thesis, I show how to train and analyze neural networks according to cognitive theories of human language processing. In myfirst branch of work, I introduce a method for neural network agents to communicate according to cognitively-motivated pressures for utility, informativeness, and complexity. Utility represents a measure of task success and induces task-specific communication; informativeness is a task-agnostic measure of how well listeners understand speakers and leads to generalizable communication; complexity captures how many bits are allocated for communication and can lead to simpler communication systems. All three terms are important for human-like communication. In experiments, training artificial agents according to different tradeoffs between these properties led them to learn different naming systems that closely aligned with existing natural languages. In my second branch of work, rather than training neural agents from scratch, I probe pre-trained language models and found that some use representations of syntax in making predictions. Humans use hierarchical representations of sentence structure in understanding and producing language, but it is unclear if large language models, trained on simple tasks like next-word-prediction, should learn similar representations. I introduce a causal probing method that sheds light on this topic. By creating counterfactual representations 3of syntactically ambiguous sentences, I measured how model predictions changed for different structural interpretations of the same sentence. For example, I recorded model predictions to ambiguous inputs like “The girl saw the boy with the telescope. Who had the telescope?” with different syntactic structures. For some (but not all) models, I found that models use representations of syntax (e.g., change their answers to the previous question). Thus, I offer novel insight into pre-trained models and a new method for studying such models for other properties. Thetwohalvesofmythesisrepresentcomplementaryapproachestowardsmorehumanlike AI; training new models and analyzing pre-trained ones closes an AI development feedback loop. In this thesis, I explain my contributions to both parts of this loop and identify promising directions for future research.

Statistical Kinetics and Nonequilibrium Thermodynamics of Driven Systems: Stochastic Methods and Applications to Single-Molecule Biophysics

Thu, 01 Feb 2024 00:00:00 GMT

Statistical Kinetics and Nonequilibrium Thermodynamics of Driven Systems: Stochastic Methods and Applications to Single-Molecule Biophysics Piephoff, D. Evan Advances in condensed-phase spectroscopy have permitted the ability to obtain time traces of biomolecules at the single-molecule level of detail. These real-time trajectories provide details that are typically unavailable in ensemble-averaged experiments, such as the effect of conformational dynamics on enzymatic reactions. From a theoretical perspective, it is therefore valuable to develop kinetic approaches for characterizing measurable quantities in order to connect to such single-molecule experiments. In this thesis, we analyze the statistical kinetics and nonequilibrium thermodynamics of driven biomolecular systems, with a particular emphasis on enzymatic processes. Specifically, we focus on kinetic methodology development; analyzing single-molecule fluctuations for mechanistic insight; examining the modulating influence of conformational interconversion on enzyme catalysis; and characterizing the nonequilibrium thermodynamics of generalized biomolecular machines. For enzymatic turnover reactions, it is found that the turnover rate reduces to the celebrated Michaelis–Menten functional form when conformational detailed balance is satisfied. In the presence of non-vanishing conformational currents, we predict and characterize the rich, cooperative behaviors attainable in conformational nonequilibrium. In addition, enzyme turnover fluctuations are analyzed by studying the Poisson indicator, a normalized measure of stochastic variation. A novel pathway analysis framework is extended to nonrenewal processes (i.e., those with correlated inter-event times) and fully reversible processes, accounting for kinetic network complexities, nontrivial event-averaged initial conditions, and the constraints associated with microscopic reversibility. For a dynamically disordered biomolecular machine involving an observable process coupled to a hidden process, a recently derived time-based fluctuation theorem no longer applies to the observable first-passage time; however, using a stochastic thermodynamics approach to examine fluctuating trajectories, we find that its validity is restored in the absence of hidden flux through the initial state manifold. Thus, the violation of this relation serves as an experimentally verifiable signature of hidden detailed balance breaking. The analysis presented herein provides a novel framework for analyzing a variety of kinetic processes, including enzyme turnover, molecular motor translocation, ion transport, and fluorescence emission.

Studies on stereospecific diketopiperazine oxidation and applications to the synthesis of complex epidithiodiketopiperazines

Thu, 01 Feb 2024 00:00:00 GMT

Studies on stereospecific diketopiperazine oxidation and applications to the synthesis of complex epidithiodiketopiperazines Walker, Katherine L. I. Introduction and Background on Epidithiodiketopiperazines A brief history and summary of methods for synthesis of epidithiodiketopiperazines (ETPs) are discussed. Three hypotheses for the mechanism of action of these biologically active natural products are reviewed, and the unified biosynthetic hypothesis that our group disclosed is summarized. The total syntheses of the natural product hyalodendrin are analyzed as a case study of the total synthesis of ETPs, and representative examples of our group’s entries into the synthesis of complex ETPs are examined. II. Studies on Stereospecific Diketopiperazine C–H Hydroxylation Mechanistic investigation of the permanganate-mediated hydroxylation reaction of 2,5-diketopiperazines (DKPs) is discussed. The course of the hydroxylation reaction with three permanganate oxidants examined in our total synthesis of naturally occurring epipolythiodiketopiperazines (ETPs) is investigated with respect to the activity of the different oxidants, as well as the stereochemical outcome and the configurational stability of the product diols. An example of a subsequent thiolation was then demonstrated to proceed under retention of stereochemistry, in contrast to the stereoinvertive thiolations previously observed in several total syntheses. The data is supported by computational analyses. III. Progress Toward the Total Synthesis of (+)-Chetomin We describe our work toward the total synthesis of ETP natural product (+)-chetomin. Key features of the synthetic progress include a method for construction of the key nitrogen¬–¬carbon bond with advanced reaction partners, including protected diols, sulfides, and ETPs, and stereocontrolled thiolation strategies. The challenges remaining to access (+)-chetomin are addressed on model systems.

Investigation of Synthetic Proteins Produced via Automated Fast-Flow Peptide Synthesis

Thu, 01 Feb 2024 00:00:00 GMT

Investigation of Synthetic Proteins Produced via Automated Fast-Flow Peptide Synthesis Cowfer, Amanda Elizabeth Flow chemistry techniques and methods have given the broad scientific community high-fidelity access to chemical compounds with minimal effort compared to traditional synthetic techniques. Since the introduction of solid phase peptide synthesis (SPPS), the peptide community has endeavored to combine the convenience of flow chemistry with the iterative steps associated with peptide elongation in SPPS. Nearly one decade ago, members of the Pentelute lab envisioned and developed a flow-based peptide synthesizer, the Automated Fast-Flow Peptide Synthesizer, or AFPS for short. This technology enabled fast, reliable access to short peptide chains, with each coupling taking less than 3 minutes in total, significantly decreasing the labor needed to produce these peptides. However, peptide chains over 50 amino acids remained challenging to produce via AFPS, microwave synthesis, or traditional SPPS batch couplings. With modern research requiring rapid and high-fidelity access to long polypeptide chains, an immediate need to develop peptide synthesis technology to produce single-domain protein polypeptides in a single shot will be critical. Herein, I report on the arduous journey and unmatched teamwork needed to improve the AFPS systems for regular, reliable access to polypeptide chains of more than 200 amino acids in a single working day. In addition, I will highlight the workflow and knowledge needed to take a free polypeptide chain to a fully folded and biologically active protein, equivalent in form and function to its recombinant counterparts. I will discuss the iterative steps my team took to vary both chemical and mechanical and control variables to improve per-coupling yield enough to enable access to full-length single-domain proteins. On this journey, we utilized test peptides to validate synthesis quality and later synthesized a suite of full-length single-domain biologically active proteins. I will spend some time focusing on the barnase-barstar binding pair. Next, I will dive into how I build and design each AFPS synthesizer to improve synthesis outcomes and user-friendliness while retaining the core functionality and customizability that have made the AFPS so successful in the Pentelute lab. I will highlight my role in the renovation of the first generation AFPS system, the “Automatide,” and dive into the key characteristics that set our synthesizers apart from what is currently commercially available. Finally, we report on the synthesis and characterization of several small and very interesting luciferases. Luciferases are proteins that produce bioluminescence when exposed to specific chemical substrates, and for the organisms that produce these enzymes, they play a vital role in mating, defense, and camouflage. In the research arena, luciferases have had broad applications for decades, including detection of environmental contaminants, diagnosis of pathogens, high-throughput screening for drug discovery, understanding protein-protein interactions, and more. Current efforts in the field have focused on the development of small artificial luciferases due to their many advantages over traditional larger luciferases, such as enhanced stability and increased brightness. Herein, we report on the synthesis and characterization of the copepod, Gaussia priceps, luciferase GLuc (18 kDa), and artificial luciferases picALuc (12 kDa) and LuxSit-I (14 kDa). In addition, we synthesized the mirror-image counterpart of picALuc due to its potential for broad-reaching impact in health and diagnostics; this is the first reported mirror-image bioluminescent luciferase. Finally, we will report on our efforts to develop a split-picAluc protein complement assay (PCA) using AS-MS technology, which will be the smallest and most versatile split-luciferase reported to date. In summary, fast-flow peptide synthesis was utilized to produce and investigate several biologically relevant proteins to improve upon existing tools available to the broad chemistry and biology community.

Towards Efficient Planning for Navigation using Global Information in Large and Uncertain Environments

Sun, 01 Sep 2024 00:00:00 GMT

Towards Efficient Planning for Navigation using Global Information in Large and Uncertain Environments Kurtz, Martina Stadler We would like to enable a team of robots to navigate quickly and efficiently in large and uncertain outdoor environments. We hypothesize that in such environments, global, uncertainty-aware information is necessary to enable high-quality planning. However, most existing systems do not model or plan using global, uncertainty-aware information. For example, many planners assume access to complete global information in the form of full environment maps, or they assume that locally good planning decisions under uncertainty will result in globally good planning outcomes. To enable the use of global information for planning in large and uncertain environments, we must develop models that concisely represent key navigation features of the environment, and build planners that are capable of reasoning efficiently about global information. In this thesis, we design models and planners that use global information in large and uncertain environments to increase the efficiency and quality of planning for navigation. We present four contributions towards using global information for efficient navigation. First, we propose a high-level planning representation that can be learned from previous plans considered in the environment and used online during hierarchical, multi-query robot navigation. Second, we propose a planner for collaborative multiagent navigation in an uncertain environment; the approach uses macro-actions and value function approximations to maintain computational tractability. Third, we develop a robust hierarchical planning system to enable the deployment of the collaborative multiagent planner on a real-world team navigating in a structured, uncertain outdoor environment. Finally, we develop a method for learning uncertainty-aware, single agent value function-based approximations from graph data to increase the efficiency of the collaborative multiagent planner.

Design of Cleavable Monomers and Cross-Linkers for the Synthesis of Degradable Polymer Architectures

Wed, 01 May 2024 00:00:00 GMT

Design of Cleavable Monomers and Cross-Linkers for the Synthesis of Degradable Polymer Architectures Cardoso da Costa, Leticia Degradable materials, with different chemical compositions and various polymer architectures, are desirable for countless purposes, ranging from biological applications to recyclability of plastic waste. The creation of brand-new materials with useful, desired properties and built-in degradability is, however, very difficult. The introduction of labile bonds to already known polymers offers, thus, a much simpler approach for the manufacture of degradable materials. Here, we report the design of cleavable monomers and cross-linkers for the synthesis of degradable materials with different polymer architectures. The first half of this thesis focuses on the design of new degradable bottlebrush and brush-arm star polymers (BASPs) via ring-opening metathesis polymerization (ROMP). A brief introduction to the recent advances of bottlebrushes and related nanoarchitectures as a promising carrier platform is provided, followed by the current efforts to impart degradability within the nanoparticle in order to modulate its drug release and clearance rate (Chapter 1). After the introduction, we present the synthesis of boronic ester-crosslinked BASPs that selectively disassemble into bottlebrush fragments upon exposure to hydrogen peroxide, which is often elevated in diseased tissue microenvironments. The H2O2-induced disassembly of spirocyclohexyl nitroxide (chex)-containing BASPs induces a change in transverse magnetic relaxivity that can be detectable via magnetic resonance imaging (MRI) (Chapter 2). In the next chapter, we present the synthesis of backbone-degradable bottlebrush polymers via the co-polymerization of drug-loaded norbornenemacromonomers with a library of tailored silyl ether-based olefins via ring-opening metathesis polymerization (ROMP). The difference in backbone degradation rates, imparted by the silyl ether substituents, leads to different drug release profiles and therapeutic efficacy in vitro (Chapter 3). The second half of this thesis focuses on the introduction of degradable bonds into the polymer backbone of vinylic thermosets via radical ring-opening polymerization (rROP). A brief introduction to the current strategies utilized to impart chemical deconstruction to cross-linked polymer networks prepared by radical polymerization is presented (Chapter 4). Lastly, we improve the performance of a consumer good, gel nail polish, by imparting degradability via co-polymerization with a cleavable comonomer. Gel nail polishes, UV-curable (meth)acrylic coatings, display superior mechanical and adhesive properties compared to alternative nail polishes. These properties, however, come at the expense of ease-of-removal. Here, a cleavable bond is introduced into the resulting cured polymer networks via co-polymerization with a cleavable comonomer. This approach does not impact the material’s properties while enabling easy and fast removal under triggered deconstruction (Chapter 5).

Leveraging the Properties of Aprotic Solvents Towards Efficient Electrocatalytic Carbon Dioxide Reduction

Thu, 01 Feb 2024 00:00:00 GMT

Leveraging the Properties of Aprotic Solvents Towards Efficient Electrocatalytic Carbon Dioxide Reduction Chu, An T. Electrochemical carbon dioxide reduction has been studied as a method to sustainably produce valorized hydrocarbons. However, the reaction faces two challenges: low reaction selectivity towards value added products, and the deleterious reaction of carbon dioxide with the electrolyte to form soluble carbonate species. While both issues are sensitive to the composition of the electrolyte, the reaction has been exhaustively studied in aqueous electrolytes with limited opportunities for further extensive tunability. This thesis describes approaches for overcoming low reaction selectivity and electrolyte carbonation using aprotic-solvent based electrolytes. We leverage the unique solvation environments and equilibrium acidities accessible in such media to overcome key limitations to reaction performance that are intrinsically linked to the use of aqueous electrolytes. We demonstrate key principles for tuning aprotic solvent-based electrolytes towards improving carbon dioxide electroreduction catalysis, establishing the foundation for the development of advanced electrolyte designs. Chapter 1 details the development of a dimethyl sulfoxide / acetic acid electrolyte which can engender selective carbon dioxide reduction with minimal electrolyte carbonation on gold cathodes. We demonstrate that the key to engendering these balance of properties entails operating an electrolyte with a low water content, with simultaneous usage of a buffer which is non-nucleophilic and whose pKa is matched to the carbon dioxide / bicarbonate equilibrium. Under such conditions, the selectivity to carbon monoxide can be driven as high as 90% with only millimolar equilibrium bicarbonate formation: a compromise difficult to achieve in water. Chapter 2 details the discovery of a new mechanism for ethylene electrosynthesis on copper catalyst using dimethyl sulfoxide / phenol electrolyte. Starting from carbon monoxide—a crucial intermediate in the carbon dioxide reduction pathway—we present kinetic evidence that radically altering the solvent environment and proton donor can enable a mechanism involving quasi-equilibrium proton and electron transfer steps prior to a late rate-determining step. By demonstrating that the pathway in dimethyl sulfoxide / phenol has a potential-rate scaling and acid order distinct from those in aqueous electrolytes, we establish a new tunable platform for enabling selective electrocatalysis of hydrocarbon products.

Developments in THz Polaritonics: Towards Integrated Nonlinear THz Spectroscopy

Thu, 01 Feb 2024 00:00:00 GMT

Developments in THz Polaritonics: Towards Integrated Nonlinear THz Spectroscopy Sung, Eric Rueyhao The terahertz (THz) polaritonics platform is a compact, waveguide-based platform for the generation, manipulation, and detection of THz waves. The platform uses thin (<100 μm) lithium niobate (LiNbO₃, LN) and lithium tantalate (LiTaO₃, LT) slabs, which can be patterned to control THz propagation. One of the unique features of the platform is that the THz fields can be imaged directly within the slab with subwavelength spatial resolution and subcycle temporal resolution. Both the amplitude and phase of the fields are recorded, which allows the full spatiotemporal evolution of the fields to be visualized. This makes the platform appealing for compact, waveguide-based THz experiments. The work in the thesis aims to develop tools to enable robust, compact THz spectroscopy using the polaritonics platform. The first phase of my research aims to develop methods for enhanced THz generation in the waveguides. In a typical polaritonics experiment, the optical pump light is focused to a single line which launches THz fields with electric field strengths of approximately 10 kV/cm. Although the fields are sufficiently strong for THz imaging, any nonlinear spectroscopic applications would require the use of much larger THz fields so that the much weaker THz transients that result from multiple interactions with the sample could be reliably detected. To this end, I developed two methods. The first method uses thin LN waveguides with a beveled edge for enhanced narrowband THz generation. The optical pump light is focused onto the bevel, after which it refracts and becomes confined within the waveguide by total internal reflection. This allows the pump beam to repeatedly drive the generated THz field during its multiple back-and-forth traversals within the LN slab. Using this method, we observe a 10-fold enhancement of the THz spectral amplitude at the velocity-matched frequency. The second method combines the tilted pulse front geometry with THz focusing to generate a strong THz field in the time domain. A circular stair-step "echelon" mirror is used to shape the pump pulse into a conical tilted pulse front composed of a series of concentric rings of pump light. When the pump rings are imaged onto a thin LT waveguide, coherent superposition of the focusing THz fields excited individually by each pump ring results in a dramatically enhanced THz field at the focus. When optimized, the method generates THz fields with electric field strengths up to 175 kV/cm, which is roughly 20x larger than what is generated by a single line of pump light. The second phase of my research focuses on methods for expanding the polaritonics toolset for spectroscopic applications. Previous experiments coupling the THz phonon-polaritons in a LN waveguide to the quasi-antiferromagnetic magnon mode in an adjacent slab of ErFeO₃ took advantage of the fact that both materials have similar refractive indices. Furthermore, the ErFeO₃ layer complicates THz imaging because it strongly absorbs the optical probe light. I investigated two experimental geometries to address these concerns. The first geometry uses a high-reflecting coating sandwiched between the LN slab and the sample material. The coating is designed to reflect the optical probe light, which enables THz imaging in LN by preventing the probe light from entering the sample and greatly expands the range of possible samples. The second geometry uses a slot waveguide to localize the THz field within a low-index slot region, which results in much stronger interactions between the THz fields and a sample inserted into the slot. Using this geometry, the linear THz absorption spectrum of a test sample was measured with good sensitivity and the complex dielectric function was recovered. The work presented here describes methods for enabling robust integrated THz spectroscopy in the polaritonics platform. The methods, when combined, should also form the basis for future polaritonics experiments that interrogate the nonlinear THz responses of materials.

Exploring and Exploiting Ribonuclease 1: from Protein Biochemistry to Protein Engineering

Thu, 01 Feb 2024 00:00:00 GMT

Exploring and Exploiting Ribonuclease 1: from Protein Biochemistry to Protein Engineering Wralstad, Evans Christian Ribonuclease (RNase) 1 is a human protein with a remarkable ability to indiscriminately hydrolyze RNA. RNase 1 and its bovine homologue RNase A exhibit ubiquitous expression across tissues, a catalytic efficiency within the diffusion-limited regime, and minimal substrate sequence requirements. RNase A has been a favorite model protein of biochemists for over half a century; due to the high level of sequence conservation between RNase A and RNase 1, many observations made for RNase A have corollaries for RNase 1. RNase 1 and RNase A are members of the pancreatic-type ribonuclease (ptRNase) superfamily, a class of enzymes which share many biophysical features, including a small molecular weight, high cationicity, and a secretory nature. Historical elucidation of ribonuclease biochemistry describes their susceptibility to oxidation-induced inactivation. This raises the question: how are these secretory enzymes able to preserve catalytic competency in oxidatively challenging extracellular environments such as blood serum and even epidermal skin? In Chapter 2 of this thesis, the intrinsic antioxidative capacity of RNase 1 is described. Chemical biology and biomimetic techniques corroboratively implicate two methionine residues as sacrificial antioxidants to protect the enzymic active site, allowing catalysis to persist in the presence of reactive oxygen species. In silico studies suggest evolutionary patterns to install these antioxidative features across the ptRNase superfamily. Sulfur–arene interactions appear to tune the reactivity of methionine residues in a manner consistent with rates of oxidation. These findings highlight an underappreciated role for methionine—to protect catalytic histidine residues—and indicate a means by which ptRNases remain functional in oxidatively challenging physiological environments. The desirable biophysical features of RNase 1 and the wealth of biochemical knowledge regarding it have also made it a favored model system of protein engineers, as exemplified by RNase S and cyclic RNase-based zymogens, two systems which reversibly attenuate ribonucleolytic activity. In particular, RNase-based zymogens can be activated by exogenous proteases; this schema has biotherapeutic potential, as demonstrated by zymogens which activate in response to viral infection and exert cytotoxic ribonucleolytic activity. Efforts to establish a zymogen directed toward the coronavirus SARS-CoV-2 are described in two parts of this thesis. In Chapter 3, the main protease 3CLpro of SARS-CoV-2 is enzymologically characterized. This work clarifies reported inconsistencies in enzymological features of this key viral protease and relies on a non-Michaelis–Menten, Bayesian inference-based analytical technique to circumvent some of the causes of the inconsistent prior reports. Then, in Chapter 4, the newfound knowledge of 3CLpro enzymology is applied toward the design of an RNase 1-based, 3CL superscript pro - directed zymogen. The zymogen is inactivated by steric occlusion and conformational distortion of the active site, and site-specific activation by 3CL superscript pro results in a multi-order of magnitude increase in ribonucleolytic activity. 3CL superscript pro action upon the zymogen leads to ribonucleolytic turnover of a fluorescent RNA substrate by the activated species, affording signal amplification that enables detection of nanomolar 3CLpro concentrations in a timeframe comparable to rapid antigen detection testing.

Market-Based and Policy-Based Conditional Demand Forecaster for Airline Revenue Management

Thu, 01 Jun 2023 00:00:00 GMT

Market-Based and Policy-Based Conditional Demand Forecaster for Airline Revenue Management Lu, Yuxuan The price transparency brought by the proliferation of online travel agencies and the loosening of fare class restrictions increases the importance of competitive pricing in the practice of airline revenue management. In this dissertation, we propose a novel forecasting framework, market-based and policy-based conditional demand forecaster (MPCF) to provide airline revenue management systems (RMSs) with dynamically adjusted sell-up probabilities and fare class demand forecasts based on estimated total market demand (market-based) and predicted fare class availabilities of competitors (policy-based) for a future flight departure. In the MPCF framework, an airline estimates the total market demand for travel on a future departure day and predicts its competing airline’s future fare class availabilities. The estimation of the total market demand allows the forecasting airline to anticipate additional demand when it offers lower fare quotes than its competitors and vice versa; the prediction of a competitor’s policy enables the revision of expected passenger sell-up probabilities to higher fares, conditioned on competitive influences and assumed passenger choice behaviors. In simulation tests, we assumed a Bertrand-Edgeworth passenger choice model, corresponding to a fully undifferentiated fare environment. With an assumption of perfect knowledge of total market demand that has already arrived for a future departure date, MPCF gains 12.69% in revenue compared with the existing Q-forecasting in an isolated market. Without that perfect knowledge, which requires estimation of total market demand, MPCF still gained 6.23% of revenue. MPCF leads to higher revenue gains on departures with demand levels that are different from the mean historical demand, demonstrating its benefits in providing dynamic sell-up and forecast guidance according to the predicted policies of competitors. The simulation results confirm the benefits of constructing demand forecasts on predicted market demand and competitors’ policies.

Accelerating Practical Engineering Design Optimization with Computational Graph Transformations

Sun, 01 Sep 2024 00:00:00 GMT

Accelerating Practical Engineering Design Optimization with Computational Graph Transformations Sharpe, Peter D. Multidisciplinary design optimization has immense potential to improve conceptual design workflows for large-scale engineered systems, such as aircraft. However, despite remarkable theoretical progress in advanced optimization methods in recent decades, practical industry adoption of such methods lags far behind. This thesis identifies the root causes of this theory-to-practice gap and addresses them by introducing a new paradigm for computational design optimization frameworks called code transformations. Code transformations encompass a variety of computational-graph-based scientific computing strategies (e.g., automatic differentiation, automatic sparsity detection, problem auto-scaling) that automatically analyze, augment, and accelerate the user’s code before passing it to a modern gradient-based optimization algorithm. This paradigm offers a compelling combination of ease-of-use, computational speed, and modeling flexibility, whereas existing paradigms typically make sacrifices in at least one of these key areas. Consequently, code transformations present a competitive avenue for increasing the adoption of advanced optimization techniques in industry, all without placing the burden of deep expertise in applied mathematics and computer science on end users. The major contributions of this thesis are fivefold. First, it introduces the concept of code transformations as a possible foundation for an MDO framework and demonstrates their practical feasibility through aircraft design case studies. Second, it implements several common aircraft analyses in a form compatible with code transformations, providing a practical illustration of the opportunities, challenges, and considerations here. Third, it presents a novel technique to automatically trace sparsity through certain external black-box functions by exploiting IEEE 754 handling of not-a-number (NaN) values. Fourth, it proposes strategies for efficiently incorporating black-box models into a code transformation framework through physics-informed machine learning surrogates, demonstrated with an airfoil aerodynamics analysis case study. Finally, it shows how a code transformations paradigm can simplify the formulation of other optimization-related aircraft development tasks beyond just design, exemplified by aircraft system identification and performance reconstruction from minimal flight data. Taken holistically, these contributions aim to improve the accessibility of advanced optimization techniques for industry engineers, making large-scale conceptual multidisciplinary design optimization more practical for real-world systems.

An Integrated Vehicle, Payload, and Trajectory Optimization Framework for Highly-Coupled Aircraft Systems

Sun, 01 Sep 2024 00:00:00 GMT

An Integrated Vehicle, Payload, and Trajectory Optimization Framework for Highly-Coupled Aircraft Systems Dewald, Annick J. A class of highly-coupled aircraft systems is identified in Earth observation applications, where the aircraft design couples tightly with the science instrument design and the operation of both the aircraft and science payload. This dissertation identifies an opportunity to simultaneously optimize the aircraft platform, the science payload, and the operational strategy under one system-level objective function to improve the performance of the total aircraft system. This approach extends the field of MDO which demonstrates that simultaneously optimizing all the subsystems within a larger system allows the optimizer to leverage the couplings between disciplines, rather than be subject to them, resulting in better performance outcomes [1]. The inclusion of the instrument and trajectory into the optimization problem introduces additional objectives related to the science mission needs. While many methods for multi-objective optimization exist in the field, these methods are not tractable with the many objectives within these complex systems. A methodology is proposed to explore trade-offs between multiple objectives by sweeping through different combinations of weighting terms in a weighted-sum objective function to find Pareto optimal design points across the design space. These design points are then evaluated within the objective space, a hyperspace where each axis corresponds to a different objective, to understand the performance capabilities with respect to each objective and evaluate trade-offs between objectives. Findings from this objective space exploration can then be communicated to the science stakeholder to find the design that is best capable of meeting the identified science mission needs. This dissertation then applies this methodology to a series of case studies on a representative science mission. The science mission objective of these case studies is to reduce uncertainty in predictions of sea-level rise by understanding ice mechanics that drive ice shelf collapse and destabilize previously grounded glaciers.

Opportunities and Limitations of Earth Observation Technology for Environmental Justice Advocacy: A Case Study of Toxic Prisons in the U.S.

Sun, 01 Sep 2024 00:00:00 GMT

Opportunities and Limitations of Earth Observation Technology for Environmental Justice Advocacy: A Case Study of Toxic Prisons in the U.S. Ovienmhada, Ufuoma People of color and other socio-economically marginalized groups in the United States experience a disproportionate burden of environmental challenges such as air pollution and extreme heat; the Environmental Justice (EJ) movement aims to combat these burdens and promote collective well being. Earth Observation (EO) technology, such as satellites, can be used to monitor air quality, extreme heat, and other quantities relevant to EJ. However the application of this technology in measuring EJ, or supporting EJ advocacy efforts has not been widely explored. Satellite EO systems also historically have not been designed with EJ end users in mind. This application is increasingly more pressing as space agencies like NASA are seeking information on how their data can be used to support underserved communities. This dissertation brings together EO data science, systems engineering, and community- engagement to elucidate opportunities and limitations of Earth Observation Technology for Environmental Justice Advocacy. The dissertation is organized into three categories of contributions – Description, Evaluation, and Design/Prescription – that are each composed of multiple research efforts. In Description, I apply a three-pronged approach to provide insights on the opportunities and limitations of EO data for EJ. First, along with a team of researchers, I assess peer- reviewed literature on satellite data for environmental justice through a scoping review. The second contribution of this chapter is an interview study with a subset of grassroots EJ actors about how they can use EO data in their domain of EJ activism which contests the exposure of prisons and incarcerated people to environmental hazards. The third contribution of this chapter is a system’s engineering architectural description of NASA’s current satellite EO for EJ ecosystem. Using justice theory as an analytical framework, I reveal limitations of NASA’s current EO for EJ architecture for advancing holistic notions of EJ. In Evaluation, with support from co-authors, I measure spatiotemporal patterns of air pollution burden, and air and land surface temperature extremes in prison landscapes across the U.S. These studies contribute to a nascent literature documenting empirical evidence of environmental hazards in carceral landscapes. It also extends the literature on applications of satellite-derived and modeled geospatial data for EJ. In Design/Prescription, first, supported by 3 years of community engagement with prison EJ activists, I present the Design of a GIS decision support system that features EO data responding to expressed needs of prison EJ activists. Then, I present two essays that Prescribe recommendations for methodological innovations in the design and application of EO technologies and geospatial data for EJ advocacy. Together, these three chapters demonstrate the immediate relevance of EO and geospatial technologies for prison EJ advocacy, and broader implications for the EO community interested in supporting the aims of the EJ movement more holistically.

Interplay of Transition Metals and Noncovalent Interactions in C–H Activation Catalysis

Thu, 01 Feb 2024 00:00:00 GMT

Interplay of Transition Metals and Noncovalent Interactions in C–H Activation Catalysis Vennelakanti, Vyshnavi Selective C–H activation is crucial for the synthesis of bioactive molecules and natural products, and plays an important role in pharmaceutical industry, medicinal chemistry, and the materials industry. While synthetic routes to activate unreactive C–H bonds require harsh conditions and usually show poor selectivity, biological systems, such as non-heme iron enzymes, carry out selective C–H activation efficiently under ambient conditions. These enzymes catalyze a variety of reactions including C–H halogenation, hydroxylation, epoxidation, and ring closures, several of which are mediated with the help of noncovalent interactions such as hydrogen bonds (HBs). Most reactions share a common catalytic pathway with the formation of a reactive ferryl intermediate which is hard to be characterized experimentally. Computational studies of these enzymes help to bridge the gap in experiments towards understanding enzyme mechanism and selectivity. In this thesis, we study the interplay of noncovalent interactions and transition metals in C–H activation catalysis using quantum mechanical simulations. We employ density functional theory (DFT) and wavefunction theory to perform an extensive computational study of protein HB interactions and transition metal complex (TMC) active sites in non-heme iron halogenases and hydroxylases. Due to the fleeting nature of the ferryl intermediate, experimentalists tend to use vanadyl mimics in order to better understand the ferryl intermediate. However, these metals exhibit distinct electronic structure, motivating us to investigate if vanadyl mimics are indeed faithful to the native ferryl intermediates. Studying the mechanism of metalloenzymes using first principles methods could be challenging due to the larger system sizes. Thus, we also try to understand C–H activation carried out by 3d TMCs focusing on the specific case of partial oxidation of methane to methanol. While the oxidation and spin states of the metals in the enzyme active site are well defined through spectroscopic methods, that is not the case with TMC catalysts. Thus, modeling TMC catalysts is accompanied by the twin challenges of identifying the ground spin state and determining the appropriate method to identify the ground state since properties such as reaction energies and scaling relations are sensitive to the computational method used. Additionally, the ability of TMCs to exist in multiple spin states is often leveraged for practical applications, with one such example being spin crossover (SCO) complexes that exhibit a change in spin state as a function of external stimulus like temperature and are widely studied due to their increasing use in molecular switches. We curate an experimental data set of 95 Fe(II) SCO complexes and predict SCO behavior using DFT with the aim of identifying the best performing functional. This in turn sets the stage to design SCO complexes with tailed properties such as those that exhibit SCO behavior at room temperature. We expect that the insights from this work can directly guide efforts on biomimetic chemistry as well as both biological and synthetic C–H activation catalysis.

Wearable Gut and Brain Interfaces for Valence Detection and Modulation

Sun, 01 Sep 2024 00:00:00 GMT

Wearable Gut and Brain Interfaces for Valence Detection and Modulation Vujic, Angela Emotion detection interfaces have shown promise in mediating our emotional health through improved diagnosis, self-tracking, social support systems, mindfulness, and biofeedback training; however, the most popular methods falter when distinguishing between positive and negative valence - or lead to privacy or social issues. Brain and gut interfaces can serve as an alternative, but often require complex setups with many electrodes, large datasets, and the usage of significant training to achieve benchmark emotion detection performance. I present novel, wearable gut- and brain-interfaces for valence detection and modulation that can be made feasible with as few as two electrodes, minimal training and statistical analysis. I coin and define the area of gut-brain computer interfacing (GBCI), while further developing the field of affective brain-computer interfacing (aBCI). I take a novel approach by using the stomach signal and motivational direction models as an alternative to traditional affective modalities and models. I present Joie, a joy-based electroencephalography (EEG) brain-computer interface (BCI); JoyNet, a neural network for joy detection with EEG; and KALM, an EEG, electrodermal activity (EDA) and respiration rate multimodal fusion model. I also present Serosa, an novel electrogastrography (EGG) GBCI which non-invasively records indices of gastric neurons that can be correlated with emotional states and provide a new affect detection modality. This thesis presents findings and innovations in research and application: first, offline affect detection models which contextualize neural with embodied modalities and evaluate how each signal influences affect detection performance. Second, novel real-time interfaces are implemented and evaluated with placebo-controlled laboratory studies. Third, I present a novel neuroethics discussion which uses socioecological models to anticipate harms and I reflect on the works in this thesis.

Secure Computation in Decentralized Systems

Sun, 01 Sep 2024 00:00:00 GMT

Secure Computation in Decentralized Systems Zyskind, Guy Decentralized systems like Bitcoin and Ethereum are real-world examples of secure distributed systems deployed at scale. Over the past decade, these systems and others have proven to provide a trust-minimized solution for computing. They ensure the correct execution of code (correctness), maintain the integrity of stored data, and remain consistently available (availability). Additionally, they allow any user to interact without the risk of censorship. However, while decentralized systems guarantee security properties like integrity, correctness, and availability, they do not provide privacy. In this regard, they are strictly worse than assuming full trust in a centralized server, since any node in the network must see all data. Furthermore, in many of these open systems (also known as 'permissionless' networks), there are no restrictions on who can operate a node. This means that decentralized systems, and public blockchains in particular, cannot operate on private data, greatly limiting the kinds of use-cases they can support. This dissertation explores solutions to mitigate the privacy concerns associated with modern decentralized systems, focusing particularly on blockchains. The research employs Secure Multiparty Computation (MPC) techniques to address these issues, demonstrating how MPC, which already shares a similar distributed trust threat model, can enhance privacy in decentralized systems. More specifically, this thesis focuses on the following key areas in decentralized systems: Access Control Mechanisms and Confidential Smart Contracts: The thesis begins by exploring access control mechanisms on blockchains, and from that builds up to the concept of confidential smart contracts -- arbitrary programs that execute both correctly and privately. Identity Management and Authentication: Building on access control and confidential smart contracts, we examine identity management and authentication within decentralized networks. We develop a highly efficient Threshold ECDSA protocol that runs in the server-aided MPC model. Perhaps more importantly, we revisit the server-aided MPC model itself, which sits somewhere between the dishonest and honest-majority MPC paradigms, and show that a confidential smart contract is a real-world realization of the server in this model. We thus theorize that dishonest MPC protocols in general can be practically improved under this model, and argue that because there is a real-world counterpart, this model is realistic. An Improved Distributed Point Function (DPF) and ORAM: A major theoretical contribution of this work is a novel three-party Distributed Point Function (DPF) construction. This leads to state-of-the-art Oblivious RAM (ORAM) and Distributed ORAM (DORAM) protocols, which are important building blocks in MPC. Privacy-Preserving Digital Currencies: Using this DPF construction, we revisit the problem of privacy-preserving digital currencies, proposing a solution in the account model. This approach challenges the current consensus that privacy in blockchains requires a UTXO model. Secure Inference with private retrieval: Lastly, the thesis explores how Large Language Models (LLMs) can perform secure inference while retrieving data from private, distributed databases. This method represents a step towards building secure decentralized AI systems that respect user privacy.

Cyborg Psychology: The Art & Science of Designing Human-AI Systems that Support Human Flourishing

Sun, 01 Sep 2024 00:00:00 GMT

Cyborg Psychology: The Art & Science of Designing Human-AI Systems that Support Human Flourishing Pataranutaporn, Pat As Artificial Intelligence (AI) becomes increasingly integrated into our daily lives, understanding the psychological implications of human-AI interaction is crucial for developing systems that truly support human capabilities. This dissertation introduces “Cyborg Psychology,” an interdisciplinary, human-centered approach to understanding how AI systems influence human psychological processes. Cyborg Psychology also emphasizes applying these insights to design and develop AI systems that support human flourishing. Cyborg Psychology recognizes the complex, non-linear interactions between humans and AI, acknowledging that both can influence and shape each other in dynamic and often unpredictable ways. Informed by human-computer interaction, psychology, and behavioral sciences, this dissertation focuses on understanding AI’s impact on crucial cognitive and behavioral processes, including motivation, critical thinking, self-reflection, confidence, beliefs, biases, and more. In addition, the work presents several AI systems that apply psychological insights to support human cognition and behavior. For example, the “Wearable Reasoner” seeks to enhance human rationality, “Personalized Virtual Characters” aims to support learning motivation, and “Future You” is designed to encourage long-term oriented thinking and behavior. Employing a diverse array of research methodologies, this work proposes a framework for investigating the implications of interaction design choices. The ultimate goal is to empower the development of AI systems that foster human flourishing by nurturing intellectual growth, cultivating motivation, stimulating critical thinking, and preserving individual autonomy in decision-making.

Mechatronic Design and Evaluation of a Two-Degree-of-Freedom Powered Ankle-Foot Prosthesis with Myoneural Interfacing Capabilities

Sun, 01 Sep 2024 00:00:00 GMT

Mechatronic Design and Evaluation of a Two-Degree-of-Freedom Powered Ankle-Foot Prosthesis with Myoneural Interfacing Capabilities Hsieh, Tsung-Han Recent advancements in neural interfaces and sensing technologies have opened new possibilities for enhanced prosthesis control. The agonist-antagonist myoneural interface (AMI) connects residual muscle pairs to emulate natural dynamics, while electronic osseointegrated prostheses for the rehabilitation of amputees (eOPRA) allow direct measurement of neural signals through implants. Additionally, magnetomicrometry enables precise, real-time measurement of muscle length. These innovations motivate the development of more sophisticated prosthetic designs, including two degrees of freedom (2DoF) ankle systems. This Ph.D. thesis advanced bionic limb technology through three primary aims. First, a comprehensive characterization study of human-scale actuators was conducted, including brushless motors of different sizes. Using a custom-built dynamometer, the performance of these actuators was evaluated across their full operating range. Building upon this foundation, an innovative bionic ankle-foot prosthesis with enhanced capabilities was designed and fabricated. This advanced prosthetic system achieved biological fidelity in terms of range of motion, torque output, and angular velocity, thus enabling more natural and adaptable gait patterns. To validate the efficacy of the system, a subject with AMI constructs was fitted with the prosthesis and underwent a series of locomotion tasks, including level-ground ambulation and obstacle traversal. This work pushed the boundaries of bionic limb function and advanced the restoration of natural locomotion after lower limb amputation, providing valuable insights into the potential of combining advanced prosthetic design with neural interfacing techniques.

Quantifying the effects of sunlight on the fate of oil spilled at sea

Sun, 01 Sep 2024 00:00:00 GMT

Quantifying the effects of sunlight on the fate of oil spilled at sea Freeman, Danielle Haas Oil spilled at sea is transformed by sunlight-driven photochemical reactions. The transformed oil has different properties and behavior in the environment compared to the fresh oil, resulting in different fates and effects. My work in this thesis was to put numbers on these changes, with the goal of better predicting where oil goes and how it behaves in diverse spill scenarios. First, I focused on how sunlight generates water-soluble compounds from oil, which can lead to the dissolution of oil-derived compounds in seawater (photo-dissolution; Chapter 2). To find out whether photo-dissolution could be an important fate process during an oil spill, I used a combination of experiments and photochemical rate modeling to calculate photo-dissolution rates for the 2010 Deepwater Horizon spill (DwH) in the Gulf of Mexico (GoM). I found that photo-dissolution likely converted ~8% of the floating surface oil to dissolved organic carbon during DwH, a fraction similar in magnitude to other well-recognized fate processes. Moving beyond DwH, I evaluated the sensitivity of oil photo-dissolution and photochemically-altered oil physical properties to temperature. I found that if a spill like DwH had occurred in 5°C water rather than the exceptionally warm 30°C water of the GoM, 7x less oil could have dissolved via photo-dissolution and the viscosity of the remaining insoluble oil could have been 16x higher, resulting in lower entrainment of oil into the water column as small droplets (Chapter 3). The net result is that more oil would stay at the sea surface in a cold-water spill. Finally, I determined photo-dissolution rates for diverse oil products beyond the light crude that spilled during DwH (Chapter 4). I found that oil photo-reactivity could be predicted from oil chemical composition. I also found that photo-dissolution likely affects oil mass balance in spills of light oils forming thin slicks but not in spills of light or heavy oils forming thick slicks. Overall, this work advances our understanding of how oil changes in the environment upon sunlight exposure. This information can be applied to better predict, evaluate, and mitigate the effects of oil spilled at sea on marine ecosystems, including humans.

Advancements in Management Science: Applications to Online Retail, Healthcare, and Non-Profit Fundraising

Sun, 01 Sep 2024 00:00:00 GMT

Advancements in Management Science: Applications to Online Retail, Healthcare, and Non-Profit Fundraising Zhai, Chen Wen (Sabrina) Management science is an evolving-field that requires novel models and algorithms, combining methods from statistics, optimization, and machine learning. This thesis presents advancements in management science across three domains: revenue management, healthcare, and non-profit funding platforms. The chapters in this thesis develop rigorous algorithms and techniques which are relevant in practice, and present data-driven insights into each of the application areas. Chapter 2 studies a personalized dynamic pricing problem commonly faced by online retailers. Customers arrive sequentially to the selling platform, and for each arrival the seller must make an immediate pricing decision for that customer. The seller aims to learn the demand as a function of price and customer covariates through price experimentation, while simultaneously earning as much total revenue as possible. Previous work on this topic have adopted a classical online learning setup, where the retailer begins the selling horizon with no information about the problem and gains all knowledge about the demand function from the online selling phase. However, this assumption is often not true in practice. Many retailers already possess some information about their product's demand from market research or previous sales data, and not utilizing this information is clearly suboptimal. The chapter develops a novel framework that allows the seller to incorporate historical data on pricing decisions and realized demand, and moreover enables one to study the effect that certain characteristics of this historical dataset have on online selling performance. Using this framework, a dynamic pricing algorithm is proposed which effectively uses both historical and real time data, and achieves provably optimal performance. Furthermore, a new distance measure is developed to quantify how close the historical pricing decisions are to being optimal. Using this distance measure, the chapter shows a surprising inverse relationship between this measure and the achievable online performance. Chapter 3 focuses on applying causal inference techniques to study the treatment efficacy of different antibiotics on patients with urinary tract infection. Up to 50% of women will experience a urinary tract infection (UTI) in their lifetime, making it the third most common indication for antibiotic treatment in the United States. Though national treatment guidelines encourage using one of three antibiotics as the first-line treatment, other second-line and alternative antibiotics are still commonly prescribed in practice. Studies on the efficacy of first-line versus second-line and alternative antibiotics for UTI are limited and dated. The chapter presents a retrospective cohort study using the claims database from Independence Blue Cross to determine the relative efficacy and adverse event rates between different categories of antibiotics. By combining causal inference techniques with automated feature extraction using the Observational Medical Outcomes Partnership (OMOP) common data model, evidence is found which supports the use of guideline-recommended first-line treatments for uncomplicated UTI. Specifically, the rate of treatment efficacy is higher for first-line antibiotics relative to alternatives. Surprisingly, the analysis also finds evidence which supports increased efficacy of first line agents relative to second-line antibiotics, which are of broader spectrum, albeit the effect difference is smaller compared to the comparison between first-line antibiotics and alternatives. This large-scale cohort study which includes a comprehensive collection of covariates provides much-needed evidence to support the continued recommendation of first-line drugs for the treatment of UTI. The chapter also suggests the feasibility for performing complex causal inference analyses using automated feature engineering packages for OMOP-formatted datasets. Chapter 4 studies an online matching problem where sequentially arriving donors must be matched to projects needing funding on peer-to-peer philanthropic crowdfunding platforms such as DonorsChoose.org. Empirical studies have shown that (i) donors have heterogeneous preferences over the projects, and (ii) many return to make more than one donation. Facing such donors, the platform’s aim is to match each donor to one of their preferred projects so as to maximize the total donation without over-funding any projects and without knowing the arrival pattern. Previous work in the literature have not studied the effect of returning donors on algorithm performance. The chapter shows an upper bound on the best achievable worst-case performance of any online algorithm which reveals the relationship between donor return rate and algorithm performance. Furthermore, numerical analysis shows that a simple known algorithm achieves a performance that improves with the number of returning donors without differentiating between the original and return donors. The algorithm is intuitive and straightforward to implement, and the results shed light on the practical value that returning traffic can bring for fundraising platforms.

Laboratory studies of atmospheric photochemistry in indoor and outdoor environments

Sun, 01 Sep 2024 00:00:00 GMT

Laboratory studies of atmospheric photochemistry in indoor and outdoor environments Goss, Matthew B. Secondary organic aerosol (SOA), fine particulate matter formed through indirect photochemical reactions, influences the climate and contributes to air pollution harmful to human health. While these two effects act at different scales, they are governed by similar chemical processes. This work investigates the atmospheric photochemistry of indoor and outdoor environments, giving particular attention to the reactions that lead to SOA formation, notably those involving oxidant and peroxy radical (RO2) chemistry. First, this thesis examines the oxidation of dimethyl sulfide (DMS), which represents a large natural source of sulfur to the atmosphere and affects the climate. Using varied chemical conditions across numerous environmental chamber experiments, we characterize aerosol formation from the oxidation of DMS, as well as two related compounds, dimethyl sulfoxide and dimethyl disulfide. We also measure key rate constants crucial to understanding the formation and fate of hydroperoxymethyl thioformate, an important recently-discovered DMS product. Second, this work investigates the indoor air quality implications of 222 nm germicidal ultraviolet lamps (GUV222). While these lamps are effective at reducing the spread of airborne pathogens, they lead to the formation of ozone (O3), a harmful air pollutant. Through environmental chamber experiments, we quantify the GUV222-driven production of O3, OH, oxidized products, and SOA, and further demonstrate that GUV222 causes new particle formation. Based on these results, we recommend that GUV222 lights be operated at their lowest effective level. Finally, we pivot to examine assumptions embedded within the relationship between chamber experiments and SOA parameterizations in global chemical transport models. We represent historical laboratory experiments in a box model, enabling explicit estimates of the unmeasured RO2 and oxidant chemistry that influences aerosol formation. This work shows that reaction conditions are dynamic, changing within and between experiments, and demonstrates that RO2 isomerization is implicitly built into SOA parameterizations, even without its explicit representation. Overall, this thesis connects multiple areas of indoor and outdoor atmospheric photochemistry, improving our understanding of marine organosulfur chemistry, the impacts of GUV222 lamps, and the relationship between laboratory chamber measurements and the modeling of aerosol on a global scale.

New Purification Strategies and Monomer Platforms for Ruthenium-Initiated Living Ring-Opening Metathesis Polymerization

Wed, 01 May 2024 00:00:00 GMT

New Purification Strategies and Monomer Platforms for Ruthenium-Initiated Living Ring-Opening Metathesis Polymerization Kilgallon, Landon J. Chapter 1: Introduction to Covalent Capture Purification and Ring-Opening Metathesis Polymerization In chemical spaces where difficult purifications are commonplace, innovative purification methodologies have been developed to circumvent the limitations associated with classical physiochemical property-driven purifications (chromatography, crystallization, distillation, etc.). Covalent capture purification—a type of catch-and-release purification—purifies molecules by selectively capturing them (via a covalent bond) onto a solid support, washing away impurities, and cleaving the product from the support for recovery. In the first half of this chapter, we review literature examples where covalent capture has been implemented for the purification of chemically synthesized molecules, including synthetic peptides, oligonucleotides, oligosaccharides, and small molecules. Ruthenium-initiated ring-opening metathesis polymerization (ROMP) remains an extraordinary tool for polymer synthesis due to its functional group tolerance, the ready availability of monomers and initiators, and the overall ease at which well-defined polymers can be rapidly synthesized. However, complete removal of ruthenium residues from the product is a difficult task that is compounded by the lack of understanding of initiator decomposition in ROMP. The existing methods for purification of ROMP polymers, which are typically solubility-based, are reviewed. The promise of covalent capture purification—a reactivity-based purification method— for living ROMP is discussed. Chapter 2: Covalent Capture Purification for Living Ring-Opening Metathesis Polymerization Covalent capture purification, a type of catch-and-release purification, facilitates complex molecule purification by partitioning reaction mixtures based on chemical reactivity rather than physiochemical properties. While this purification methodology has proven highly valuable for the purification of synthetic peptides, oligonucleotides, and oligosaccharides, it has not yet been implemented for the purification of synthetic polymers. Ruthenium-initiated living ROMP remains an extraordinary tool for polymer synthesis, but removal of trace ruthenium from the polymeric product remains a difficult task due to the wide scope of polymer compositions, the lack of a complete understanding of initiator decomposition, and the unknown identities of trace ruthenium products generated during ROMP. In this work, we translate covalent capture purification to living ROMP for the first time, and demonstrate its use as a general purification method for ROMP polymers. The optimized covalent capture system was used to purify a variety of linear polynorbornenes (up to ~7 kDa) in yields ≥49% and high purities (≥99.6% ruthenium removed). Chapter 3: Tricyclononenes and Tricyclononadienes as Efficient Monomers for ROMP: Understanding Structure–Propagation Rate Relationships and Enabling Facile Post-Polymerization Modification Tricyclononenes (TCN) and tricyclononadienes (TCND) represent under-explored classes of monomers for ROMP that have the potential to both advance fundamental knowledge (structure-polymerization kinetics relationships) and serve as practical tools for the polymer chemist (post-polymerization functionalization). In this work, a library of TCN and TCND imides, monoesters, and diesters, along with their exo-norbornene counterparts, were synthesized to compare their behavior in ruthenium-initiated ROMP. To understand the relationship between monomer structure and ROMP propagation rate, density functional theory methods were used to calculate a variety of electronic and steric parameters for the monomers. While electronic parameters (e.g., HOMO energy levels) correlated positively with the measured kp values, steric parameters generally gave improved correlations, which indicates that monomer size and shape are better predictors for kp than electronic parameters for this data set. Furthermore, the TCND diester— which contains an electron-deficient cyclobutene that is resistant to ROMP—and its polymer p(TCND) are shown to be highly reactive toward base-catalyzed conjugate addition with thiols, providing a protecting/activating-group free strategy for post-polymerization modification. Chapter 4: Safe and Scalable Syntheses of N,N-Dimethyltrifluoromethanesulfonamide (DMTMSA) and Other Trifluoromethanesulfonamide Solvents for High Energy Density Battery Applications A simple, scalable synthetic methodology for the synthesis of N,N-dimethyltrifluoromethanesulfonamide (DMTMSA) and other trifluoromethanesulfonamide solvents is described. No specialized glassware is required, water is the solvent, and an ice bath is used for cooling. Up to 155 g of DMTMSA is synthesized in a single batch in 92% yield. The optimized process is highly mass efficient (PMI = 9.1), and excess dimethylamine may be recovered (93% recovery, 51% decrease in waste) and recycled via a simple short-path distillation.

Development of quorum-sensing circuits for metabolic flux control in Escherichia coli

Sat, 01 Feb 2020 00:00:00 GMT

Development of quorum-sensing circuits for metabolic flux control in Escherichia coli Dinh, Christina V. Metabolic engineering seeks to reprogram microbial cells to efficiently produce value-added chemicals. Traditionally, this is achieved by overexpressing the production pathway and/or knocking out competing endogenous pathways. However, limitations in some pathways are more effectively addressed through dynamic metabolic flux control to favor different objectives over the course of the fermentation. This thesis aims to develop autonomous and pathway-independent regulation tools that can be applied to controlling metabolic fluxes in these contexts to improve production. To this end, quorum-sensing (QS)-based circuits were constructed, characterized, and applied to regulating metabolic fluxes in a cell-density-dependent manner. The first tool is a bifunctional QS circuit in which each control module regulates transcription under circuits derived from different QS systems. Characterization showed that the switching dynamics of both circuits can be tuned by varying the expression level of circuit components. To address major limitations in the naringenin and salicylic acid pathways, one module was used to delay transcription of key heterologous genes to overcome enzyme inhibition and growth burden while the second module controlled expression of CRISPRi components to silence competing endogenous pathways. Application of these regulation schemes resulted in significant production improvements in both pathways. Especially when aiming to dynamically down-regulate enzyme activity, post-translational control can offer faster response dynamics. To develop a post-translational control tool, expression of a protease linker was regulated under a QS circuit, resulting in selective degradation of tagged enzymes. This circuit was applied to regulating phosphofructokinase (Pfk:) levels with the ultimate goal of dynamic composition control in co-culture fermentations. Application of this control circuit in a naringenin-producing co-culture system resulted in improved composition profiles, which benefited production. Finally, a second post-translational control system that co-localizes proteins in response to cell-density changes was constructed and characterized. Such a system can be applied to actuate changes in reaction rates with minimal dependence on host cell machinery. Overall, this work developed QS-based circuits and showed they can be powerful tools for addressing key limitations in microbial syntheses.

Multifidelity Methods for Design of Transition MetalComplexes

Sat, 01 Feb 2020 00:00:00 GMT

Multifidelity Methods for Design of Transition MetalComplexes Janet, Jon Paul The rational design of materials with tightly controlled properties is crucial to addressing future challenges in energy, electronics and catalysis. While improvements in computing power have made simulation with density functional theory (DFT) an essential tool in screening new materials, it remains too costly to address truly high-dimensional design spaces. This problem is especially acute for open-shell transition metal (TM) complexes, which are of central importance in homogeneous catalysis and have applications in solar energy and electronics. The space of TM complexes is enormous and poorly characterized, while DFT calculations for these systems are expensive and sensitive to method choice, making it impractical to simulate large numbers of candidates indiscriminately. This makes the search for TM complexes with desired properties a formidable challenge. This thesis addresses this challenge by formulating strategies for materials design that exploit insights from data-driven surrogate models together with first-principles simulations. A framework for data-driven inference of the quantum properties of TM complexes is developed, using artificial neural networks (ANNs) and graph-based molecular representations that facilitate rapid screening while retaining physical meaning such that chemical insights can be extracted. Multiple sources of uncertainty that would limit the application of these methods to TM complexes are addressed. Surrogate models are trained to estimate system-specific DFT uncertainty by including data from DFT calculations with different fractions of exact exchange, and a novel uncertainty metric for data-driven discovery is proposed that quantifies the ability of ANNs to generalize to unseen data based on similarity in the learned latent space. This metric is shown to offer superior performance over existing methods. The application of these methods to virtual design problems is demonstrated with two case studies: 1) identifying spin crossover complexes from a design space of thousands using an evolutionary strategy and 2) probabilistic, multiobjective optimization of redox couples over a 3 million-complex space. The utility of this surrogate-assisted approach is evident and orders-of-magnitude accelerations are obtained over screening purely with DFT. Such strategies open the door for in silico design of some of the most challenging molecular systems at a far greater scale than ever before.

Interoceptive Interventions: Interfacing with Inner States

Sun, 01 Sep 2024 00:00:00 GMT

Interoceptive Interventions: Interfacing with Inner States Jain, Abhinandan This thesis explores the emerging frontier of Human-Computer Interaction (HCI) that moves beyond traditional interfaces to directly modulate internal bodily processes, emotions, and cognitive states. As HCI progresses towards further integration between human and machine, this thesis investigates novel technologies that interface with interoceptive systems to influence subjective experiences and mental states. In this thesis, I introduce "Interoceptive Interventions" — tools designed to modulate physiological states. These tools interface with and alter internal physiological conditions, thereby influencing emotional and behavioral states. I present three individual proof-of-concept wearable prototypes “Frisson”, “ReCode”, and “Somnia” - grounded in neuroscience theories and evidence from embodied cognition. Frisson is a system targeted to elicit aesthetic chills and their downstream cognitive effects. I showcase experimental evidence of chills’ impact in modulation of emotional state, negative beliefs and amelioration of anhedonia in depression. Next, I present ReCode, a system which modulates baroreceptor activity and causally influences sympathetic activity (fight or flight response) and has consequential effect on perceived emotion and anxiety ratings. Finally, I present Somnia, a system which stimulates the vestibular system to influence sleep onset. These prototypes target specific pathways to enable on-demand emotion elicitation, emotion regulation and sleep regulation for users, while also providing potential non pharmacological interventions for conditions like insomnia, depression, and anxiety. This thesis aims to make a twofold contribution: First, it introduces a conceptual framework that highlights how interfacing with unconscious bodily processes opens up new possibilities for human computer interface design. Specifically, by gently actuating core physiological dynamics linked to consciousness and psychology, there is potential for such tools to deliver a promising new paradigm for digital wellness interventions. Second, the interoceptive modulation tools developed in this work provide a platform for researchers to experimentally engineer physiological processes underlying emotions and sleep. This could allow examining causative pathways between physiology and psychology beyond correlational observations and developing interventions for affective/sleep disorders. Researchers and designers can build on this to advance a generation of augmented technologies that empower users to self-regulate the body and the mind.

Public Interest Computing: a Pluralistic Design Language Foundation for Societal-Machine Alignment

Sun, 01 Sep 2024 00:00:00 GMT

Public Interest Computing: a Pluralistic Design Language Foundation for Societal-Machine Alignment Gaikwad, Snehalkumar 'Neil' S. The proliferation of algorithmic systems, including artificial intelligence (AI), in decision-making contexts necessitates a critical examination of their alignment with societal and environmental values. The reciprocal relationship between these norms and emerging AI technologies calls for a structural conceptualization of algorithmic systems that extends the scale of human-centered considerations. This dissertation introduces “Public Interest Computing, a Pluralistic Design Language,” which enables a novel design space for value-sensitive algorithmic ecosystems, fostering what we term “Societal-Machine Alignment.” The research is structured in three interconnected parts. First, we establish a comprehensive theory of Public Interest Computing, grounded in the planning and capability approach to human development. Second, we present a series of Public Interest Computing systems that instantiate and refine the proposed theoretical framework. These systems, co-designed with communities, demonstrate societal-machine alignment through five key design dimensions. Farm Pulse System exemplifies substantive fairness for at-risk farmers by enabling restorative justice through recourse in climate change adaptation decisions. Boomerang exhibits incentive alignment, promoting equitable designs of reputation systems in AI data markets. The Prototype Tasks System illustrates computationally mediated cognitive alignment, creating a level playing field for workers. The Beyond Boundaries framework enables environmental alignment, providing a platform for public discourse on climate change. Our analysis using Gobo focuses on value alignment, investigating ways to increase human agency in interactions with invisible algorithms on online platforms. Each system serves as an empirical testbed, providing critical design insights that shaped the theory and engineering of Public Interest Computing. The third part demonstrates the interplay between the developed Public Interest Computing systems and policy by applying the Pluralistic Design Language to real-world scenarios. We illustrate the bidirectional relationship between technology and policy, showing how Public Interest Computing informs policy decisions (“AI for Policy”) and, conversely, how policy shapes the responsible development of AI systems (“Policy for AI”). This symbiotic relationship opens new avenues for evidence-based policymaking, with Public Interest Computing serving as a foundation. By synthesizing the insights gained from this demonstration, we offer a principled approach for future research and practice, paving the way for a more informed and responsible design of algorithmic systems that aligns with societal values and priorities. Public Interest Computing and its Pluralistic Design Language serve as a guiding lens, leading us towards a future where societal values and algorithmic ecosystems are inherently aligned. Public Interest Computing is not an end in itself but a means for understanding, reflection, and adaptation, ensuring that as technology advances, so does our commitment to aligning it with the greater good.

Fluid-fluid displacement in porous-media microfluidics

Sun, 01 Sep 2024 00:00:00 GMT

Fluid-fluid displacement in porous-media microfluidics Qiu, Yu Immiscible fluid-fluid displacement under geometric confinement is a key physical process in large-scale subsurface energy technologies such as geologic carbon sequestration and in small-scale microfluidic techniques. Research over the past few decades has provided improved understanding of the fluid-fluid displacement patterns on the macroscopic scale, which range from compact displacement to fractal pattern. Many questions remain, however, regarding how the macroscopic displacement patterns are controlled by the microscale interactions between the fluid interface and the solid surface in systems under geometric confinement like microfluidic devices and porous media. This fluid-solid interaction—exacerbated by the roughness inherent to all natural and engineered surfaces—introduces a large energy dissipation near the solid boundary that challenges our ability to interpret laboratory experiments and develop mathematical models. In Part I of this Thesis, we study the motion of a fluid-fluid interface at the scale of a single capillary through mathematical modeling and laboratory experiments. We first develop a phase-field model to simulate two-phase flow with moving contact lines in the partial wetting regime. We construct a self-consistent formulation of fluid-solid surface energy which allows prescribing arbitrary static contact angles. We then propose a formulation to account for nonequilibrium conditions near the contact line and demonstrate the ability of our model to simulate dynamic configurations, from spontaneous imbibition to wetting transition and interface pinch-off. We then experimentally study the shape of a moving interface in a capillary tube prewetted with the invading liquid. For viscously favorable displacements (when the invading fluid is more viscous than the defending fluid), we find a universal behavior of the dynamic contact angle—a macroscopic descriptor of interface shape—which increases monotonically with capillary number. In contrast, for viscously unfavorable displacements, we observe a sharp wetting transition where the dynamic contact angle shoots to 180 over a narrow range of flow rates. Above the transition, a trailing film of viscous defending fluid is left behind the displacement front and the invading fluid propagates along the tube center as a finger. We rationalize the emergence of this sharp, trailing-film type of wetting transition by means of a minimal-ingredients hydrodynamic theory that exhibits bifurcated solutions. In Part II of this Thesis, we investigate the role of surface roughness on twophase displacements. We do so in a microfluidic device with a precisely controlled structured surface as an analogue for a rough fracture. In the drainage regime, we show that the roughness induces two types of liquid films entrained on the solid surfaces behind the displacement front: the classical Bretherton “thick film”, and a new type of “thin film” that is confined within the roughness. Each type of liquid film is characterized by distinct stability criteria and dewetting dynamics. In the imbibition regime, we show that surface roughness promotes that the wetting liquid preferentially advances within the roughness layer. The formation of a leading film stabilizes the displacement front as the flow rate increases, which would otherwise— that is, in a smooth confinement—become fractal. In summary, our work sheds light on the microscale physics and macroscopic pattern formation in rough confinement that may control long-term mixing and reactivity in geological systems and lab-on-a-chip applications.

Investigating Interventions in Fine-grained Contexts for Habit Formation

Sun, 01 Sep 2024 00:00:00 GMT

Investigating Interventions in Fine-grained Contexts for Habit Formation Khan, Mina Behavior change is important, yet hard to sustain. Habits are automatic responses to specific contextual cues, and can help sustain behavior change. Fine-grained specific contexts are commonly used in habit formation, but interventions in automatically-detected fine-grained contexts have rarely been explored for habit formation. We investigate habit-formation using interventions in fine-grained mobile, physical-world and digital, computer-based contexts, making three key contributions for each: a survey to identify behavior change needs, a prototype system designed to deliver fine-grained context-specific interventions, and a study to investigate habit-formation using interventions in fine-grained contexts, compared to interventions in less fine-grained contexts. We use the Self-report Habit Index (SRHI) and Self-Report Behavioral Automaticity Index (SRBAI) to measure habit formation and habit automaticity, respectively. For mobile, physical-world behavior change, the survey of needs (N=53 participants) indicated that participants want diverse and personalized behavior change support in diverse and specific contexts. We created a wearable device with on-device deep learning for interventions in personalized and privacy-preserving egocentric visual contexts. In a 4-week pilot study (N=10), interventions in egocentric visual contexts led to more percentage increase in average habit formation (SRHI) and automaticity (SRBAI) than interventions in coarse-grained contexts based on time, geolocation, and physical activity. The percentage increase in median habit formation was also more for the fine-grained egocentric context group, whereas the percentage increase in median habit automaticity was similar between the two groups. For both groups, the habits persisted in the post-study evaluations 1 and 10 weeks later, without interventions. For computer-usage behavior change, the survey of needs (N=68) indicated that participants want to reduce excessive/unnecessary use, e.g., social media, and found off-the-screen breaks helpful. We created a Chrome extension to deliver interventions based on specific web activities, and conducted a 6+2-week study (N=31, 6 weeks of interventions and 2 weeks of post-study without interventions). After 6 weeks, interventions in fine-grained website-entry-based contexts led to more percentage increase in mean and median habit formation and automaticity than interventions in coarse-grained interval-based or random contexts. After the additional two-week post-study, without interventions, the website-entry group had the largest percentage increase in mean SRHI/SRBAI, whereas the interval-based group had the largest percentage increase in median SRHI/SRBAI. Qualitative results from both studies indicated that interventions in fine-grained contexts were delivered at more opportune moments and were less disruptive. We discuss the limitations of our research and present a first step towards investigating interventions in fine-grained contexts for habit formation, potentially for sustainable behavior change, without long-term dependence on technology.

The Impact of Vegetation Morphology on Turbulence and Bedload Transport

Sun, 01 Sep 2024 00:00:00 GMT

The Impact of Vegetation Morphology on Turbulence and Bedload Transport Zhao, Tian By promoting sediment deposition and retention, aquatic vegetation can contribute to river bank stabilization, biodiversity, as well as carbon sequestration. The morphology and distribution of aquatic plants influence the velocity field, turbulence intensity, and sediment transport in wetlands, which impacts the erosion and deposition processes. By combining physical and numerical experiments, this thesis quantified how vegetation geometry impacts turbulence and sediment transport near the bed. In aquatic canopies, turbulence generated at the stem scale, and for submerged canopies, also in the canopy shear layer, could contribute to the near-bed turbulence. Results of flume experiments using a constant channel average velocity revealed that bedload transport was predominantly correlated with near-bed turbulence, but was also weakly correlated with near-bed velocity. First, in emergent canopies, if vegetation was not clustered, turbulent kinetic energy (TKE) and bedload transport did not depend on the arrangement and stem diameter(s) and can be predicted from plant biomass and velocity. If vegetation was clustered in patches, TKE and bedload transport decreased with increased clustering and can be predicted from plant biomass, patch geometry, and velocity. Second, in submerged canopies, for constant channel velocity, submerged canopies could enhance or reduce bedload transport, depending on their degree of submergence. With increasing submergence, H/h (defined as the ratio of flow depth H to canopy height h), the near-bed velocity and TKE decreased, and the source of near-bed turbulence shifted from stem wake to the shear layer at the canopy top. A model to predict near-bed TKE in submerged canopies was developed and used to explore bedload transport under more realistic conditions with constant energy slope and flexible vegetation. For a constant energy slope, the denser the canopy, and/or the larger fraction of flow depth occupied by the canopy (decreasing H/h), the greater the sediment transport was reduced compared to unvegetated beds. This thesis provides essential parameterizations of vegetation to hydrodynamic and morphodynamic models, which can be used to predict the vegetation conditions that promote or diminish erosion, offering a useful guide for river and coastal restoration.

On the non-microbial sources and sinks of dissolved metabolites in seawater

Sun, 01 Sep 2024 00:00:00 GMT

On the non-microbial sources and sinks of dissolved metabolites in seawater Germolus, Noah Paul Dissolved marine metabolites are small (<1000 Da) organic chemicals that remain in seawater when passed through a filter (typically <0.2 µm pore size). Their name implies their biological function: to be produced and consumed by cellular metabolism. These chemicals are the flows of the “microbial loop”—the principle that most of the photosynthesized matter in the ocean is exchanged, respired, and restructured by single-celled organisms. Metabolites have critical biological utility, so they are considered extremely labile; estimates of the time each spends outside cells range from hours to days. Their concentrations are drawn down by their consumers to nanomolar and picomolar levels, making measurement difficult. However, improved techniques to measure metabolites simultaneously and at extremely low concentrations avail the question of what happens to metabolites outside the cell membrane. Conventionally, representations of labile DOM exchange networks avoid that question—metabolites’ short lifetimes imply their flows lead from one organism to the next. This thesis begins to interrogate that assumption, asking if there are other processes that could change the seawater exometabolome on time scales that are relevant to microbial life. In Chapter 1 I discuss the ways ambient metabolite pools could be affected by animals, chemistry, and physics. In Chapter 2 I investigate the photolysis of metabolites and examine metabolomic techniques’ suitability for such experiments. In simulated sunlight, 11 of 57 metabolites decayed to some extent in artificial or natural seawater, and tryptophan and kynurenine may decay rapidly in the mixed layer of an oligotrophic ocean. For Chapter 3, I captured five species of migratory zooplankton and measured metabolites in their dissolved excreta. Four species survived the experiment and produced 43 metabolites, many at a rate that should be measurable in field samples. Chapter 4 harnesses the previous two chapters, plus a model for physical mixing, to probe a field dataset comprising 60 metabolites from Hydrostation S (south of Bermuda). Based on eight profiles over the course of two days, I posit: (1) copepods alone can supply the entire demand of >20 compounds to the mixed layer; (2) mixing is rapid enough to erase input signatures in the mixed layer; and (3) photochemistry is a slow leak of metabolites to forms whose lability is yet unknown. Chapter 5 reflects on how metabolites break the microbial loop—and suture it together with more ecological richness than with elemental fluxes alone.

Creativity and Justice: Leveraging Creative Learning Principles to Co-Design Just Futures With and For Young People

Sun, 01 Sep 2024 00:00:00 GMT

Creativity and Justice: Leveraging Creative Learning Principles to Co-Design Just Futures With and For Young People Trapp, Jaleesa Young people who live in underserved and under-resourced communities and have access to a creative learning environment are poised to create positive change within their communities. Their lived experiences make them experts on the issues their communities are currently facing, and the creative learning environment lends itself as a space where young people can prototype, improve, and implement solutions. Young people can use their imagination and creativity to seek justice and re-imagine their communities. This dissertation examines the Youth Activism and Advocacy program, which I designed using a transformative justice framework, in collaboration with the Clubhouse Network, a global network of after-school centers in historically under-resourced communities. Young people in ten communities around the world used their creativity, lived experiences, and civic imagination to develop and sustain social justice campaigns in their communities. This dissertation addresses the following research questions: (1) How might we cultivate and support constructionist learning environments that serve young people from communities that have been marginalized? (2) How might we use computational tools to support creative learning while developing and amplifying social justice campaigns? (3) How might we use Human Centered Design methods to allow for meaningful participation and engagement from youth who have been marginalized? While there were multiple pathways into and motivations for engaging in community action projects, all of the young people gained technical, organizational, and leadership skills that can be applied in future education and career pursuits. The outcomes of the Youth Activism and Advocacy program are complex and intertwined, prompting a call to action to further examine how civic engagement and creative learning can broaden participation in STEM and computing fields—and support youth in making a positive impact in their communities, moving them towards greater justice.

Textile Macroelectronics: Architecting Sensate and Computational Fabrics across Scales

Sun, 01 Sep 2024 00:00:00 GMT

Textile Macroelectronics: Architecting Sensate and Computational Fabrics across Scales Wicaksono, Irmandy Textiles are omnipresent and among the oldest forms of art and culture in human civilization. They serve as our protective skin, the interface between our bodies and the environment, and a medium for self-expression and collective experience. As electronics become more compliant, miniaturized, and low-cost, textiles provide an ideal substrate for technology integration, further driving the era of ubiquitous computing. My research fuses recent advances in functional materials, digital fabrication, hardware systems, and immersive technologies to demonstrate Textile Macroelectronics architecture and develop sensate and computational fabrics across scales. In this dissertation, I propose a ubiquitous computational textile framework—a synergy between functional device selection, textile structures, fabrication tools, and system architecture—that integrates a distributed network of sensing and computational elements as primitives or raw materials in the manufacturing process of electronic textile products. In the first part of the dissertation, I present several methods, artifacts, and implementations of sensate textiles using functional fibers and digital machine knitting. I argue that to promote the disruption and adoption of sensate textiles and achieve seamless integration, we require a better hierarchical understanding of textile construction and fiber-fabric properties, as well as ways to integrate electronics and functionalities with industrial textile fabrication processes. By controlling functional and common yarn inputs, along with knitting structures and patterns, I can architect fabric forms and aesthetics while tuning their electrical and mechanical properties. With this approach, I have developed a set of custom proxemic and tactile textile interfaces based on capacitive and piezoresistive sensing for musical expression, human-computer interaction, activity recognition, and multi-sensory experiences in various forms such as cloth, footwear, mats, carpets, and large-scale architectural facades. In the second part of the dissertation, I will discuss my work in exploring flexible, stretchable, and soft printed circuit technologies, incorporating multi-modal sensing with distributed computation to address scalability issues inherent in large and dense sensate textiles. These efforts have led to unique power, interconnection, and networking paradigms that allow us to transition from application-specific sensate textiles to generic computational fabrics that can be tailored and programmed for various applications. Finally, through these collective and complementary efforts, I aim to demonstrate an ecosystem of fabric artifacts that will lead us toward an Electronic Textile Gaia—a vision where sensing and intelligence are seamlessly interconnected and integrated into the fabric of everyday life, from in-body, on-body, room-scale, to architectural textiles, for applications ranging from physiological and physical activity monitoring to interactive media and built environments.

Wave propagation sensors for structural control

Wed, 01 Jan 1992 00:00:00 GMT

Wave propagation sensors for structural control Pines, Darryll J. (Darryll John) Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 1992; Includes bibliographical references (leaves 166-172).

Global solvability of invariant differential operators.

Sun, 01 Jan 1978 00:00:00 GMT

Global solvability of invariant differential operators. Zhang, Weida. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1978; Vita.; Bibliography: leaves 96-97.

The Federal Urban Renewal Program: a financial and economic analysis

Mon, 01 Jan 1962 00:00:00 GMT

The Federal Urban Renewal Program: a financial and economic analysis Anderson, Martin Carl. Thesis: Ph. D., Massachusetts Institute of Technology, School of Industrial Management, 1962; Includes bibliographical references.

A crosscorrelation method for measuring the impulse response of reactor systems

Sun, 01 Jan 1961 00:00:00 GMT

A crosscorrelation method for measuring the impulse response of reactor systems Balcomb, J. Douglas. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nuclear Engineering, 1961; Includes bibliographical references (leaves 136-137).

An application of Bernoulli polynomials to the theory of cyclotomic fields.

Fri, 01 Jan 1965 00:00:00 GMT

An application of Bernoulli polynomials to the theory of cyclotomic fields. Segal, Robert. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1965

Interaction of aflatoxin B1 with DNA in vivo in the rat and mouse

Mon, 01 Jan 1979 00:00:00 GMT

Interaction of aflatoxin B1 with DNA in vivo in the rat and mouse Croy, Robert George. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nutrition and Food Science, 1979; Vita.; Bibliography: leaves 153-165.

Specificity and structural characterization of the PDZ domain from DegS, an extracytoplasmic E. coli protease

Specificity and structural characterization of the PDZ domain from DegS, an extracytoplasmic E. coli protease Walsh, Nathan P. (Nathan Peter), 1973- DegS is a membrane-bound bacterial protease that is involved in the extracytoplasmic-stress response. The C-terminal domain has limited homology to PDZ domains and was thought to be involved in regulation or substrate recognition. A model of this PDZ domain was generated from NMR solution studies and homology modeling. Peptide selection studies identified the sequence Tyr-Tyr-Phe (YYF) as a C-terminal motif that binds to the PDZ domain. Possible targets were identified including many of the outer-membrane proteins (OMPs), which contain both a conserved terminal YxF and internal YYF sequences. The binding of the DegS PDZ domain to a YYF peptide and OMP derivatives were confirmed using microcalorimetry. Because stress signaling can be triggered by over-expression of some of the outer-membrane proteins, I propose that DegS may receive a signal from unassembled OMPs and transmit it to the aE transcription factor by increasing proteolysis of RseA. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biology, February 2002; Includes bibliographical references (p. 87-95).

Photocatalysis in a New Light: A Biohybrid Approach for Improved Reactivity with Tunable, Low-Energy Light Excitation

Sun, 01 Sep 2024 00:00:00 GMT

Photocatalysis in a New Light: A Biohybrid Approach for Improved Reactivity with Tunable, Low-Energy Light Excitation Cesana, Paul T. Since the advent of photoredox catalysis, much thought has been devoted to the development of exciting reaction modalities and the catalysts which perform these reactions. Less thought has been placed into the specific aspects of light absorption as the key step in photocatalytic mechanisms. Natural photosynthetic systems drive the high-energy reactions of photosynthesis with efficient and broadband energy capture. They provide a blueprint toward optimizing these processes in synthetic systems. In photosynthesis, both light capture and reactivity have been optimized by separation into distinct sites. The dominant process by which absorbed sunlight is transferred between these sites is through resonance energy transfer, which is highly efficient over long distances. This work highlights that light capture and energy transfer are crucial steps for the design of highly efficient photocatalysts in the future. Chapter 1 describes the relevant structures in natural photosynthesis as inspiration for synthetic approaches, the different mechanisms of energy transfer, and examples of photocatalytic systems that harness such excitation transfer processes to improve performance. Chapter 2 reports the synthesis of a biohybrid photocatalyst inspired by the modular architecture of photosynthetic apparatus which conjugated a photosynthetic light harvesting protein to a transition metal photocatalyst. Spectroscopic investigation found that absorbed photoenergy was efficiently funneled from the light harvester to the photocatalyst. The utility of the biohybrid photocatalyst was demonstrated via an increase in yields for two test reactions, including enabled reactivity at red wavelengths where the photocatalyst alone does not absorb. Chapter 3 establishes the power of incorporating nature’s design into non-natural photoenzymatic catalysis, generalizing the approach to other systems and methodologies. Photoenzymes require high-intensity light to function because of the poor absorption properties of their photoactive intermediate. A conjugate composed of a covalently linked photoenzyme and light antennae separates light capture from catalysis. Spectroscopic characterization of the conjugate showed the presence of efficient energy transfer from the light-harvesting components to the photoenzyme. In the presence of energy transfer, a maximum ~4-fold increase in product yields was observed as well as enabled reactivity. Chapter 4 highlights spectroscopic exploration into emerging molecular catalyst species. Finally, Chapter 5 provides an outlook to the future possibilities of the topics presented herein.

Studies on the synthesis of bisindole Aspidosperma alkaloids

Sun, 01 Sep 2024 00:00:00 GMT

Studies on the synthesis of bisindole Aspidosperma alkaloids Pinto, Taylor I. Introduction and Background on Aspidosperma Alkaloids A brief overview of monoterpene indole Aspidosperma alkaloids is discussed. The biosynthesis of the characteristic pentacyclic core from tryptamine and secologanin is summarized. Some representative examples of total syntheses of Aspidosperma alkaloids are discussed. Synthetic strategies for the synthesis of bisindole members of the family are also examined. II. Total Synthesis of (–)-Voacinol, (–)-Voacandimine C, and related congener, (−)-methylenebisdeoxoapodine We describe the first total synthesis of complex aspidosperma alkaloids (–)-voacinol and (–)-voacandimine C via a late-stage C7-methylenation strategy inspired by a biogenetic hypothesis. We envisioned rapid access to these natural alkaloids from a common, symmetrical precursor assembled by methylenation of a D-ring-oxidized variant of the structurally related natural product (–)-deoxoapodine. Chemoselective N9-oxidation of a pentacyclic deoxoapodine precursor enabled the synthesis of the corresponding hexacyclic C8-aminonitrile. Stereocontrolled methylenation of a C8-enamine derivative of deoxoapodine, accessed by ionization of the C8-aminonitrile, afforded a symmetrical dodecacyclic bisaminonitrile as a versatile precursor to these bisindole alkaloids. Final-stage, biosynthesis-inspired, controlled reductive opening of the oxolane substructures of this dodecacyclic intermediate provided a unified approach to (–)-voacinol and (–)-voacandimine C, while direct reduction of the same intermediate afforded the structurally related (–)-methylenebisdeoxoapodine. III. Progress Toward the Total Synthesis of Voacandimine A We describe our work toward the total synthesis of bisindole Aspidosperma alkaloid, voacandimine A. Key features of the synthetic progress include two routes for monomer synthesis, two methods for complex fragment assembly to form the bisindole structure, and strategies to address the stereochemistry of the ring fusion.

Symmetry and its Signatures in Quantum Many-Body Dynamics

Sun, 01 Sep 2024 00:00:00 GMT

Symmetry and its Signatures in Quantum Many-Body Dynamics Ogunnaike, Olumakinde Symmetry has long been a defining feature in our understanding of statistical or manybody systems. By making appeals to universal properties associated with global symmetries and topology, one may describe universal properties of “typical” states and dynamics in equilibrium, even when keeping track of the precise dynamics of a particular many-body system is impossible. This challenge of tracking allowable states and dynamical transitions is only exacerbated for non-equilibrium systems, where one cannot rely on the same notions of typicality. Further, when driven out of equilibrium by external interactions, quantum orders constructed from highly sensitive correlations between states are liable to vanish. Despite these conceptual and practical difficulties, the rise of quantum technologies and accompanying theoretical developments has motivated a surge of interest in dynamical quantum phenomena. The recent developments in the field of quantum many-body dynamics provide satisfactory accounts of many interesting phenomena, including failures of the Eigenstate Thermalization Hypothesis, various dynamical and mixed-state phases of matter, and measurement-induced dynamics and phase transitions. Many of these results are explained for specific systems or within different conceptual frameworks, however these results rarely generalize. In this thesis, I attempt to unify many aspects of quantum many-body dynamics under the same conceptual framework through an investigation of the universal signatures of symmetry in quantum dynamical systems. This is accomplished via a mapping between the averaged dynamics and the low-energy spectrum of an effective Hamiltonian in a “doubled Hilbert space,” comprised of two copies of the original space. This provides a general and versatile framework to qualitatively understand both familiar and novel universal properties of dynamical phenomena like charge diffusion, sub(super)-diffusion of multipole moments in systems with short and long-range interactions, charge and multipole, and even measurement-induced phase transitions. By expanding into a doubled Hilbert space, one may capture the subtleties of non-equilibrium physics, and particularly dynamical phases, within the framework of equilibrium physics and phases. In this work, we examine how to understand various symmetry-constrained dynamical phases and phase transitions using through a dual description of symmetry-constrained equilibrium phases and symmetry-breaking transitions in an enlarged Hilbert space.

Exploring the Intersection of Physics Modeling and Representation Learning

Sun, 01 Sep 2024 00:00:00 GMT

Exploring the Intersection of Physics Modeling and Representation Learning Kitouni, Ouail Representation Learning has evolved into a multi-purpose tool capable of solving arbitrary problems provided enough data. This thesis focuses on two primary directions: (1) Harnessing the power of deep learning for applications in fundamental physics and (2) using physicsinspired tools to improve and shed some light on otherwise large-scale, inscrutable black-box algorithms. We explore a collection of applications that improve different aspects of nuclear and particle physics research across its many stages, from online data selection to offline data analysis. We also tease out how deep learning can open up entirely new avenues of research through the lens of mechanistic interpretability to (re)derive fundamental theory as well as new ways to reinterpret physics measurements. Lastly, we study how physics tools can be useful to better understand the dynamics of deep learning as well as provide a solid foundation for algorithms and training paradigms that expand the frontier of machine learning.

Protein spatiotemporal dynamics in gene regulation and disease pathology

Sun, 01 Sep 2024 00:00:00 GMT

Protein spatiotemporal dynamics in gene regulation and disease pathology Zheng, Ming A cell orchestrates billions of proteins to the right place at the right time to perform diverse cellular processes. Over the decades, this field has been evolving by integrating advances in microscopy, biochemistry, and molecular biology to unravel the intricate mechanisms governing protein spatiotemporal dynamics as well as the functional consequences. This thesis focuses on the physical motions of proteins at a length scale of tens of nanometers to several microns, where the apparent diffusion and the condensate dynamics of assembly and disassembly are specifically studied. In the studies presented in this thesis, the functional relevance of protein motion is exemplified in the context of gene regulation and disease pathology. We find that the apparent diffusion of transcription factors (TFs) is preferentially partitioned into slowly diffusing states by interacting with RNA, leading to enhanced chromatin occupancy and gene expression (Oksuz et al., 2023). The assembly and disassembly dynamics of transcriptional condensates are coupled to the active RNA synthesis, linking gene expression and the spatiotemporal organization of transcriptional proteins in a feedback loop (Henninger et al., 2021). In addition to transcriptional proteins, we find insulin receptors (IRs) are incorporated in dynamic condensates in normal cells to perform metabolic signaling transduction. In insulin-resistant cells which could occur in chronic diseases such as type 2 diabetes (T2D), IR signaling is dysregulated, associated with diminished IR condensate dynamics of assembly and disassembly (Dall’Agnese et al., 2022). Furthermore, pathogenic signaling reduces the mobility of key proteins–both inside and outside of condensates—that act in many cellular functions. Such reduced protein mobility under diverse pathogenic stimuli, termed proteolethargy, may account for diverse cellular dysregulation seen in chronic disease (Dall’Agnese, Zheng, Moreno et al., 2024).

Advanced Reconstruction Techniques for CUORE: Searching Beyond the Standard Model with Cryogenic Calorimeters

Sun, 01 Sep 2024 00:00:00 GMT

Advanced Reconstruction Techniques for CUORE: Searching Beyond the Standard Model with Cryogenic Calorimeters Mayer, Daniel W. Located within the Laboratori Nazionali del Gran Sasso (LNGS), the Cryogenic Underground Observatory for Rare Events (CUORE) is an experiment primarily searching for neutrinoless double beta decay in ¹³⁰Te. It is the largest operating sub-kelvin cryogenic detector array, instrumenting 988 TeO₂ detector channels at temperatures below 20 mK. CUORE uses the cryogenic calorimeter technique, resolving the thermal signatures from nuclear/particle interactions within crystal absorbers for precise determination of deposited energy. This work establishes methods and analysis techniques to treat CUORE as a segmented detector in aggregate, with a focus towards identifying and reconstructing track-like signatures induced by high-energy through-going particles traversing the detector array. Implementations of such high-multiplicity techniques are used to validate that CUORE can resolve the remaining underground flux of muons within LNGS. This result demonstrates CUORE’s unprecedented size and acceptance as compared to previous cryogenic calorimeter arrays, and has applications towards future searches for neutrinoless double beta decay for which muon-induced backgrounds are non-negligible. Additionally, these methods open up new avenues for CUORE to search for exotic beyond-the-Standard Model particles and interactions, such as particles with fractional electric charge. If realized in nature, fractionally charged particles (FCPs) could be present within the underground flux of cosmic radiation and would leave faint track-like signatures across the detector. We report on a search for FCPs using the first tonne-year of CUORE’s exposure, finding no excess of FCP track candidates over background, and setting leading limits at 90% C.L. on the possible underground flux of FCPs with charges between 1/24 − 1/5 that of an electron. Lastly, we introduce differentiable programming methods for the end-to-end training of neural ordinary differential equations to model thermal pulse dynamics within CUORE calorimeter channels. These methods and results improve understanding of detector response, enable improved in situ background characterization, and open novel opportunities for CUORE and future tonne-scale cryogenic calorimeters to search for physics beyond the Standard Model.

The Search for High-Frequency Gravitational Waves with a Modified Axion Detector

Sun, 01 Sep 2024 00:00:00 GMT

The Search for High-Frequency Gravitational Waves with a Modified Axion Detector Pappas, Kaliroë Mabelle West ABRACADABRA-10cm has had great success as a lumped-element axion dark matter pathfinder experiment, with two published axion searches and an extensive background investigation. Now, using the electrodynamics of gravitational waves and a simple change of pickup structures, we are using the ABRACADABRA detector to search for high-frequency gravitational waves in the kHz to MHz range. These higher frequencies may indicate signs of in-spiraling primordial black holes, or other beyond the standard model phenomena. With careful calibration used to distinguish between the two signals, we introduce the first simultaneous search for both axions and gravitational waves using a lumped-element axion detector. In this thesis I will present on the high-frequency cryogenic ABRACADABRA-10cm detector, the background investigations of the detector and the design and first data from the ABRACADABRA-10cm high-frequency gravitational wave search.

Radioactive Atoms and Molecules for Fundamental Physics

Sun, 01 Sep 2024 00:00:00 GMT

Radioactive Atoms and Molecules for Fundamental Physics Udrescu, Silviu-Marian The Standard Model (SM) of particle physics and the theory of General Relativity represent two of the greatest achievements in physics in the past century. However, despite their success, many experimental observations remain unanswered: What is the nature of Dark Matter and Dark Energy? Why is there so little antimatter in the Universe? Why is gravity so weak compared to the other fundamental forces? These questions point to the existence of new phenomena waiting to be discovered. High-precision laser spectroscopy experiments using atoms and molecules emerged as a fruitful approach for searching for new physics effects. Recently, atoms and molecules containing short-lived radioactive isotopes have been proposed as particularly sensitive laboratories to search for physics beyond the SM, especially at the nuclear level. However, many atoms containing very short-lived isotopes are still out of reach for spectroscopic investigations, while radioactive molecules have been completely inaccessible experimentally until recently. In this thesis, I will present a series of pioneering experiments aimed at harnessing the power of radioactive atoms and molecules to explore nuclear phenomena, both within and beyond the SM. I will start by describing the first-ever precision laser spectroscopy investigation of a radioactive molecule, radium monofluoride (RaF). I will present measurements of the vibrational, rotational, and hyperfine spectrum of RaF, proving its high sensitivity to minuscule nuclear effects. These experiments allowed the quantification of a feasible laser-cooling scheme for RaF and the observation of the effect of the distribution of nuclear magnetization inside the Ra nucleus on the energy levels of RaF. To our knowledge, this is the first time this effect was observed in a molecule, opening the way for using molecules to benchmark ab initio nuclear theory. Finally, I will present measurements of the ionization potential of RaF, showing its suitability for Rydberg states studies and precise quantum control using external electric fields. I will then present the theoretical calculations and the status of an experiment aiming to measure hadronic parity violation using single molecular ions inside a Penning trap. The experiment's goal is to use the external magnetic field provided by the trap to fine-tune molecular energy levels of opposite parity close to degeneracy, thus increasing the signal produced by parity violating nuclear properties. The sensitivity to the sought-after signal is expected to be increased by more than twelve orders of magnitude compared to atoms. This amplification will allow the observation of yet-to-be-measured parity violating effects in a molecule. These measurements will be critical to guide our understanding of electroweak nuclear phenomena. Finally, I will show preliminary results obtained from a novel experiment with the goal of enabling laser spectroscopy studies of atoms and molecules containing radioactive nuclei with lifetimes of 1 ms and below. Such isotopes can't be currently studied spectroscopically. Using an event-by-event Doppler reconstruction, our approach could overcome most of the challenges encountered by state-of-the-art experimental techniques, allowing us to extend our reach toward unexplored regions of the nuclear chart. Such short-lived isotopes are of great importance for our microscopic understanding of nuclei as well as for constraining the properties of nuclear matter.

Exciton Dynamics and Anisotropy in 2D Metal Organochalcogenolate Semiconductors

Sun, 01 Sep 2024 00:00:00 GMT

Exciton Dynamics and Anisotropy in 2D Metal Organochalcogenolate Semiconductors Lee, Woo Seok Silver phenylselenolate (AgSePh) is a novel hybrid organic-inorganic two-dimensional (2D) semiconductor that belongs to the broader class of metal organochalcogenolates (MOCs). Since its blue-emitting excitonic properties were discovered in 2018, AgSePh has attracted attention from the scientific community. From a fundamental science perspective, AgSePh provides an excellent platform for exploring many-body interactions among quasiparticles (such as excitons, phonons, and photons) due to its large exciton binding energy, strong exciton-lattice interactions, and natural photonic cavity structure. From a technological standpoint, its narrow blue emission, a tunable bandgap through composition control, chemical robustness, in-plane anisotropy, and low-cost, scalable synthetic methods make AgSePh promising candidate for photonic and optoelectronic applications. However, we do not yet fully understand how its excitonic properties arise at a fundamental level. The central aim of this thesis is to elucidate the correlation between structure, inorganic composition, organic ligands, and excitonic properties in these novel hybrid 2D semiconductors. First, we present the synthesis, structural and optical properties of 2D AgEPh (E = S, Se, Te) single crystals, colloidal nanocrystals, and thin films. Importantly, the growth of millimeter-sized single crystalline 2D AgEPh (E = S, Se, Te) enables their crystal structure determination via single crystal X-ray diffraction: AgSPh in P2₁, AgSePh in P2₁/c, and AgTePh in P2₁/c. Second, we explore the underlying mechanism of light emission in AgSePh and AgTePh. Despite having the same crystal structure, these compounds exhibit strikingly different excitonic properties: AgSePh shows narrow photoluminescence (PL) with a minimal Stokes shift, while AgTePh exhibits broad PL with a large Stokes shift. Using time-resolved and temperature dependent optical spectroscopy, combined with sub-gap photoexcitation studies, we demonstrate that the exciton dynamics in AgSePh films are dominated by the interaction of free-excitons with extrinsic defect states, whereas the dynamics in AgTePh are dominated by intrinsic exciton selftrapping behavior. Third, we study alloying between AgEPh. we demonstrate that AgSePh and AgTePh form homogeneous alloys with tunable excitonic properties across all compositions, whereas AgSPh and AgSePh/AgTePh exhibit a miscibility gap. These observations are elucidated by density functional theory calculations and correlated with crystallographic information. Fourth, using polarization-resolved micro-absorption, reflectance, and photoluminescence spectroscopy, combined with the GW plus Bethe-Salpeter equation calculations, we reveal multiple low-lying excitons with in-plane anisotropy in AgSePh and AgTePh. This showcases the richness of excitonic physics in these materials, which arises from their low-symmetry crystal structures. Finally, we show that the electronic and excitonic structure of AgSePh can be engineered through organic functionalization, resulting in giant excitonic anisotropy and a completely different absorption spectrum in 2D AgSePh-F₂(2,3). This divergence in excitonic properties is attributed to the semi 1D Ag chains in AgSePh-F₂(2,3), in contrast to hexagonal 2D Ag network in AgSePh. This finding can be generalized to other blue-emitting 2D AgSePh-R compounds which exhibit either AgSePh-like or AgSePh-F₂(2,3)-like absorption spectra. Overall, this thesis advances the understanding of the structure-composition-excitonic property relationships in these emerging hybrid semiconductors, paving the way for future investigations into this exciting material family.

From Research to Search: Technologies and Techniques of Legal Research, 1880-1980

Sun, 01 Sep 2024 00:00:00 GMT

From Research to Search: Technologies and Techniques of Legal Research, 1880-1980 Reiss Sorokin, Alex In 1964, the Ohio State Bar Association (OSBA) embarked on a project to harness computer technology to automate legal research. After three years of investigation, it established the Ohio Bar Automated Research (OBAR) organization and contracted a local computer company, Data Corporation, to develop an electronic legal research service. Despite initial skepticism and mounting costs, these lawyers and technologists managed to launch a working service by 1969. The service – also named OBAR – was available through remote consoles placed in law firms, libraries, and government offices. By 1973, an improved system was relaunched as Lexis, a soon-to-be-national legal information retrieval service. Lexis went on to become a staple of American legal practice while OBAR gradually faded out of the picture. This dissertation tells the story of the OBAR system and its promise of automating legal research. What did it take for lawyers to begin using and trusting computer technology for their work? I argue that the automation of legal research required both conceptual and material rearrangement. Legal research was a deeply social activity supported by an intricate infrastructure of people, technologies, and techniques. To be trusted and used, the computer had to be constantly charged with meanings, often contradictory ones. It was presented as a tool that would be integrated into an existing legal research process and a technology that would overhaul legal research. The computer was attributed mechanical qualities, like being objective or operating according to instructions, and human ones, like being sophisticated and capable of conversation. These contradictory meanings, along with the gap between promise and reality, were constantly sewn together as part of the computerized system’s development and marketing process. To capture the process of automation, this dissertation traces legal research practices before the computer, the development process of the new technology, and the competing notions of trust and credibility in its early years. The first section traces the splintering of legal research into a distinct task that could be taught, delegated, and automated. In the first chapter, I focus on print legal research technologies and legal research instruction through the first half of the 20th century. I show that innovations in legal research went hand-in-hand with a reallocation of legal work among lawyers and non-legal staff. Examining legal research manuals shows that instruction in types of law book gradually gave way to a more systematic approach to legal research. The second chapter considers the history of legal research work through an examination of the law office and the distribution of labor within it. It shows that the development of legal research into a distinct task that could be delegated was intertwined with social, professional, and technological developments at mid-century. The third chapter describes how the specter of automation focused bar associations’ attention on legal research practices. It shows that legal research fit into a social and professional setting. Lawyers relied on an array of technologies and personnel to produce answers to legal questions. As a whole, the section argues that three factors joined to make legal research into a distinct task, thus making its automation possible: the development of instructional materials and courses on legal research, the growth and bureaucratization of law firms, and the introduction of women and machinery into the law office in the 20th century. Two chapters and two short excursuses make up the second section, which focuses on the development and early adoption of the OBAR system. In chapter four, I examine the entanglement of technological choices and ideals in the process of developing the OBAR system in the 1960s. I show that the focus on direct use by lawyers was meant to cast suspicion on human judgment while touting the computer as an objective and trustworthy tool. Excursus one unpacks OBAR’s promise of an interactive system. It shows that at the same time as the system was likened to human dialogue, it offered a substantially different interaction with court cases, a process that altered the epistemic and social setting of legal research. Chapter five considers the reactions of OBAR’s early users as communication consoles were placed in law firms and libraries across Ohio in the 1970s. Relying on call reports and correspondence, I examine controversies around the system’s accuracy and credibility. Excursus two tells the story of what came out of the system’s promise in light of later developments. Focusing on the chasm that developed between lawyers and technologists in defining the system in the 1970s, it explains how an approach that focused on the system as a product prevailed over an approach that viewed the system as a service to the profession. To become a successful national product, Lexis had to shed its connections to the organized bar and give up any social aspirations.

Coherent Terahertz Control and Ultrafast Spectroscopy of Layered Antiferromagnets

Sun, 01 Sep 2024 00:00:00 GMT

Coherent Terahertz Control and Ultrafast Spectroscopy of Layered Antiferromagnets Ilyas, Batyr The central theme of modern condensed matter physics is to understand the emergent phenomena arising from interactions among Avogadro’s number of particles in quantum materials, alongside efforts to control their properties. While powerful transport, thermodynamic, and spectroscopic tools have been developed, they often fall short to reveal the intricate interplay among electronic, spin, orbital, and lattice degrees of freedom. A promising approach involves selectively perturbing one degree of freedom while observing responses in others, made possible by ultrafast lasers with femtosecond time resolution. These advancements not only showcase the capability of ultrafast experiments in understanding complex material properties but also demonstrate the manipulation of ordered phases at ultrafast timescales, thereby opening a laboratory for studying materials in nonequilibrium regime. This dissertation contributes to the ongoing effort of developing new ultrafast spectroscopy tools, utilizing them to probe lattice, magnetic, and electronic properties, and gaining active control over them. Specifically, it investigates the induction of a new magnetic state with net magnetization using intense low-energy terahertz (THz) pulses in the van der Waals antiferromagnet FePS₃. Critical fluctuations near the phase transition are found to enhance both the magnitude and the lifetime of this new state. Additionally, a broadband two-dimensional (2D) THz spectroscopy technique is developed and employed to study interactions among low-energy collective excitations and to directly identify phonons that induce the new magnetic phase. Furthermore, time-resolved spectroscopy in the visible and near-infrared spectral range is utilized to detect a bound state between phonon and electronic states in the sister compound NiPS₃, and to capture a magnetostriction effect in FePS₃ using coherent phonon spectroscopy, that was elusive to conventional diffraction experiments. Finally, second harmonic generation spectroscopy with microscale spatial resolution, is employed to study the multiferroic material NiI₂, demonstrating its persistence down to the single atomic layer — a first of its kind. These findings and tools can potentially be extended to frustrated quantum magnets to control their magnetic phases and potentially detect their collective modes. The 2D nonlinear spectroscopy utilized in this dissertation is gaining attention both theoretically and experimentally as a promising tool for detecting fractionalized spin excitations.

Decarbonizing Long-haul Trucking

Wed, 01 May 2024 00:00:00 GMT

Decarbonizing Long-haul Trucking Jones, Robert As climate change poses an ever-increasing challenge for the world, the transportation sector has experienced significant troubles in mitigating its carbon dioxide emissions. Particularly responsible for this development is the heavy-duty trucking sector. Heavy-duty freight trucks are responsible for approximately 30 % of the highway transportation emissions even though they only represent about 5.5 % of vehicles on the road. Heavy-duty trucks are also the backbone of US freight, as they account for 71 % of freight delivered to the American people. The corresponding road freight energy consumption has been consistently increasing over the last decades and is expected to grow even further in the future. Emissions must be drastically reduced in order to adhere to the proposed targets of the 2015 Paris Agreement and to limit global warming. This contrast raises a crucial question: how can road freight emissions be substantially reduced while at the same time facing a growing transportation demand? Especially for long-haul class 8 trucks, this question is difficult to answer. This study seeks to elucidate potential competitive powertrain and fuel combinations and eliminate other poor alternative options.

On the physics of intranuclear organization

Sun, 01 Sep 2024 00:00:00 GMT

On the physics of intranuclear organization Sood, Amogh Eukaryotic nuclei, despite their diverse and crowded chemical milieu, can achieve precise spatiotemporal organization of their contents and chemistry, despite lacking access to membrane-bound organelles. It has recently become apparent that the cells accomplish this feat by leveraging physical processes such as liquid-liquid phase separation driven by multivalent macromolecular interactions to form biomolecular condensates which can serve as membrane-less organelles for the precise, vectorial organization of intranuclear contents. In particular, the hierarchical and functional packaging of DNA into chromatin is mediated by phase separation. Epigenetic modifications of histone proteins, which DNA wraps around to form nucleosomes, are key determinants of nucleosomes’ condensability and chromatin’s higher-order structure. Chromatin structure, by regulating access of transcriptional machinery to the genome, in turn, has broad implications for cellular processes such as gene regulation and cellular differentiation. Furthermore, there exists a bi-directional feedback between 1D epigenomic sequence and 3D chromatin structure as the former is spread and maintained by enzymes that have a “reader-writer” functionality that allows them to similarly modify nucleosomes close to each other in sequence but not necessarily in space. Recent advances suggest chromatin has the properties of a viscoelastic network and exhibits non-trivial dynamics. Therefore, the dynamics of chromatin structure and the spread and maintenance of epigenetic marks are intimately and inextricably linked yet poorly understood. Part I of this thesis is devoted to understanding the complex interplay between chromatin structural dynamics and stochastic reaction networks describing histone modifications. Furthermore, given the prominent role phase separation plays in intranuclear organization, we devote Part II of this thesis to study the impact of competition between specific and non-specific interactions on liquid-liquid phase separation coupled to percolation and thereby attempt to elucidate the molecular grammar of phase separating biomolecules and evolutionary pressures that shape them.

Numerical and Analytical Methods in Low-Dimensional Strongly Correlated Quantum Systems

Sun, 01 Sep 2024 00:00:00 GMT

Numerical and Analytical Methods in Low-Dimensional Strongly Correlated Quantum Systems Peng, Changnan The study of low-dimensional strongly correlated quantum systems lies at the intersection of intricate theoretical models and practical numerical methods, offering deep insights into condensed matter physics. This thesis explores the application of various numerical and analytical methods to these systems. It addresses universal behaviors and phase transitions, exemplified by the phenomenon of multiversality. Specifically, the transition from a 1D Luttinger liquid to a charge density wave insulator, characterized by partly Kosterlitz-Thouless transition and partly Ising transition, is analyzed using both analytical renormalization group calculations and numerical density matrix renormalization group simulations. Additionally, the thesis introduces a statistical smoothing spline method to pinpoint transition points systematically. The work extends to quantum dynamics, presenting a generic theoretical framework for analyzing quantum-classical adiabatic dynamics with learning algorithms. A provably efficient adiabatic learning (PEAL) algorithm with favorable scaling properties is developed. The algorithm is numerically validated on the 1D Holstein model, demonstrating its precision in predicting dynamics. Furthermore, the thesis derives a Hamiltonian lattice formulation for the 2+1D compact Maxwell-Chern-Simons theory, providing an analytical solution that aligns with continuum theories and facilitating future numerical applications. Through these explorations, the thesis underscores the complementary roles of numerical and analytical methods in advancing the understanding of complex quantum systems.

Decarbonizing the US Power Sector

Wed, 01 May 2024 00:00:00 GMT

Decarbonizing the US Power Sector Farnsworth, Amanda As the second highest national emitter, the US has the opportunity, and responsibility, to reduce emissions and mitigate the impacts of climate change. The power sector has been identified as the linchpin in our national decarbonization strategy, with high electrification goals for the other sectors. As of 2022, the power sector was responsible for more than a quarter of annual emissions. As electrification increases, the importance of decreasing the emissions and emissions intensity of electricity production grows. This thesis explores the challenges and opportunities of decarbonizing the US power sector. Two models were built to complete this analysis: Ideal Grid (IG) which is a greenfield capacity expansion and economic dispatch model, and Evolving Grid (EG), which is a brownfield capacity expansion and economic dispatch model. These models are an especially novel addition to the current arsenal of publicly available capacity expansion models because they include embodied emissions, in addition to the industry-standard consideration of power plant tailpipe emissions from fossil fuel combustion. Nine regions of the contiguous US are represented in these models. First, IG is used to highlight regional decarbonization challenges. Regions with significant land available for variable renewable energy (VRE) buildout and strong wind resources had the cheapest paths to a clean grid. Also, hydropower resources play a significant role. At deep decarbonization levels, the need for long-duration energy storage (like pumped hydropower storage) increases. The role of embodied emissions is explored, showing that as fossil-fuel consumption decreases and VRE penetration increases, they become nonnegligible. To most effectively reduce system emissions, embodied emissions should be accounted for. Next, fusion is integrated into the model to demonstrate its potential role. Assuming a $8,500/kW CAPEX, fusion is not economically competitive unless a carbon constraint is applied. But, at deep decarbonization levels, fusion is prominent in all regions. EG shows that intermediary decarbonization goals before 2050 play a pivotal role in determining fusion adoption and overall fleet composition. Lastly, the versatility and value of presented models is demonstrated by outlining other potential applications.

The structure of hadrons and other potential phases of QCD

Sun, 01 Sep 2024 00:00:00 GMT

The structure of hadrons and other potential phases of QCD Schindler, Stella T. Quantum chromodynamics (QCD) is a mathematical theory describing subatomic particles called quarks and gluons, and the strong force that binds them together into protons and neutrons. This thesis centers on two major thrusts of modern QCD research: (1) uncovering the inner quark and gluon structure of the proton, and (2) mapping out other phases of matter that quarks and gluons form as we vary pressure and temperature. To study these topics, we develop, utilize, and synergize tools in quantum field theory (analytics), lattice gauge theory (numerics), and phenomenology (comparing theory to experiment). Specifically, we use new and existing techniques to access precision information about the inner structure of the proton, via the study of transverse momentum distributions, energy correlators, and diffractive processes at colliders. Additionally, we develop new analytic and numerical techniques for studying QCD phase structure inspired by non-Hermitian physics, and probe the possibility of new exotic phases near the QCD phase transition.

Nonreciprocal phenomena in superconductivity

Sun, 01 Sep 2024 00:00:00 GMT

Nonreciprocal phenomena in superconductivity Davydova, Marharyta This thesis introduces and studies several unusual phenomena that arise in low-dimensional systems in the presence of a magnetic field. The first example that we discuss is nonreciprocal superconductivity, which occurs upon simultaneous breaking of inversion and time-reversal symmetries. Nonreciprocal superconductors describe certain classes of unconventional superconductors that include certain kinds of mixed-pairing and finite-momentum ones. They also occur in engineered systems exhibiting s-wave pairing-based superconductivity, for which we put forward several simple proposals. We demonstrate several striking observable consequences of nonreciprocal superconductivity. These include current rectification in normal metal-nonreciprocal superconductor junctions and the Josephson diode effect, for which we propose a simple and universally applicable mechanism. With the advent of novel low-dimensional symmetry-breaking materials, such as multilayer graphenes and twisted cuprates, as well as modern experimental possibilities involving engineered systems, nonreciprocal phenomena could eventually become an indispensable tool for revealing the nature of superconducting orders. The second part of this thesis concerns doped Mott insulators in a magnetic field, described by a triangular-lattice Fermi-Hubbard model in the limit of strong interaction. This is relevant for many novel materials, such as moiré transition metal dichalcogenides bilayers. We predict a new bound state, spin polaron, formed by binding a doped hole with a magnon (spin flip). Spin polarons have large effective mass and are spin 3/2 quasiparticles. The mechanism for their formation is kinetic frustration, and therefore their binding energy is proportional to the hopping t, which is the largest energy scale within a single Hubbard band. We then propose a new phase diagram for the triangular lattice Hubbard model in a magnetic field as well as multiple experimental signatures. We hope that the prediction of the spin polaron, which has since been experimentally confirmed, will give rise to novel mechanisms for superconductivity and correlated orders in doped Hubbard models.

Decoding Dark Matter Halos through the Lens of Machine Learning

Sun, 01 Sep 2024 00:00:00 GMT

Decoding Dark Matter Halos through the Lens of Machine Learning Nguyen, Tri V. Dark matter (DM) constitutes about 85% of the matter in the Universe, yet its particle nature remains one of the greatest outstanding questions in astrophysics. DM halos act as the scaffolding within which galaxies form, but the specific mechanisms through which they influence galaxy evolution are not fully understood, especially at galactic scales. While cosmological simulations and astrophysical surveys have made significant strides in constraining DM properties, upcoming surveys will generate terabytes of complex, high-dimensional data. It is thus imperative to develop new methodologies capable of interpreting and linking this data with theoretical models. Machine learning techniques, coupled with advancements in cosmological simulations, present a transformative opportunity. In this thesis, I conduct a multi-scale investigation into the nature of DM and its role in shaping galaxies by integrating advanced machine-learning techniques with cutting-edge cosmological simulations. First, I employ simulation-based inference and graph neural networks to infer the mass density profiles of DM halos in dwarf galaxies from their stellar kinematics. Next, I develop a generative model using normalizing flows and recurrent neural networks to reconstruct the mass assembly histories of DM halos in cosmological simulations. Furthermore, I utilize variational diffusion models and Transformer-based neural networks to perform point-cloud modeling of satellite populations under alternative DM models. Finally, I create synthetic surveys for the Gaia surveys from Milky Way-like simulations, bridging the gap between simulations and observations. This thesis demonstrates the transformative potential of machine learning techniques to probe the DM properties and galaxy formation. The methodologies developed herein provide new avenues for interpreting vast and complex astronomical datasets and offer insights that could lead to a deeper understanding of the fundamental nature of DM.

Exploring New Frontiers in High Energy Physics: Boosted Resonances Decaying To Quarks, Foundation Models, and Heterogeneous Computing at the CMS Experiment

Sun, 01 Sep 2024 00:00:00 GMT

Exploring New Frontiers in High Energy Physics: Boosted Resonances Decaying To Quarks, Foundation Models, and Heterogeneous Computing at the CMS Experiment Krupa, Jeffrey In this thesis, we introduce machine learning (ML) tools to optimize data taking and analysis at data-intensive scientific experiments, focusing on the CMS experiment at the Large Hadron Collider (LHC). A path to a foundation model for LHC physics is described, where self-supervised learning is enabled through the re-simulation of decaying partons. The first experiments with remote operation of GPUs in LHC experiments are presented. These tools will help equip experiments at the High-Luminosity LHC (HL-LHC) to perform precision measurements and searches for new physics, for example, low mass resonances decaying to quarks. In this context, a search for narrow resonances decaying into quarkantiquark pairs produced with high transverse momentum is presented. The analysis is based on data collected in Run 2 with the CMS detector at the LHC in proton-proton collisions at √ 𝑠 = 13 TeV. Resonance candidates are reconstructed as large-radius jets and identified using a state-of-the-art jet tagging algorithm. This analysis presents the most sensitive limits for new spin-1 bosons coupling universally to quarks and spin-0 bosons coupling preferentially to heavier quarks. The invariant jet mass spectrum is probed for a potential narrow peaking signal over a smoothly falling background. Upper limits at 95% confidence level are set on the coupling of narrow resonances to quarks as a function of the resonance mass. For masses between 50 and 300 GeV, these are the most sensitive limits to date on all possible mediators. Using conventions on s-channel dark matter mediators, limits are set on dark photons and dark matter in the context of the relic density.

Spectroscopic study of emergent electronic phases in transition metal based compounds

Sun, 01 Sep 2024 00:00:00 GMT

Spectroscopic study of emergent electronic phases in transition metal based compounds Song, Qian Antiferromagnets with non-relativistic spin splitting are outstanding candidates as the next generation of spintronic materials owing to their electron-volt (eV) scale spin splitting, ultrafast spin dynamics and nearly vanishing stray fields. Achieving voltage-based control of spin polarization in antiferromagnets is of great interest for realizing energy-efficient and compact devices for information storage and processing. Spin spiral type-II multiferroics exhibit an inversion-symmetry-breaking antiferromagnetic order which directly induces ferroelectric polarization, allowing for symmetry protected cross-control between spin chirality and polar order. This intrinsic coupling between the magnetic and dipolar order parameters results in record-strength magnetoelectric effects. Two-dimensional materials possessing such intrinsic multiferroic properties have been long sought for harnessing magnetoelectric coupling in nanoelectronic devices. The recent discovery of intrinsic magnetic order in atomically-thin van der Waals (vdW) materials has created new opportunities for the study of collective spin phenomena in free-standing two-dimensional (2D) systems and nanoscale devices. Among possible multiferroic vdW materials, several families have been identified, and of particular promise is the magnetic semiconductor NiI₂. The multiferroic state of NiI₂ is characterized by a proper-screw spin helix with given handedness, which couples to the charge degrees of freedom to produce a chirality-controlled electrical polarization. We use a suite of optical technique which reveal an ordered magnetic, polar state that persists down to the ultrathin limit of monolayer NiI₂. Recent development of spin-group formalism has identified a new class of magnets with nontrivial spin textures, including even-parity d, g, or i-wave altermagnet and odd-parity p-wave antiferromagnets. The chiral magnetic order in NiI₂ breaks Inversion-Time-Reversal-Translation (P Tτ ) symmetry, and Spin-Rotation-Translation (Uτ ) symmetry, allowing for spin splitting even in the absence of spin-orbit-coupling (SOC). We provide direct evidence that the spin polarization in a spin spiral type-II multiferroic exhibits p-wave (odd-parity) character and directly couples to the spin chirality, enabling electrical control of non-relativistic spin splitting. Our findings represent the first observation of a p-wave antiferromagnet, and open a new frontier of voltage-based switching of non-relativistic spin splitting in vdW antiferromagnets.

Beyond Color: Lattice Gauge Theory for Strongly-Coupled Physics

Sun, 01 Sep 2024 00:00:00 GMT

Beyond Color: Lattice Gauge Theory for Strongly-Coupled Physics Oare, Patrick R. Quantum Chromodynamics (QCD) is the prototypical strongly interacting Quantum Field Theory (QFT). It is the interaction that yields the strong nuclear force that binds protons and neutrons together. The underlying mathematical picture of QCD is known exactly: it is an 𝑆𝑈(3) gauge theory coupled to six flavors of fermions (the quarks). Despite this, it remains difficult to compute QCD observables because QCD is strongly-coupled, and typical perturbative methods used in QFT only work in specific regimes of validity for QCD. The most successful ab initio method to study QCD is Lattice Gauge Theory (LGT). This computational formalism computes observables by discretizing spacetime to render the path integral tractable. The primary focus of LGT in the 40 years since its inception has been the study of QCD, as the theory has direct physical relevance to so much of our universe, and the desire to understand QCD has driven many conceptual breakthroughs and advancements in LGT. Despite the focus on QCD, lattice methods find significant utility in studying other strongly-coupled gauge theories related to and unrelated to QCD. This thesis will focus on applying LGT to strongly-coupled physics inside and outside of QCD and on developing techniques within LGT that may be used to better understand said theories. First, the spectral function reconstruction problem in LGT is considered, and a new method for spectral function reconstruction in LGT is presented. Spectral functions describe the energy states of a theory: bound states, resonances, and continuum thresholds. The presented reconstruction method uses the analytic properties of the retarded Green’s function to constrain the full set of spectral functions that may be reconstructed from LGT data using the Nevanlinna-Pick interpolation problem. Next, two theories will be numerically studied using LGT. The first is the Standard Model Effective Field Theory (SMEFT). The SMEFT process that is considered is neutrinoless double 𝛽 (0𝜈𝛽𝛽) decay, a hypothetical decay of two neutrons into two protons and two electrons. LGT is used to compute non-perturbative matrix elements for the unphysical 𝜋⁻→ 𝜋⁺𝑒⁻𝑒⁻ transition, which contributes to nuclear 0𝜈𝛽𝛽 decay, and for the decay of the dinucleon 𝑛⁰𝑛⁰ → 𝑝⁺𝑝⁺𝑒⁻𝑒⁻. Connections to Effective Field Theory studies of 0𝜈𝛽𝛽 decay will also be discussed. Finally, adjoint QCD (QCD₂), the theory of a Majorana fermion coupled to a 𝑆𝑈(𝑁) gauge field in the adjoint representation in 1+1 spacetime dimensions, will be studied using LGT. QCD₂ is a well-studied QCD-like theory whose properties have been crucial in the study of confinement. Lattice methods are used to compute the static quark potential, string tensions, and the low-lying spectrum of the theory, which will provide input that may be used to understand better QCD₂ and the confinement mechanism in general.

From Sample to Answer: Innovations in sample processing and CRISPR-based diagnostics for enhanced clinical translation and field deployment

Wed, 01 May 2024 00:00:00 GMT

From Sample to Answer: Innovations in sample processing and CRISPR-based diagnostics for enhanced clinical translation and field deployment Arizti Sanz, Jon The recent (re)emergence and rapid spread of infectious disease agents underscore the urgent need for effective disease prevention and control strategies. Accurate and timely diagnostics serve as the base for effective disease management, enabling the rapid identification of disease outbreaks, guiding treatment decisions, and informing public health interventions. However, the global diagnostic testing capacity is currently insufficient to effectively respond to emerging infectious disease threats, which has fueled the spread of Lassa, Ebola, SARS-CoV-2, and Zika virus in recent years. Globally, this diagnostic gap is particularly pronounced at the primary care or community level, an essential site for swift and effective response. Widespread, rapid, and user-friendly diagnostic tests are vital components of effective outbreak containment and response strategies, as they enable the rapid identification of new cases, thereby facilitating timely intervention and preventing further pathogen spread. Therefore, addressing the critical need in the global diagnostic testing infrastructure requires the development and deployment of diagnostic tools that are accurate, affordable, and accessible in decentralized settings. Existing diagnostics fall short in bridging the current diagnostic gap, but recent advances in nucleic acid-based technologies, and CRISPR-based diagnostics (CRISPR-Dx) in particular, have shown significant promise in transforming infectious disease detection. CRISPR-Dx are easily programmable, robust, sensitive, isothermal, and highly specific, but further advances will be required to facilitate their use outside of centralized laboratories. This thesis aims to address this critical gap in global diagnostic testing capacity, focusing on the innovation, validation, and deployment of CRISPR-Dx for infectious diseases. We first developed SHINE, a rapid and sensitive Cas13-based nucleic acid detection platform without the need for nucleic acid extraction. In this first version (SHINEv1), we simplified the CRISPR-Dx workflow, reducing user manipulations and assay time, and enabling automated interpretation of assay results using a companion smartphone application. Next, we made further improvements and thoroughly validated this platform to create SHINEv2, a further streamlined, equipment-free, and easily deployable technology with the ability to discriminate SARS-CoV-2 variants of concern (VOCs). Given the excellent programmability of CRISPR-Dx, we expanded the use of SHINE beyond SARS-CoV-2 to other clinically relevant pathogens. We developed and validated SHINE assays to detect and discriminate species, subtypes, and variants of influenza virus, with important implications for public health and clinical care. We also designed and tested multiplexed diagnostic assays for the detection and differentiation of three tick-borne pathogens in clinical samples. Finally, given the inadequacy of existing sample processing methods – and their importance to nucleic acid test deployment – we developed a high throughput experimental workflow to analyze the effects of chemical reagents on diagnostic assay performance and nuclease activity in patient samples using a commercially available microfluidic platform. Together, the research presented in this thesis contributes to the development of more effective, accessible, and field-deployable diagnostic solutions, thereby enhancing our ability to respond to the global burden of infectious diseases.

Total Synthesis of Verticillin A and Application of Diazene-Directed Fragment Assembly to the Synthesis of Heterodimeric Epidithiodiketopiperazine Derivatives

Sun, 01 Sep 2024 00:00:00 GMT

Total Synthesis of Verticillin A and Application of Diazene-Directed Fragment Assembly to the Synthesis of Heterodimeric Epidithiodiketopiperazine Derivatives Knauss, Walker I. Total Synthesis of (+)-Verticillin A We report the first total synthesis of (+)-verticillin A, completed in 16 steps. Our initial strategy of late-stage sulfidation on a dimeric substrate produced an undesired diastereomer of ETP. We were able to access an ETP with the desired diastereoselectivity by effecting sulfidation on an epimerized, monomeric substrate. In order to install a disulfide with the desired facial selectivity, we developed a stepwise sequence involving stereoselective formation of a C15-benzhydryl disulfide followed by intramolecular sulfidation at C11. Because ETPs are unstable to carboncentered radicals and irradiation with UV light, we developed conditions to reduce the disulfide and protect the resulting thiols as alkylsulfides prior to cobalt reductive dimerization and photochemical desulfonylation. Finally, deprotection of the thiols and oxidation delivered the ETP natural product (+)-verticillin A. II. Synthesis of Heterodimeric ETP Derivatives Using Diazene-Directed Fragment Assembly We report the development of a novel route to heterodimeric ETP derivatives using diazenedirected fragment assembly. This is the first application of diazene-directed coupling to the synthesis of dimeric diketopiperazine alkaloids Our group’s initial route to heterodimeric ETP derivatives relied upon reductive cobalt dimerization, which produces a nearly statistical mixture of homo- and heterodimeric products. In contrast to the initial route, the diazene-based approach disclosed herein enables selective heterodimerization. To demonstrate the utility of heterodimeric ETP derivatives, we have synthesized an ETP-diazirine photoaffinity labelling probe, which we hope can be used to study the interactions of ETPs with cellular targets.; II. Synthesis of Heterodimeric ETP Derivatives Using Diazene-Directed Fragment Assembly We report the development of a novel route to heterodimeric ETP derivatives using diazenedirected fragment assembly. This is the first application of diazene-directed coupling to the synthesis of dimeric diketopiperazine alkaloids Our group’s initial route to heterodimeric ETP derivatives relied upon reductive cobalt dimerization, which produces a nearly statistical mixture of homo- and heterodimeric products. In contrast to the initial route, the diazene-based approach disclosed herein enables selective heterodimerization. To demonstrate the utility of heterodimeric ETP derivatives, we have synthesized an ETP-diazirine photoaffinity labelling probe, which we hope can be used to study the interactions of ETPs with cellular targets.

Explorations in two dimensional strongly correlated quantum matter: from exactly solvable models to conformal bootstrap

Sun, 01 Sep 2024 00:00:00 GMT

Explorations in two dimensional strongly correlated quantum matter: from exactly solvable models to conformal bootstrap Jones, Robert A. This dissertation presents two projects that touch upon the role of quantum mechanics in classifying phases of matter and their transitions. In the first project, we set out to answer: is it possible to find a lattice model in the Ising universality class that realizes the Kramers Wannier symmetry in such a way that it squares to 1, rather than a lattice translation as in the usual Ising model? Using insights from symmetry-protected topological phases of matter, we answer in the affirmative, with the caveat that the symmetry, beyond being non-onsite, actually acts on a Hilbert space that is not a local tensor product. The second concerns the nature of the Neel-VBS deconfined quantum critical point. This is thought to be described by the noncompact CP¹ model, which we argue to be continuously connected to the theory accessed by the 2 + ε expansion for the O(3) NLSM. To shed light on the nature of the DQCP, we perform conformal bootstrap studies of the O(3) model in 2 < d < 3.

Squeezing the Quantum Noise of LIGO below the Standard Quantum Limit

Sun, 01 Sep 2024 00:00:00 GMT

Squeezing the Quantum Noise of LIGO below the Standard Quantum Limit Jia, Wenxuan The year 2015 marked the first detection of a gravitational wave signal from a pair of black holes located 410 megaparsecs (1.3 billion light-years) away. Their merger unleashed an immense amount of energy, with the peak emission rate surpassing the combined power of all luminous stars in the observable universe. Unlike stars, the merger of two black holes does not emit electromagnetic radiation like visible light but instead illuminates the universe with gravitational radiation. These waves traveled freely for over a billion years before being captured by the twin Laser Interferometer Gravitational-Wave Observatory (LIGO) detectors. Upon reaching Earth, these waves caused a minuscule length change between the LIGO mirrors, on the order of 10^(−18) m, a thousand times smaller than a proton. The unprecedented sensitivity of LIGO requires an extremely low noise level. The design of LIGO as an interferometer converts the gravitational-wave signal to an optical signal, which is measured on photodiodes along with other noises. One of the noise sources is the quantum noise due to the quantum vacuum fluctuations of the light itself. Besides the light, the mirror also has quantum-mechanical features and experiences quantum back-actions when we probe it with light. Knowing the position of the mirror very well would inevitably perturb its momentum, which prevents us from precisely making the next measurement of the position. This is fundamental physics dictated by Heisenberg’s uncertainty principle. In the case of continuous measurement like LIGO, the quantum back-action leads to an apparent sensitivity limit known as the Standard Quantum Limit (SQL). It tells us how precisely we can measure an object with light. The SQL applies when using uncorrelated photons or coherent light to measure the object, such as a laser beam. However, introducing quantum correlations through squeezed light, a technique called squeezing (Chapter 2), can circumvent this limit. Squeezed vacuum, a non-classical light state, exploits quantum correlations between photon pairs to reduce vacuum fluctuations in one quadrature at the cost of another. By manipulating the quantum correlation between light and the mirror, the squeezed vacuum can potentially reduce quantum noise below the SQL, a concept explored in frequency-dependent squeezing. This thesis develops a first-principle model of quantum noise in LIGO (Chapter 3) and investigates how squeezing can mitigate it while considering practical factors like optical losses and mode-mismatch (Chapter 4). These theories are constructed with a bottom-up approach. Experimental details on generating and utilizing frequency-dependent squeezing for LIGO are also discussed (Chapter 5), culminating in the observation of LIGO’s quantum noise below the SQL (Chapter 6). Besides squeezing, increasing optical power can also reduce quantum shot noise. Nevertheless, maintaining high power levels (fractions of megawatts) in LIGO is challenging due to experimental imperfections, such as unintended point absorbers on the mirror coating. This thesis analyzes the thermoelastic distortions caused by these absorbers, which limit achievable optical power in current and future gravitational-wave detectors (Chapter 7).

Ultrafast dynamics in quantum materials probed by time-and-momentum-resolved techniques

Sun, 01 Sep 2024 00:00:00 GMT

Ultrafast dynamics in quantum materials probed by time-and-momentum-resolved techniques Su, Yifan The interactions of quasiparticles in quantum materials give rise to intriguing phenomena, including magnetism and superconductivity. However, these interactions are often challenging to understand due to the intertwining of multiple degrees of freedom, such as charge, spin, orbital, and lattice. To fully understand such strongly correlated systems, a suite of experimental techniques that respectively probes various degrees of freedom and simultaneously resolves multiple channels, including energy, momentum, time, and space, are highly desired. This poses a significant challenge for the entire community. In this dissertation, I will focus on a series of experiments performed on quantum material systems utilizing several multi-resolution techniques. Ultrafast electron diffraction (UED) and time-and-angle-resolved photoemission spectroscopy (trARPES) are tools that I co-developed with my colleagues at MIT in the past several years. Supplemented by the time-resolved X-ray diffraction (trXRD) setup at free electron laser facilities around the world, they provide direct access to lattice (UED and trXRD) and electronic (trARPES) structures in quantum materials on an ultrafast timescale of a few hundred femtoseconds. The first part of the dissertation will briefly introduce assorted aspects of ultrafast phenomena as well as the fundamental principles and instrumentation of the several time-andmomentum-resolved techniques. Following the introduction to these time-and-momentumresolved techniques, the second part of the thesis focuses on the coherent acoustic phonons in quantum materials observed with UED. The crystalline lattice is the building block of any solid-state system and, thus, the most important aspect in condensed matter physics research. The study of coherent acoustic phonons, the fundamental coherent excitation of the lattice, could be traced back to the 1980s when solid-state ultrafast lasers were first developed. However, the knowledge about the excitation mechanism was not complete. In this part of the thesis, I will introduce a new pathway for launching coherent acoustic phonons: magnetostriction, and discuss the spin-mediated shear oscillator enabled by this mechanism in van der Waals antiferromagnet. I will further discuss the original methodology I developed that uses coherent acoustic phonon detected with UED as a picosecond timescale "lock-in" experiment that senses nano-scale mechanical motions in ultra-thin quantum materials. The last part of the dissertation will focus on charge density wave (CDW) phase transitions in quantum materials. CDWs are systems where strong interplays between electrons and phonons drive the phase transition that causes the modulation of charge density and is thus accompanied by periodic lattice distortions. In this dissertation, I will focus on systems with multiple interacting CDW orders. These systems are ideal platforms for studying the interplays among multiple order parameters. The suite of probes, including UED, trXRD, and trARPES, offers a comprehensive view of CDW systems from both phononic and electronic perspectives. This part of the thesis will examine a series of CDW materials with multiple CDW orders, including ErTe₃, EuTe₄, and, CsV₃Sb₅. Via a series of ultrafast multi-messenger experiments, I will survey various origins and behaviors of CDW interactions and answer longstanding questions about the nature of CDW ground states in these quantum materials. The overarching theme of this dissertation is to establish a paradigm of problem-solving in quantum materials research via a combination of multiple channels acquired from a suite of ultrafast momentum-resolved techniques. Coherent phonons and CDW systems are two of the richest playgrounds in the ultrafast regime. I am going to investigate various cases where an ultrafast laser pulse decodes the intertwined degrees of freedom in quantum materials. The insight developed in these case studies may be carried over to other quantum material systems with emergent quantum states, such as superconductivity and magnetic orders.

Ultrafast Terahertz Spectroscopy for the Manipulation and Elucidation of Correlated Quantum Materials

Sun, 01 Sep 2024 00:00:00 GMT

Ultrafast Terahertz Spectroscopy for the Manipulation and Elucidation of Correlated Quantum Materials Allington, Clifford Light-matter interactions are at the heart of quantum mechanics. The photoelectric effect, blackbody radiation, and the hydrogen emission spectrum were all experimental observations using light and its interaction with matter which led to the discovery of the quantum mechanical nature of the universe. In modern research, the interactions of light with matter plays a significant role in both understanding the properties of, or controlling various aspects of quantum materials, a class of materials whose macroscopic properties are only understood through quantum mechanics. Quantum materials are often categorized into two classes: topological materials or strongly correlated materials, though the cross-over and interplay between these two aspects is a significant field of study as well. Strongly correlated materials exhibit exotic physical phases such as magnetism, superconductivity, or heavy fermion formation due to the strong interactions of electrons. Many of these properties hold significant promise for application, yet the ability to predict correlated physics from a theoretical standpoint is still at a young stage of development. To this end, experimental efforts to demonstrate and understand the interplay between different degrees of freedom in a material (spin, charge, lattice, and orbital) are essential for progressing in this direction. In this thesis, a variety of light-matter interactions using ultrafast techniques are explored in a set of quasi two-dimensional strongly correlated materials. These are bulk materials, whose properties are strongly founded in the two-dimensional layers stacked on top of one another. A variety of Optical-Pump Terahertz-Probe spectroscopic methods are used to drive a system out of equilibrium while monitoring the low-energy physics in the terahertz (THz) spectral range. This part of the electromagnetic spectrum is essential to understanding many aspects of strongly correlated physics. For example, the charge carriers in a metallic (or photoexcited) material have a strong spectral weight here and many of the collective modes of insulating phases, such as phonons or magnons, occur at these energies as well. Specifically, the collective modes of two van der Waals antiferromagnets are excited coherently with the use of ultrafast optical pulses. In the antiferromagnet NiPS3 a new mechanism for launching a coherent magnon is discovered. In the multiferroic, antiferromagnet NiI2, evidence for a new type of quasiparticle, an electromagnon-polariton, is demonstrated in a non-equilibrium sample. Further, preliminary data regarding the measurement of a new type of Kondo hybridization gap (a pseudogap) in the kagome strange metal Ni3In is reported using the photoexcited dynamics and the Rothwarf-Taylor bottleneck model.

Instrumental Effects in 21 cm Cosmology: One-point Statistics and Power Spectrum with the HERA Interferometer

Sun, 01 Sep 2024 00:00:00 GMT

Instrumental Effects in 21 cm Cosmology: One-point Statistics and Power Spectrum with the HERA Interferometer Kim, Honggeun The epoch of reionization (EoR) signifies a critical phase in the universe’s evolution, marking the shift from a predominantly neutral intergalactic medium to the ionized state observed today. A key aspect of studying the EoR involves observing the redshifted 21 cm line emission with radio telescopes. A significant challenge in this endeavor is isolating the faint 21 cm signals from bright foreground emissions and systematics. This collection of works focuses on understanding the impact of instrumental systematic effects on statistical measurements, such as the one-point statistics and power spectrum, using the Hydrogen Epoch of Reionization Array (HERA). First, I investigate one-point statistics measured from image cubes based on HERA Phase I observations after foreground removal for the first time. I highlight the influence of systematics on these measurements, by measuring the second and third moments. These analyses show that, despite efforts to mitigate systematics, the residual systematics still cause deviations in the measurements from the expected values. In addition, I evaluate EoR models against observational data, suggesting the second moment measurements likely reject the cold reionization model characterized by inefficient X-ray heating. The third moment, which captures non-Gaussianity features of the signals, is significantly diminished by the instrument response and further reduced by the foreground removal process, making it challenging to probe non-Gaussianity. However, there remains the potential to detect some skewness at low redshifts. One potential systematic for HERA involves calibration errors stemming from per-antenna perturbations due to feed misalignment. I have simulated these calibration errors by modeling realistic perturbed primary beams for HERA Phase II observations. The chromatic calibration errors are critical since they can cause foreground emission to contaminate the region of Fourier space expected to be dominated by cosmological signals. I then present the work focused on developing a method to mitigate the calibration errors and foreground leakage, thereby recovering the clean EoR window.

Optical and core-level X-ray spectroscopy of correlated two-dimensional materials

Sun, 01 Sep 2024 00:00:00 GMT

Optical and core-level X-ray spectroscopy of correlated two-dimensional materials Occhialini, Connor Alexander The intersection of low-dimensionality and strongly correlated electrons in van der Waals (vdW) materials offers a rich landscape of ordered phases and associated excitations for potential applications in nanoelectronics. The coupling between distinct degrees of freedom in correlated materials provide routes to realize novel functional properties, which can be further manipulated by the high tunability intrinsic to vdW materials through, e.g., heterostructures and doping. However, identifying the mechanism of correlated phases poses a fundamental challenge due to coexistent and competing orders. This requires detailed knowledge of the microscopic interactions/excitation spectra, methods to disentangle the individual roles of coexistent orders, and selective probes of symmetry-breaking within different coupled degrees of freedom. In this thesis, we demonstrate the utility and complementarity of resonant X-ray spectroscopy and symmetry-selective optical probes in combination with appropriate external tuning parameters (e.g. strain, pressure, ligand substitution, layer number) for revealing the origin of correlated phases in low-dimensional vdW materials. We first investigate the triangular lattice antiferromagnet NiI₂. Frustrated exchange interactions result in a helimagnetic ground state and spin-induced ferroelectric order, making bulk NiI₂ a type-II multiferroic. Using a combination of optical spectroscopic probes, including Raman, magneto-optics, and second harmonic generation, we demonstrate the persistence of multiferroic order to the single-layer limit. We then aim to resolve the microscopic magnetic interactions and their interplay with the lattice symmetry to identify the origin of the magnetic ground state. Towards this goal, we investigate the magnetic ground state and transition temperature versus hydrostatic pressure and layer number, and directly probe the evolution of magnetic/structural orders with resonant magnetic X-ray scattering/structural diffraction, respectively. From these results, we demonstrate the central role of interlayer exchange interactions and their coupling to the structural symmetry in driving the magnetic ground state of NiI₂. We next investigate the broader class of triangular lattice nickel dihalides, NiX₂ (X = Cl, Br, I), to identify the origin of sharp optical excitations, i.e. excitons, in nickel-based vdW magnets. We employ Ni-L₃ edge resonant inelastic X-ray scattering (RIXS) to access a q-resolved and site-specific view into the excitation spectra. We identify the sharp excitons with spin-forbidden intra-configurational multiplets of octahedrally-coordinated Ni²⁺, which become renormalized by Ni-X charge transfer. We also observe a finite dispersion of these excitations, demonstrating a multiplet delocalization that is controlled by the ligand-tuned charge transfer gap in a process analogous to ground state superexchange. These results establish the microscopic origin of these excitons and provide a mechanism to explain their possible coupling to the magnetic order/excitations. Finally, we study the iron-based superconductor FeSe, which displays a rotational symmetry breaking electronic nematic phase in proximity to unconventional superconductivity without magnetic order. To understand the origin of nematicity, we investigate the ordering of the orbital degrees of freedom using X-ray linear dichroism with in-situ uniaxial strain tuning, electronic transport measurements and structural diffraction. We observe a lattice-independent orbital polarization acting as the primary nematic order parameter. This resolves the orbital origin of nematicity in FeSe and suggests that anisotropic spin fluctuations are the mechanism of unconventional superconductivity.

Collagen-mimetic peptides for diagnosis and analysis

Sun, 01 Sep 2024 00:00:00 GMT

Collagen-mimetic peptides for diagnosis and analysis Borgula, Isabella M. Collagen, the most abundant protein in the human body, is an essential scaffold for tissue development, regulation, and homeostasis. As a major structural component of the extracellular matrix, collagen is not static. Rather, it is highly diverse and dynamic, actively participating in tissue physiology. Collagen can be a challenging protein to study due to its massive size and heterogeneity across subtypes. A valuable tool to study and better understand collagen is a technology known as collagen-mimetic peptides (CMPs), which are synthetic peptides that mimic the natural structure of collagen. These peptides can be applied to study collagen structure and function, from its macromolecular architecture in tissues to the significance of molecular modifications on its amino acid sidechains. This thesis explores the application of CMPs in diagnostic applications, in which CMPs detect aberrations in native collagen, and analytical contexts, in which CMPs act as a simplified system to understand collagen biochemistry. Chapter 2 investigates the ability of CMPs to identify collagen remodeling in a mouse model of pulmonary fibrosis, demonstrating their potential as non-invasive diagnostic tools for fibrotic diseases. Chapter 3 analyzes the collagen-rich desmoplastic reaction surrounding PDAC in murine models and human samples, highlighting the utility of CMPs in characterizing tumor microenvironments. Finally, Chapter 4 examines the structural implications of threonine phosphorylation on collagen stability, showcasing the value of CMPs in studying posttranslational modifications. The findings discussed in this thesis lay a foundation for future CMP applications in targeted drug delivery and biomaterials design.

Precision Metrology with Ytterbium Ions for New Physics Search

Sun, 01 Sep 2024 00:00:00 GMT

Precision Metrology with Ytterbium Ions for New Physics Search Kniazev, Evgenii Modern physics faces a growing discrepancy between the success of the Standard Model and the body of evidence pointing to the New Physics beyond it. A powerful method of New Physics searches is using quantum sensing tools based on Atomic, Molecular, and Optical physics. In particular, the modern optical atomic clocks demonstrate unprecedented accuracy and precision. Complementary to high-energy searches with particle colliders, the atomic clocks are used to place stringent bounds on tests of fundamental physics. One of the possible candidates for physics beyond the Standard Model is a carrier of a fifth force. Such a hypothetical particle that mediates interactions between leptons and quarks can potentially be detected in a tabletop atomic clock experiment. In particular, the isotope shift measurements may show sensitivity to coupling induced by such particles. In this thesis, we describe the efforts to place bounds on this particle using isotope shifts of optical transitions in Ytterbium. We conduct the isotope shift experiment by measuring ions one at a time and in a co-trapped configuration following the protocol of correlation spectroscopy. We study the systematic uncertainty budget for both types of measurements. We apply the King plot method to isotope shift spectroscopy data and observe the King nonlinearity. Using the analysis of the nonlinearity patterns, we determine the significance of the second source of the King nonlinearity with a currently unknown source.

Emergence, Formation and Dynamics of Hot QCD Matter

Sun, 01 Sep 2024 00:00:00 GMT

Emergence, Formation and Dynamics of Hot QCD Matter Scheihing Hitschfeld, Bruno Sebastian Understanding the dynamics of Quantum Chromodynamics (QCD) in quantitative detail is one of the main frontiers in particle physics. While the last century gave us the formulation of the theory of nuclear interactions, QCD, as well as that of the rest of visible matter encoded in the Standard Model of Particle Physics, much remains to be understood. In particular, the hot QCD matter produced in high energy collisions of heavy ions presents a unique challenge to theory and phenomenology due to the vast number of different phenomena that take place in such a collision, and even more so because it is an out-of-equilibrium process. In this thesis, we make progress in two concrete directions in the vast landscape of hot QCD physics. The first one is quarkonium transport inside quark-gluon plasma (QGP), the high temperature phase of QCD. Over the past two decades it has been realized that a significant fraction of quarkonium suppression in high energy heavy ion collisions comes from dynamic dissociation and recombination processes, instead of static screening of the interaction potential as originally proposed by Matsui and Satz. Our contribution is the formulation of the precise correlation functions in QCD at finite temperature that describe the dissociation and recombination processes of heavy quarkonium in QGP, as well as their calculation in weakly coupled QCD and strongly coupled N=4 supersymmetric Yang-Mills theory. We also formulate the Euclidean version of these correlation functions so that they may be calculated using Lattice QCD techniques. In this way, our results provide the necessary ingredients to carry out an analysis of the suppression of ϒ states in heavy ion collisions in terms of the parameters of the QCD lagrangian. The second contribution we make is the development of tools to understand the process of hydrodynamization in QCD kinetic theory and their application to a simplified description where only a subset of the QCD scattering mechanisms are included. By doing this, we learn that the process of hydrodynamization in this theory, and specifically, how memory of the initial condition is lost, follows the recently proposed Adiabatic Hydrodynamization scenario. Concretely, hydrodynamization proceeds through a sequential process in which a monotonously shrinking set of low-energy states dominate the dynamics, where the opening of an energy gap relative to the ground state(s) signals the start of each stage of this process. The hydrodynamic attractor is reached when only one low-energy state remains as the ground state, and the system approaches local thermal equilibrium following the adiabatic evolution of this low-energy state.

Magnetooptical studies of small-gap semiconductors : Hg₁₋ Cd Te and InSb

Sat, 01 Jan 1977 00:00:00 GMT

Magnetooptical studies of small-gap semiconductors : Hg₁₋ Cd Te and InSb Weiler, Margaret Horton. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1977; Vita.; Includes bibliographical references.

Transcription of the adenovirus genome during productive infection of HeLa cells.

Sat, 01 Jan 1972 00:00:00 GMT

Transcription of the adenovirus genome during productive infection of HeLa cells. Price, Richard Pearsall. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biology, 1972; One unnumbered leaf inserted.; Includes bibliographical references.

The action of colocins E1 and K on proline transport in isolated membrane vesicles of E. coli.

Fri, 01 Jan 1971 00:00:00 GMT

The action of colocins E1 and K on proline transport in isolated membrane vesicles of E. coli. Kabat, Jonathan Peter. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biology, 1971; Seventeen unnumbered leaves inserted. Vita.; Bibliography: leaves 105-107.

Mechanisms of fatigue crack initiation and propagation in an age hardenable aluminum alloy.

Thu, 01 Jan 1970 00:00:00 GMT

Mechanisms of fatigue crack initiation and propagation in an age hardenable aluminum alloy. Erhardt, Karl Edward. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Metallurgy and Materials Science, 1970; Vita.; Includes bibliographies.

Symplectic fibrations and weight multiplicities of compact groups

Sun, 01 Jan 1989 00:00:00 GMT

Symplectic fibrations and weight multiplicities of compact groups Lerman, Eugene. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1989; Includes bibliographical references (p. 71-72).

Activation of the c-Ha-ras oncogene

Sun, 01 Jan 1984 00:00:00 GMT

Activation of the c-Ha-ras oncogene Tabin, Clifford James. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biology, 1984; Bibliography: leaves 202-214.

Magneto-optical studies in In[subscript 1-x]Ga[subscript x]As[subscript y]P[subscript 1-y] semiconducting alloys

Thu, 01 Jan 1981 00:00:00 GMT

Magneto-optical studies in In[subscript 1-x]Ga[subscript x]As[subscript y]P[subscript 1-y] semiconducting alloys Alavi, Kambiz. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 1981; Vita.; Includes bibliographical references.

Risk analysis for earthquake-induced ground failure by liquefaction.

Thu, 01 Jan 1976 00:00:00 GMT

Risk analysis for earthquake-induced ground failure by liquefaction. Yegian, Mishac K. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Civil Engineering, 1976; Bibliography: leaves 281-292.

Photosynthetic regeneration of ATP using native and immobilized bacterial chromatophores.

Thu, 01 Jan 1976 00:00:00 GMT

Photosynthetic regeneration of ATP using native and immobilized bacterial chromatophores. Yang, Ho Seung. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nutrition and Food Science, 1976; Vita.; Includes bibliographies.

Pressure broadening of infrared absorption lines at moderate densities.

Thu, 01 Jan 1976 00:00:00 GMT

Pressure broadening of infrared absorption lines at moderate densities. Wormhoudt, Joda Cornelius. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemistry, 1976; Vita.; Includes bibliographical references.

Surface Friction and Spectroscopic Probes of New Physics with Trapped Ions

Fri, 01 May 2020 00:00:00 GMT

Surface Friction and Spectroscopic Probes of New Physics with Trapped Ions Counts, Ian T. To trap a single ion under vacuum is to control a microscopic, isolated quantum laboratory. This thesis describes two research programs made possible by single-ion trapping of Yb⁺. The first program is a study of a paradigmatic frictional interface: a trapped ion transported along a one-dimensional multistable energy landscape formed by a periodic, corrugated optical potential and a harmonic electric trapping potential. Two regimes of friction behavior are differentiated: single-slip (whereby an ion slips out of a corrugated groove and sticks into its neighboring groove) versus multislip (whereby the ion instead sticks into its next-neighboring or next-next-neighboring groove). By varying transport speed and corrugation depth, experimental signatures of both regimes are measured and used to write a predictive Boltzmann model. At low enough corrugations, the ion can be expected to tunnel through (in addition to slip over) the potential barriers of the energy landscape, leading to a reduction in friction termed quantum lubricity. Attempts at seeing quantum tunneling via static Rabi oscillations are described. While no smoking-gun signature of tunneling was repeatable, the suppression of quantum tunneling behavior is attributed to certain technical limitations of the experiment apparatus, and possible remedies are considered. The second (and largest) research program of this thesis is a probe of new physics via isotope shift spectroscopy. Shift measurements are taken to sub-kHz precision across all five even-numbered isotopes of Yb+ along three clock transitions (quadrupolar shifts S₁/₂ → D₅/₂, S₁/₂ → D₃/₂, and octupolar shift S₁/₂ → F₇/₂). Deviations from theoretical predictions have been found and indicate higher-order Standard Model effects or even beyond-the-Standard-Model physics. Spectroscopic design and shift results, as well as possible theoretical conclusions, are discussed.

System-level design of low-carbon structures

Wed, 01 May 2024 00:00:00 GMT

System-level design of low-carbon structures Fang, Demi L. “What is more likely to be associated with a reduction in emissions: switching from concrete to timber, or shortening the spans throughout the building?” While such insights are valuable for mitigating emissions from structural systems during early stages of design, it is difficult to answer these types of questions in current paradigms of performance-driven design. This dissertation makes several original contributions to the system-level design of low-carbon structures. First, a literature-supported network of strategies available to reduce emissions during early-stage structural design is established and evaluated on the bases of literature availability, impact, implementability, and compatibility. Material efficiency and material choice represent two key levers for reducing emissions in structural design, but it is difficult to navigate trade-offs between these strategies at a system level of structural design. Holistic design strategies can help achieve this, but these current paradigms of performance-driven design (e.g. deploying rules of thumb, comparing a few design options, and optimization) are limited in their capacity to inform decision-making towards higher performing designs. There is a particular opportunity to produce these insights using data-driven approaches given the growing quality and quantity of data in the field of low-carbon structural design. In response, this dissertation analyzes both types of data that are available in the field: wild data (measured from the industry) and synthetic data (produced from bottom-up parametric structural models). Data from over 200 fully designed structural systems from a structural engineering firm are analyzed. This analysis is the first to 1) provide empirical evidence for floors and foundations representing the largest opportunities for carbon reductions and 2) evaluate the relationship between structural material quantities and embodied carbon in structural systems (many analyses evaluate the latter without the former). In a field where material choice is a predominant impression for reducing emissions, these new insights importantly affirm the prominent role of material efficiency in reducing a structural system’s emissions. While the design space of wild data includes a diverse variety of projects, leveraging a synthetic dataset computed from a bottom-up parametric model helps produce insights specific to the design problem at hand. The final contribution of this dissertation is to propose a computational framework that leverages synthetic data to empower decision-making in design. The framework addresses two challenges: 1) the challenge of extracting decision-making insights from design data, and 2) the challenge of comparing decision-making across continuous (numerical) and categorical variables, which are typical in most design problems. In this framework, a machine learning model is trained on a provided set of design data to compute gradients across the design space. These gradients are distilled into “influence metrics”, which offer a novel, accessible way to build and supplement intuition on low-carbon design decisions. A few case studies in low-carbon structural design are presented to demonstrate the use of the proposed method with synthetic datasets. By striking a meaningful balance between applying rules of thumb and optimization, the method empowers a paradigm shift from performance-driven design to performance-informed, human-driven design. Key words: embodied carbon of structural systems, design decision-making, low-carbon structural design

Self-similar singularity formation and wellposedness theory for compressible fluids and dispersive PDE

Sun, 01 Sep 2024 00:00:00 GMT

Self-similar singularity formation and wellposedness theory for compressible fluids and dispersive PDE Cao Labora, Gonzalo In this thesis, we study different problems related to singularity formation and local wellposedness of fluid equations and dispersive PDE. Regarding singularity formation, we construct radially symmetric smooth selfsimilar profiles for the compressible Euler equations which exhibit an implosion type singularity in finite time. This will be the first part of the thesis. The second part of the thesis consists on doing a non-radial stability analysis around those profiles to show singularity formation for adequate small perturbations of the profile. In particular, this stability analysis also allows to conclude existence of singularities for periodic initial data. The stability also allows to obtain singularity formation for the corresponding equation with dissipation: the compressible Navier-Stokes equations. Moreover, the self-similar profiles constructed are also intimately related to dispersive equations, and we will show how to use them to prove finite time singularity formation for some supercritical defocusing NLS equations, using its hydrodynamical formulation. The third part of the thesis consists of the study of a different dispersive equation: the Zakharov– Kuznetsov equation. The equation is a generalization of the KdV equation to higher dimensions with applications in plasma physics. We improve the deterministic local wellposedness in the cyilnder both in the deterministic and the probabilistic setting.

The Contract, the Contractor, and the Capitalization of American Building

Sun, 01 Sep 2024 00:00:00 GMT

The Contract, the Contractor, and the Capitalization of American Building Spencer, Chelsea Anne The heroic claims of twentieth-century architects notwithstanding, modern American architecture was built by general contractors. This new type of builder was unknown to US Americans before the Civil War, but by the turn of the twentieth century they commanded a powerful position in the widening gulf between architects and the construction of their buildings. Operating at the critical inflection point between projection and materialization, paper and concrete, contractors appealed to investment-minded clients as fellow businessmen, offering them what neither craft builders nor professional architects could deliver: a completed building, for a fixed price, on a guaranteed schedule. This dissertation tells the story of how building became contracting in the United States during the long nineteenth century. Known to legal historians as the age of contract, the nineteenth century gave rise to a constellation of juridical and economic ideas that revolved around a vision of social relations modeled on market exchange and possessive individualism. Revealing the ideological and institutional foundations of today’s construction industry, the dissertation shows how nineteenth-century thinking about contract, freedom, value, and risk shaped the architectural building contract, the limits of the architecture profession, the practice of general contracting, and thus the modern relationship between architecture and building.

Modeling technology pathways and retrofit adoption to achieve city-wide building emissions reduction goals

Wed, 01 May 2024 00:00:00 GMT

Modeling technology pathways and retrofit adoption to achieve city-wide building emissions reduction goals Berzolla, Zachary M. Achieving net zero emissions from buildings by 2050 is an unprecedented challenge that will require an all-in effort at local, state, federal, and international levels. The exact path to reach this goal in existing buildings varies widely from one community to another. Thus local planning efforts and a bottom-up approach is needed to attain emissions reduction goals. This dissertation lays out a framework to create technology pathway roadmaps to help cities around the world identify actionable strategies to achieve their building emissions reduction goals. These “technical potential” roadmaps can help policymakers quantify the exact requirements in terms of retrofits, workforce, and material to attain their end goals. The application of these tools in 24 cities around the world are discussed. A sound roadmap is only as good as its implementation, and currently retrofit rates lag what is necessary to achieve 2050 goals on time. One of the oft-cited barriers to retrofit adoption is the high upfront cost. This dissertation documents a survey carried out by the author and the resulting model used to help quantify households’ willingness to pay for retrofits. Leveraging the willingness to pay model enables policymakers to analyze the techno-economic pathways to their goals. Finally, one of the greatest challenges to achieve emissions reduction goals is the timeline of retrofit adoption. Under the current business as usual retrofitting rate, less than a fifth of the building stock will be retrofitted by 2050. To help policymakers grasp this temporal challenge, this dissertation introduces a novel application of technology diffusion models that can quantify retrofit adoption over time. The tools developed in this dissertation are aimed at providing communities of all sizes with data-driven insights to meet their ambitious but necessary building-related decarbonization goals in a timely manner.

From Words to Worlds: Bridging Language and Thought

Sun, 01 Sep 2024 00:00:00 GMT

From Words to Worlds: Bridging Language and Thought Wong, Lionel Catherine What do we understand when we understand language? Human language offers a broad window into the landscape of our thoughts. We talk about what we see, believe, and imagine, posing questions and communicating our plans. Language, in turn, stocks our mental inventories with new concepts and theories, communicating ideas that we might not otherwise have discovered by thinking on our own even over the course of a lifetime. How do we make meaning from language, and how, in turn, does the meaning we construct from language draw on the other resources and capacities of human thought, from perception, to mental simulation and decision making? This thesis proposes a computational framework for modeling language-informed thinking, organized into two parts. In the first, I overview the overarching framework that makes up the backbone of this thesis, Rational Meaning Construction, which proposes how natural language can construct arbitrary expressions in a flexible, symbolic, and probabilistic language of thought that supports general inferences. I present examples and experiments demonstrating the range of this theory, modeling how concrete propositions and questions in language can update and query beliefs about many different domains of knowledge. In the second section, I turn to language that communicates more abstract conceptual knowledge – generic background concepts and theories that we can learn from language, and which give us building blocks for representing more concrete beliefs. I present three models that build on the basic premises of Rational Meaning Construction to learn new lexical concepts and theories from language. The first models how we can learn new theories from generic sentences that explicitly communicate or implicitly presuppose abstract knowledge. The second elaborates on this model to also incorporate environmental feedback alongside information from language. The third suggests how we can learn the meanings of new words from scratch, with very little linguistic data, using principles of both representational and communicative efficiency to guide learning. I conclude by discussing a open questions that this thesis raises about how we learn and understand language, and outline future directions that might make progress on answering them.

Low temperature deformation of copper single crystals oriented for multiple slip

Wed, 01 Jan 1964 00:00:00 GMT

Low temperature deformation of copper single crystals oriented for multiple slip Saimoto, Shigeo. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Metallurgy, 1964; Vita.; Includes bibliographical references.

Sobolev tests for uniformity on compact Riemannian manifolds,

Mon, 01 Jan 1973 00:00:00 GMT

Sobolev tests for uniformity on compact Riemannian manifolds, Giné, Evarist, 1944- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 1973; Vita.; Includes bibliographical references.

The effects of taxing unemployment benefits

Fri, 01 Jan 1999 00:00:00 GMT

The effects of taxing unemployment benefits Ellis, W. Philip. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1999; "September 1999."; Includes bibliographical references.

What makes the money go round?

What makes the money go round? Rosenblat, Tanya, 1971- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, c1999; Includes bibliographical references (p. 129-133).

Essays on the economics of work and family

Essays on the economics of work and family Johnson, John H. (John Henry), 1973- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, c1999; Includes bibliographical references (p. 117-120).

Essays on international macroeconomics : the real exchange rate, income inequality and the international investment position of small countries

Essays on international macroeconomics : the real exchange rate, income inequality and the international investment position of small countries García, Pablo S. (Pablo Silva), 1970- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, c1999; Includes bibliographical references.

Essays on crises

Essays on crises Dudek, Maciej Konrad, 1971- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, c1999; Includes bibliographical references.

Social security, pensions, and the retirement decisions of individuals and couples

Social security, pensions, and the retirement decisions of individuals and couples Coile, Courtney. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, c1999; Includes bibliographical references.

Repeated games with private information

Repeated games with private information Amarante, Massimiliano, 1966- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, c1999; Includes bibliographical references (leaves 56-59).

The information content of asset prices and emerging market crises

The information content of asset prices and emerging market crises Aguiar, Mark. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, c1999; Includes bibliographical references (p. 91-95).

Essays on market integration and productivity

Essays on market integration and productivity Park, Charles C., 1968- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, February 1998; Includes bibliographical references (leaves 110-112).

Three essays on search and bargaining models

Fri, 01 Jan 1999 00:00:00 GMT

Three essays on search and bargaining models Dasgupta, Sugato. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1999; Includes bibliographical references (leaves 92-94).

Essays on information production and information use in financial markets

Thu, 01 Jan 1998 00:00:00 GMT

Essays on information production and information use in financial markets Solomon, Amit. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 1998; Includes bibliographical references.

Layer-by-Layer Nanoparticles for Cytokine Delivery

Sun, 01 Sep 2024 00:00:00 GMT

Layer-by-Layer Nanoparticles for Cytokine Delivery Pires, Ivan S. In the past decade, cancer immunotherapy has been a promising therapeutic strategy for cancer treatment. However, immunotherapy has failed to improve responses in certain cancers such as ovarian cancer (OC). The action of cytokines in the tumor microenvironment (TME) is key to regulating immune responses, but dose-limiting toxicities limit the application of cytokines in cancer therapy. One promising approach to improve treatment with cytokines are nanoparticles (NPs) which, when modulated via layer-by-layer (LbL) assembly, can provide many of the desirable characteristics of cytokine-delivery vehicles including tumor cell targeting, subcellular localization, and improved pharmacokinetics. In this thesis, we address some aspects of NPs that have limited their clinical utility including manufacturing, control over self-assembly, and mechanistic understanding of their interactions in biological environments The focus here was on using liposomal LbL-NPs coated with a bilayer of poly-L-arginine (PLR) and poly-L-glutamate (PLE). The coating of NPs with PLR/PLE enables targeting towards cancer cell surfaces which allows for extended extracellular presentation of cargos. This ability is used for targeted delivery of a potent immunostimulant – interleukin-12 (IL-12) to disseminated tumors in metastatic OC. Aspects on the manufacturing of other lipid-based nanocarriers such as discoidal assemblies and immune stimulating complexes (ISCOMs) are also explored. We show that employing a bottom-up approach to produce lipid-based NPs from mixed micelles allows for greater control over NP self-assembly. With this procedure, we generated immune stimulating complexes (ISCOMs) co-loaded with monophosphoryl-lipid-A (MPLA) via a scalable approach for clinical-scale manufacturing of the adjuvant termed Saponin MPLA NanoParticles (SMNP). Moreover, we discover that this approach allows for precise control over liposome size from 50 nm to 1 µm with minimal polydispersity. Lastly, by exploiting the lipid headgroup charge repulsion, we find that multivalent charged lipids yield discoidal lipid nanoparticles through this approach. Unlike previous attempts to generate lipid-based discs, this new class of NPs termed charge-stabilized nanodiscs (CND) do not require disc-stabilizing agents such as proteins or polymers. CNDs are shown to be promising drug delivery vehicles, especially when coated with PLR/PLE via the LbL technique where they have greater tumor accumulation than LbL-coated liposomes. On the use of LbL-NP for cytokine delivery via PLR/PLE coated NPs, we found that covalent conjugation of IL-12 to the liposomal core of LbL-NPs greatly improves targeting and retention of IL-12 in peritoneally-disseminated OC tumors, enabling immunological and therapeutic effects not observed with free cytokine treatment. Mechanistic investigations revealed that these LbL-NPs rapidly accumulated in tumor nodules upon intraperitoneal (i.p.) administration, wherein shedding of the LbL coating allowed for gradual release of IL-12-lipid 3 conjugates via lipid extraction by serum proteins present in interstitial fluid. Upon a single dose of IL-12 conjugated to LbL-NPs using an intraperitoneally disseminated OV2944 highly-metastatic (HM-1) mouse model, we observed a dramatic increase in T cell levels within the ascites and the tumor nodules dispersed within the i.p. space which was not observed with either free cytokine or unlayered IL-12-NPs. When evaluated for its effectiveness in this highly aggressive model, two doses could significantly enhance survival compared to even five times (5x) the amount of free cytokine. Remarkably, while the model was non-responsive to checkpoint inhibitor (CPI) therapy with anti-PD1 and anti-CTLA4, when combined with LbL-IL-12-NPs, we achieved complete responses with robust immune memory induction. The mice were able to rapidly clear rechallenges with fresh cancer cells in the i.p. space. Towards the clinical translation of LbL-IL12-NPs, we demonstrate that LbL assembly is readily performed via microfluidic mixing technology amenable for clinical-scale manufacturing. We also find that we can titrate the polymer amount used to omit time-consuming purification steps. We also find that the LbL film conformation is key to maintaining therapeutic efficacy as thicker films hinder IL-12 delivery. Lastly, we uncover that the binding target of PLE on the surface of cancer cells is SLC1A5, a glutamine amino acid transporter.

Automatic Generation of Chemical Kinetic Models including Macromolecules in Multiphase Systems

Sun, 01 Sep 2024 00:00:00 GMT

Automatic Generation of Chemical Kinetic Models including Macromolecules in Multiphase Systems Pang, Hao-Wei Detailed chemical kinetic models are indispensable tools for unraveling the complexities of industrial and environmental chemistry systems. Many important industrial and environmental chemistries involve thousands of species and hundreds of thousands of complex pathways, which are difficult to resolve manually. To address this challenge, automatic mechanism generation software has been developed. Previous studies have demonstrated the promising quantitative agreements of automatically generated mechanisms with experimental data. However, previous studies have primarily focused on small molecules in single-phase systems, overlooking the complexities of multiphase systems and macromolecules commonly found in industrial and environmental processes. This thesis introduces advancements in three key areas of automatic mechanism generation: Part I extends the current framework of automatic mechanism generation to tackle the longstanding issue of polymer fouling in the industrial system. Two detailed kinetic models are presented: one for anaerobic fouling and the other for aerobic fouling in distillation columns. Modeling innovations are introduced, which allow one to construct models including thousands of chemical reactions occurring in the liquid and film phases, vapor-liquid equilibria of hundreds of molecules, transport between the phases, and flows between the trays. All of these factors significantly affect the fouling rate. Most of the critical model parameters are derived from quantum chemistry calculations. The modeling method is validated using experimental film growth measurements made with a quartz-crystal microbalance. These models clarify the mechanistic details of the fouling process. Part II develops machine learning models for predicting thermochemical parameters in gas and liquid phases. A decision tree model based on subgraph isomorphism for gas-phase radical thermochemistry is presented. The model demonstrates improved accuracy compared to the existing empirical model and reliable uncertainty estimates for both interpolation and extrapolation tasks. Additionally, the effectiveness of active learning for building models for solvation-free energy is explored under various compositions of initial training sets and uncertainty estimation methods for data acquisition. The possibility of aiding data acquisition with unsupervised learning for active learning is also assessed. Part III adds new features and enhances the performance of multiple packages under the Reaction Mechanism Generator software suite, originally developed by the MIT Green Group. New tools are developed to facilitate thermochemical data augmentation, multiphase 3 simulation for automatic mechanism generation, and the automatic implementation of quasisteady state assumptions during the simulation of detailed kinetic models. A new species and reaction selection algorithm is developed to enable the automatic generation of mechanisms for molecular growth systems. Various speed improvement techniques are applied to improve both the simulation speed and sensitivity analysis of large-scale detailed kinetic models. By addressing these key areas, this thesis contributes to the advancement of automatic mechanism generation, paving the way for more accurate and efficient modeling of complex chemical systems.

From the body to the brain: Studying drug delivery and physiological interactions using MRI

Sun, 01 Sep 2024 00:00:00 GMT

From the body to the brain: Studying drug delivery and physiological interactions using MRI Dawson, Miranda The brain is in continuous communication with the rest of the body. Nerves connect the peripheral and central nervous system, and complex vasculature networks selectively permit passage of small molecules with an exogenous origin into the brain parenchyma. Although brain-body interactions underpin a host of cognitive and physiological phenomena, they are often overlooked in studies of brain biology and mental function. We studied aspects of the interaction between brain and body using functional and molecular magnetic resonance imaging (MRI), in combination with other tools. In a first project, we examined properties of the blood-brain barrier (BBB). The BBB is a highly selective collection of endothelial cells and tight junction proteins that restrict passage of extracerebral substances from the blood vessels into the brain tissue. We disrupted and bypassed the BBB to deliver an MRI contrast agent and quantitatively assessed the resulting contrast dynamics. We discovered that individual brain regions display method-independent susceptibility to BBB disruption and washout, suggesting principles for calibrating drug delivery and understanding the propensity for chemical exchange across the BBB. We then used one of the widefield brain delivery techniques to apply a novel contrast agent for the study of the cholinergic system, a neurochemical pathway important for motor control mechanisms in both the central and peripheral nervous systems. Kinetic modeling of probe distributions revealed intrinsic localization of cholinergic enzymes. Finally, we applied related neuroimaging tools to an animal model of substance abuse, a pathology for which brain-body interactions are particularly engaged but underappreciated. We designed a study to investigate the role of the insula, a cortical mediator of peripheral physiological signals, in responses to opioid exposure. With molecular imaging approaches, we show the insula shapes drug-dependent brain phenotypes and physiological responses during substance exposure and withdrawal. In all, this work serves as a demonstration of the power of quantitative neuroimaging methods for multifaceted investigation of brain and body relationships.

Discovering non-equilibrium mechanisms that regulatestructure and function of biomolecular condensates usingphase-field modeling

Sun, 01 Sep 2024 00:00:00 GMT

Discovering non-equilibrium mechanisms that regulatestructure and function of biomolecular condensates usingphase-field modeling Natarajan, Pradeep Biomolecular condensates are phase-separated assemblies in living cells that form through cooperative interactions between their constituents such as proteins and RNAs. They are emerging as important organizers of biochemistry in cells and are dysregulated in disease. Understanding the physical principles that shape the form and function of these condensates is a fundamental scientific challenge, which if addressed can provide novel therapeutic avenues to improve human health. The equilibrium principles behind condensate formation are well understood. Several studies have investigated the impact of multivalency, protein sequence, protein-RNA interactions, and the role of DNA in modulating biomolecular interactions that drive phase separation. However, the living cell is inherently out of equilibrium with non-equilibrium reactions that constantly burn ATP and turn over biomolecules. My thesis investigates how the interplay between biomolecular interactions and non-equilibrium reactions that turn over biomolecules affect the structure and function of biomolecular condensates. Phase-field modeling is used for these investigations, as this approach has been historically successful in answering similar questions in other fields such as material science. Prior work shows that proteins present in biomolecular condensates associated with RNA transcription undergo complex coacervation with the RNA product. The first project in this thesis investigates the interplay between complex coacervation and spatially heterogeneous RNA synthesis on condensate morphology and dynamics using a phase-field model. This simple model exhibits a rich variety of dynamical behaviors and steady states. It also provides a unifying framework to explain diverse experimental observations related to condensate morphology and dynamics such as vacuole formation, aspherical shapes, directed motion, and splitting-fusion behaviors. The second project investigates how transcription of messenger RNA (mRNA) by transcriptional condensates is modulated by other RNAs in the vicinity, such as long non-coding RNAs (lncRNAs). Our model reveals that lncRNA transcription in the vicinity can regulate mRNA transcription by altering the protein concentration and lifetime of transcriptional condensates, 3 promoting mRNA transcription from genes expressed at a low level and inhibiting transcription from highly expressed genes. This model provides a unifying framework to reconcile conflicting observations in the literature about transcriptional regulation by lncRNAs. The final project focuses on the fibrillar center of the nucleolus, an important condensate that is involved in ribosome biogenesis. Using a phase-field model that explicitly accounts for rRNA-protein interactions in the nucleolus and the non-equilibrium reaction of rRNA transcription, we show that the coarsening of fibrillar centers is arrested leading to a preferred size. Altering this size affects rRNA export and processing from the fibrillar centers. These predictions are validated by experiments. Using a combination of experiments and theory, we uncover the non-equilibrium mechanism that controls size control of fibrillar centers and the functional consequences of this size control on rRNA processing.

Molecular, Genetic, and Process Approaches for Improving Secreted Pharmaceutical Protein Quality in Komagataella phaffii

Sun, 01 Sep 2024 00:00:00 GMT

Molecular, Genetic, and Process Approaches for Improving Secreted Pharmaceutical Protein Quality in Komagataella phaffii Yang, Yuchen Biopharmaceutical products constitute a significant portion of the global bioeconomy. Compared to traditional synthetic small-molecule drugs, recombinant therapeutic proteins offer advantages like enhanced specificity and reduced side effects, and there has been tremendous growth in their innovation thanks to modern DNA technologies and AI-driven algorithms. While mammalian platforms such as Chinese Hamster Ovary (CHO) cells are commonly used for their high production titer and capability for complex post-translational modifications, thier high cost of goods manufactured can greatly constrain biopharmaceutical global accessibility. The yeast Komagataella phaffii is the prime candidate for next-generation biomanufacturing for reasons including simpler host biology, reduced time to market, and better sustainability. Nevertheless, product quality, such as size/charge variants and non-human glycosylation, can be of major concern for proteins secreted from this host organism. This thesis explores three different engineering approaches aimed at improving the quality of both aglycosylated and glycosylated proteins, with a particular focus on monoclonal antibodies, the leading class of protein biopharmaceuticals by both sales and innovation. Firstly, we demonstrated significant quality improvements through molecular sequence engineering of aglycosylated monoclonal antibody backbones. By making informed, conservative mutations to two or three amino acid residues, we greatly reduced product-related variants from proteolysis and N-terminal variations. We further showed the comparability between yeast- and CHO-secreted products, providing a framework for rapid product development with this unconventional yeast. Secondly, we applied CRISPR-Cas9 gene editing technology to humanize the glycosylation pathway of K. phaffii. We achieved homogeneous G0 glycosylation on a reporter peptide by resolving a previously unreported synthetic lethality via a transcriptomics-informed approach. Key challenges for monoclonal antibody glycosylation were also identified through further comprehensive pathway engineering. Lastly, we examined the performance of glycoengineered K. phaffii strains under varied process conditions. Employing a machine learning algorithm, we improved the desired glycan abundance on a subunit vaccine candidate. The process-robustness of engineered strains suggests the potential of this host as a viable commercial biomanufacturing host.

Microporous Polymer-Metal Organic Framework (MOF) Hybrid Materials for Separations

Sun, 01 Sep 2024 00:00:00 GMT

Microporous Polymer-Metal Organic Framework (MOF) Hybrid Materials for Separations Wu, Wan-Ni Membrane-based separation holds significant promise for reducing the high energy consumption associated with traditional thermal-based separation processes in the chemical industry. Recent advancements in microporous materials, such as polymers of intrinsic microporosity (PIMs) and metal-organic frameworks (MOFs), have demonstrated performance improvements over conventional polymers. Mixed-matrix membranes (MMMs) have emerged as a potent strategy, combining the processability of polymers with the superior separation properties of MOFs to create high-performance membranes. Additionally, the integration of MOFs into polymers can mitigate stability issues such as plasticization, swelling, and physical aging. This thesis investigates MMMs based on PIM-1 and its derivatives, along with UiO MOFs, for gas and organic solvent-based separations. The studies focus on enhancing polymer–MOF interfacial compatibility, understanding penetrant transport, and addressing key challenges in MMM design and fabrication. A longstanding challenge with MMMs fabrication is poor polymer–MOF compatibility, leading to particle agglomeration and non-selective interfacial voids. To address this, the strategy of decorating polymers and MOFs with compatible functional groups was explored. By studying UiO-66-NH2 MOF and carboxylic acid-functionalized PIM-1 (PIM-COOH), it was demonstrated that MMMs with compatible functional groups exhibit enhanced polymer–MOF interaction and plasticization resistance. To further understand transport within these MMMs, self-diffusivities of gases were measured using pulsed-field gradient nuclear magnetic resonance and compared to macroscopic diffusivities obtained from permeation and sorption analysis. The PIM–MOF material platform was also extended to solvent-based separations.To understand solvent transport through microporous polymers, intrinsic properties of swollen polymers were obtained both experimentally and computationally, and these properties were correlated with solvent transport metrics. Finally, MMMs composed of PIM-COOH and UiO MOFs with systematically increasing pore apertures were evaluated for their solvent nanofiltration performance. Key challenges such as MOF instability and non-ideal polymer–MOF interfaces were identified. In summary, this thesis delves into the structure-property relationships of microporous materials for gas and solvent-based separations, offering insights that can guide the future design of advanced composite membranes for challenging separations.

Regulation of the hif-1-dependent hypoxic stress response by C. elegans

Sun, 01 Sep 2024 00:00:00 GMT

Regulation of the hif-1-dependent hypoxic stress response by C. elegans Diehl, Calista Sorine All aerobic organisms need a way to sense oxygen levels and respond accordingly when in an unfavorable environment. In almost all metazoans, oxygen is both sensed and regulated by the HIF-1 (hypoxia inducible factor) transcription factor that is activated in periods of hypoxia and goes on to regulate hundreds of genes allowing for appropriate adaptations to hypoxia. HIF-1 activation results in changes at the cellular, tissue and whole organism levels such as increases in glycolysis, vascularization and erythropoiesis; HIF-1 is a critical factor in human development as well as progression of numerous diseases including ischemic stroke, COPD and cancer. HIF-1 is negatively regulated by the O2-dependent prolyl hydroxylase EGL-9 (known as EGLN, PHD, or HIF-PH in mammals). In normoxic conditions, EGL-9 uses ambient O2 to hydroxylate HIF-1. Hydroxylated HIF-1 is recognized by the von Hippel-Lindau (VHL-1) tumor suppressor protein, a component of an E3-ubiquitin ligase complex that targets HIF-1 for proteasomal degradation. In hypoxic conditions, EGL-9 is unable to hydroxylate HIF-1; stabilized HIF-1 enters the nucleus to regulate the expression of target genes that coordinate the hypoxia response. Increased activity of HIF-1, produced by either hypoxia or an egl-9(lf) mutation, induces the hypoxic stress response, which coordinates numerous adaptive changes in C. elegans, including retention of eggs in the uterus, decreases in locomotion and defecation rates, and increased resistance to not only hypoxia but also other stresses including oxidative stress and ER stress. By identifying suppressors of the egl-9(lf) mutant phenotype of egg retention, we have identified two independent pathways that regulate aspects of the hypoxic response in C. elegans. First, we discovered that loss of the conserved nonsense-mediated decay (NMD) pathway, an RNA surveillance mechanism that degrades aberrant mRNA transcripts with premature termination codons, suppressed the egl-9(lf)-induced changes in egg laying and defecation and caused increased hypoxia sensitivity. Other aspects of the egl-9(lf) phenotype, such as resistance to oxidative stress and changes in locomotion, were not affected by NMD-pathway mutations, indicating that NMD modulates specific aspects of the hypoxia response. Secondly, we found that loss of the neprilysin metallopeptidase, nep-2, suppressed the egl-9 Egl phenotype through the degradation of multiple neuropeptides including the known NEP-2 target SNET-1. Our findings reveal two different pathways that function downstream of egl-9 to regulate aspects of the hypoxic stress response, both providing a new pathway with which to study the neuromuscular control of egg laying using NEP-2, and critically showing the integration of the evolutionarily conserved hypoxic-stress response and nonsense-mediated decay pathways.

A synthetic biology platform for malaria parasites based on orthogonal transcriptional control

Sun, 01 Sep 2024 00:00:00 GMT

A synthetic biology platform for malaria parasites based on orthogonal transcriptional control Cárdenas Ramírez, Pablo Malaria is responsible for half a million deaths each year in some of the poorest communities around the world. Furthermore, the evolution of drug resistance among malaria parasites threatens to continue this trend. However, our understanding of malaria parasite biology is held back by a lack of tools with which to study the function of their genes. In light of this, we have created systems that control gene expression in the malaria parasite Plasmodium falciparum using bacterial repressor proteins. These are the first tools to reliably control malaria parasite transcription and offer the most robust method of conditional gene expression in Plasmodium parasites to date. We develop automated DNA design software to apply this technology to study essential parasite genes for functional genomics and confirm compound-protein interactions for drug discovery. We hope these tools advance efforts to engineer and control malaria parasites in the future.