VictorJuiz/thesis huggingface dataset:
Imaging with electrons : unraveling nanoscale structure-function relations in energy storage & quantum optical materials
Keywords: Electrical Engineering.

While nanoscaling offers unique tuning knobs for optimizing system performance, it also changes process fundamentals. Ensemble measurements are often convoluted due to system heterogeneity, and lacks spatial resolution into the nanoscale. How do we correlate nano-to-atomic scale structure to the system performance? My work tackles this question for two vital technologies: (1) energy storage like batteries and metal hydrides, and (2) bright single photon sources for quantum optics, communication and sensing. We leverage, modify and develop electron microscopy techniques which let us probe the system performance (H-content and reaction rates for the former, optical emission for the later) and do correlative structural analysis at the nanoscale. At first, we explore how nanoscale dimensionality changes hydrogenation phase-change thermodynamics. We find that, one-dimensional nanorods have very distinct steady-state phase-coexistence in contrast to zero-dimensional nanoparticles. We report a length range beyond which it is more likely to form defects during phase-change. Next, we focus on understanding the effect of shape and surface faceting on reaction dynamics. We develop a technique to visualize the reaction in real-time in-situ using diffraction contrast. Capitalizing on this, we find that the phase-change is a nucleation-growth process. Nuceation always occurs at the corner irrespective of shape, but phase-propagation direction depends on shape. We also do structural analysis of reaction intermediates, and find that while rotational defects are likely to form during phase-transition, the particles 'self-heal' at the end. Next, we focus on developing single photon sources in a van der Waals insulator: hexagonal boron nitride (hBN). The biggest question circumventing technological development of hBN is the origin of spectral variability of emission. We develop a technique to correlate the defects' cathodoluminescence (CL) in a transmission electron microscope and photoluminescence (PL) in an optical microscope. Using this, we localize each defect with 15−20 nm resolution. Based on PL-CL correlation and local strain of the local host environment using electron diffraction, we postulate that there are at least four different atomic defects resulting into the observed spectral variability. Together, our results pushes the field towards direct identification of these defects and achieve 'designer-defect' and emission properties
Immune development at single cell resolution
Keywords: Genetics.

The immune system is highly complex and contains many different cell types that are able to recognize and fight invading pathogens. B cells and T cells are both part of the adaptive immune system and must undergo specialized training programs to specifically recognize non-self invaders. Because these immune cells must be able to recognize a wide variety of pathogens, these cells and the cells that train them are remarkably heterogeneous. During the past 10 years, single cell technologies have evolved at a rapid pace allowing for a deeper understanding of these immune cells at the single cell level. In this dissertation I explore three projects I pursued during my PhD, where I use a variety of single cell technologies to explore the immune education of both B and T cells. In chapter 2, I combine single cell RNA-sequencing with lineage tracing and ablation and recovery of cells to explore the lineage relationships between medullary thymic epithelial cells, the cells responsible for negative selection of self-reactive T cells. In chapter 3, I combine single cell RNA-sequencing, single cell ATAC-sequencing, and single cell antibody capture (CITE-sequencing) to explore the transcriptional and chromatin landscape of B cells during affinity maturation, the process during which B cells adapt to be highly specific to a single pathogen. In chapter 4, I use single cell RNA-sequencing to explore the heterogeneity of thymic tuft cells, a subpopulation of medullary thymic epithelial cells. Taken together, these chapters improve our understanding of events that occur during the education of B and T cells
Importance of Modern Indigenous Art in Museums
Keywords: Indians
Indians of North America
Art museums
Art
Stereotypes (Social psychology)
Text
Thesis

Today, museum owners, curators, and donors continue to recreate history through a Western lens by showcasing Indigenous art as artifact and by not including modern and contemporary pieces by Native artists. Contemporary Native art is particularly important to showcase in learning institutions such as museums because they underscore that Native individuals continue to exist, are important, and are multifaceted. This is beneficial for both Native and non-Native individuals, for the inclusion of modern art challenges dated ideas of Indigenous identity, expanding Native possibility and confronting non-Natives understanding of Indigeneity.
Improving efficiency and stability of perovskite tandem photovoltaics
Keywords: Materials Science and Engineering.

Organic-inorganic metal halide perovskites are a class of semiconductor materials with excellent optoelectronic properties that hold promise for applications including photovoltaics, light-emitting diodes, and detectors. They are fabricated from low-cost, scalable solution processing or vapor deposition methods and have readily tunable optoelectronic properties through manipulation of chemical composition. This unique combination of materials properties has opened the door for low-cost tandem photovoltaics, in which a wide band gap perovskite solar cell is paired with a low band gap solar cell such as silicon, copper indium gallium diselenide, or low band gap perovskites, reducing thermalization losses that are intrinsic to single junction solar cells and enabling higher power conversion efficiencies. However, to enable commercialization of this new technology, the stability of metal halide perovskites must be improved. In this dissertation, I first describe the current understanding of degradation mechanisms for metal halide perovskite solar cells and methods to prevent them. I then discuss in detail metal oxide barrier layer design to prevent oxygen and moisture ingress and reactions between halogen species from the perovskite film and metal contacts and use these barrier layers to demonstrate efficient devices that have state-of-the-art operational and thermal stabilities. I then apply these learnings to highly efficient perovskite/silicon tandems, enabled by a stable device architecture and a novel, triple-halide (I, Br, Cl) perovskite with excellent photostability and optoelectronic properties enabled by the addition of chlorine into the lattice. I conclude with an in-depth study of interfacial reactions between the perovskite and nickel oxide, a highly desirable hole transport layer for efficient and stable perovskite tandem solar cells and demonstrate a method to prevent these reactions, improving device voltages and efficiencies
Improving healthcare decisions through data-driven methods and models : analysis of policies for personalized medicine
Keywords: Management Science and Engineering.

This dissertation develops methods and models for personalized medicine. First, we develop a new modeling framework for personalizing medical treatment decisions and apply it to personalize selection of antipsychotic drugs for patients with schizophrenia. We project that use of this framework can substantially and cost effectively improve patient health outcomes. Second, we demonstrate potential adverse effects of partial personalization, which we define as personalization based on a subset of patient-specific risks and preferences. We develop a new method for partial personalization and show that it avoids these potential adverse effects. Third, we develop a method for simplifying complex models for personalization and apply it to simplify the model that we developed for personalized selection of antipsychotic drugs. This method allows for determination of the optimal degree of personalization, and improves computational performance and interpretability of the original model. Finally, we illustrate how personalized medicine approaches can be used to evaluate policies for population-level health problems. Using a personalized medicine approach, we project the health impacts of climate-change-induced nutritional deficiencies and optimal mitigation strategies. We conclude with a discussion of directions for further research
Improving inference of cosmological parameters with advanced statistical techniques using simulations
Keywords: Physics.

The focus of this thesis is the improvement of cosmological inferences through the use of advanced statistical techniques applied to simulations. This thesis approaches the challenge of optimal cosmological inference through two main approaches. The first approach is my work with cosmological emulators as part of the Aemulus collaboration. I first describe the Aemulus simulations and emulators for the halo mass function and redshift space galaxy clustering that I contributed to. Next, I describe analyses using Aemulus to study the effect of galaxy secondary bias on cosmological inferences. This includes descriptions of new models for galaxy occupation, and analyses of their ability to describe realistic galaxy distributions, as well as the use of those models to build emulators for mock analysis. Finally, I discuss work using an emulator for a new statistics, kNN-CDFs, to measure galaxy secondary bias and cosmological parameters. The second approach is my work studying cosmological neural networks. I describe new techniques I've developed to identify the features identified by cosmological neural networks in weak lensing fields
Imputation and predictive modeling with biomedical multi-scale data
Keywords: Electrical Engineering.

In recent years, large amounts of biomedical data are being collected, which allow researchers to discover new knowledge about diseases and to create methods to improve health care. However, when working with the emerging biomedical data, one may encounter problems of incomplete data, small sample data, or multi-scale data, each of which presents a unique challenge for data analysis. In this thesis, we present three pieces of work addressing these challenges. In the first part, we describe a deep learning model for efficient data imputation to handle the problem of incomplete data. When dealing with certain missing-not-at-random cases, an effective and practical shift-correction implementation is developed to improve imputation accuracy. Furthermore, we look into the mechanism that contributes to the new framework's superior performance. In the second part, we describe how we use meta-learning to alleviate the problem of small-sample data in the context of survival prediction. The meta-learning framework shows advantages over competing methods for the case of limited training samples from the target disease. In addition, the model is interpretable and allows us to uncover biological pathways related to the disease outcome. In the final part, we describe different data fusion strategies that integrate data modalities on different scales for improved results in survival prediction. The fusion strategies are evaluated with brain tumor cohorts across different age groups and tumor subtypes. We further examine whether the model learnt from one brain tumor cohort can potentially provide useful information for other cohorts with different disease characteristics
In Data We Trust: Challenges and Opportunities Facing Data-Driven Policymaking in Public Health
Keywords:  Department of Science
Computer Science
STS
Interdisciplinary Honors
Public Health
Data-Driven Policymaking
Opioid Crisis
Coronavirus Pandemic
Healthcare Disparities
Government
Race
Thesis

National management of public health crises suffers from our inability to collect and utilize data effectively in developing response policies. The reality became painfully apparent with the ongoing coronavirus pandemic but is mirrored in past crises like the U.S. opioid epidemic. Where do the gaps between data and data-driven policymaking in healthcare arise? In this thesis, using these two crises as case studies, I characterize the relationships between data, public health, and policymaking and identify pitfalls that have stymied their effective integration.
I synthesize academic literature, policy memos, and news media to describe how the federal government struggles to design legislation for crisis mitigation due to its reliance on hierarchical information pathways, which are misaligned with crisis geographies and challenged by temporal strain. I instead promote a hybrid data management strategy combining current infrastructure with proxy approaches from literature. At the local level, I place secondary sources in conversation with expert testimony to tease out data’s “public life,” and I argue that creating lasting local change will require governments to cultivate an increased public understanding of data. Across government strata, I characterize the confusion created in policymaking from the inclusion of racial data and suggest we rethink our concept of race in healthcare.
I propose an original framework for better data-driven policymaking in public health through opening bidirectional lines of communication, equipping healthcare providers and citizens to understand data at the local level, and reevaluating our understanding of traditional statistics (mortality rates and racial categorization) at all levels of government. This framework shifts the conversation surrounding data in public health policymaking from a tone of reactiveness to one of proactive readiness and, ultimately, helps save lives.
In her own right : sovereignty and gender in princely bhopal
Keywords: History.

This dissertation argues that sovereignty was mutually constituted through relations between British imperialists and princely rulers in India in the twentieth century. Despite the fact that approximately two-fifths of Indian territory and one-fourth of India's population remained in the power of the indigenous sovereigns who comprised princely India, we know surprisingly little about these rulers and their states. At the heart of the project lies Sultan Jahan Begum, the only Muslim queen to rule in her own right in India, the British empire, and the world between 1901 and 1926. I examine critical topics in governance and gender, including public health, travel and pilgrimage, the practice of purdah, Muslim anticolonial nationalism during and after World War I, veiled women in photography, princely succession, and Sultan Jahan Begum's vernacular manuscripts, within which she argued for measured emancipation for Indian women. In my study, "in her own right" refers to the discursive and performative means Sultan Jahan Begum used for exploiting grey areas in imperial policy while creating roles for both princely rulers and women in anticolonial imaginations of a decolonized India. My analysis of Sultan Jahan Begum adds to existing scholarship on how the "Muslim woman" was (and is) a collective subject for contesting authorities who were negotiating and claiming sovereignty and nation. By attending to the relationship between British and Indian actors who co-constituted sovereignty, my project also provides new ways to understand emerging fields of scholarship on sovereignty in colonial contexts, Muslim feminism, and female leadership in contemporary perspective
In silico chemistry : multiscale modeling from quantum mechanics to kinetics
Keywords: Chemistry.

Chemical phenomena occur across a wide range of length and time scales. Atoms vibrate on the order of 10-15 seconds, while chemical reactions may take place anywhere between 10-12 seconds and hours (or as long as a chemist is willing to wait). Quantum mechanics governs the smallest of these scales, where the Schrödinger equation tells us how to compute observable properties of quantum systems. For such small systems, highly accurate calculations can be performed, though often with formidable computational cost. At intermediate scales with hundreds of atoms, approximate quantum mechanical methods can be used to define potential energy surfaces, which can be used in conjunction with Newtonian mechanics to simulate the trajectories that molecules move in. Kinetic models bridge to even larger, continuum scales, by utilizing a set of differential equations that relate macroscopic thermodynamic variables, such as temperature and pressure, to the concentrations of chemical species. The differential equations can be integrated to arbitrary points forward in time, providing predictions of how thermodynamic variables and concentrations change. Each of the aforementioned models yields a trade-off: high accuracy can be attained in the limit of the very small, while in the limit of the large, we are restricted to using coarser models. Connecting microscopic descriptions of molecules with models of macroscopic phenomena, as well as the reverse, can be informative for models at both scales. This dissertation aims to do precisely this, progressively working our way up from small length and time scales to macroscopic scales. First, we discuss research on improving the performance of electronic structure calculations such as Hartree-Fock by constructing approximations to the wavefunction through partitions of atoms into fragments. Then, we show how machine learning can be utilized to approximate effective Hamiltonian matrix elements. After, we discuss molecular dynamics integration, deriving multiple time step integration methods, showing how explicit integrators can be easily implemented, and how the accuracy of symplectic integrators can be analyzed in terms of the shadow Hamiltonian. Last, we make the connection of accelerated molecular dynamics simulations of nitromethane to kinetic models, which can be used to predict reaction pathways with varying thermodynamic conditions and time scales, allowing for comparison of predictions to experiments on computationally challenging time scales
In situ visualization and nanophotonic control of hydrogen-driven nanoscale phase transformation dynamics
Keywords: Applied Physics.

Understanding and manipulating chemically-driven nanoparticle transformations is crucial for the efficacy of next-generation energy storage and catalytic materials. However, obtaining such control is an outstanding challenge. Notably, there is a mismatch in length-scale between the atomic-scale structural features (such as atomic coordination number and surface strain) which influence transformation dynamics and the extrinsic parameters (such as temperature, chemical composition, and chemical environment) that can be controlled. In this thesis, I explore how optical excitation can be used to enable new catalytic mechanisms. In particular, we utilize local surface plasmon resonances (LSPRs), which result in strong optical enhancement, local temperature gradients, and hot carrier populations, to control chemical transformations. These LSPRs enable chemical activity that is no longer solely defined by inherent nanoparticle structure but rather by the local electromagnetic field. We use a suite of in situ environmental transmission electron microscopy techniques to visualize a model reaction: the hydrogenation and dehydrogenation phase transformation of individual palladium hydride (PdHx) nanoparticles. First, I describe the mechanism of this phase transition without plasmonic excitation. By visualizing the phase transformation dynamics in distinctly-faceted nanoparticles, including single-crystalline cubes and octahedra, we find that the new phase always nucleates at nanoparticle tips and corners. Then, I demonstrate how LSPRs can modify the preferred active site using a proof-of-concept crossed-bar Au-PdHx system. Under resonant illumination, we observe new nucleation sites and dynamics at the nanoscale, which we experimentally and computationally verify to be energetically unfavorable. Our work shows how plasmons enable catalytic site-selectivity and new reaction mechanisms, with important implications spanning catalysis, energy, and information
In vivo and in vitro protein folding : novel insights in co-translational protein folding and computational protein design
Keywords: Biology.

This thesis focuses on characterizing the determinants of ribosome pausing and its correlation with the mechanisms of protein folding by combining machine learning methods with ribosome profiling. Furthermore, for the first time, a detailed explanation of the intricate manner on which the cotranslational machinery assists the folding of newly synthesized proteins is offered. Finally, a demonstration of how information provided by naturally occurring proteins can be leveraged in order to design new protein-protein interactions will be provided
Information-directed sampling for reinforcement learning
Keywords: Management Science and Engineering.

Reinforcement learning has enjoyed a resurgence in popularity over the past decade thanks to the ever-increasing availability of computing power. Many success stories of reinforcement learning seem to suggest a potential gateway to creating intelligent agents that are capable of performing tasks with human-level proficiency. However, many state-of-the-art reinforcement learning algorithms require a tremendous amount of simulated data, which is not practical when data is generated from actual interactions in the real world. Addressing data efficiency will be crucial for making reinforcement learning practical for real-world applications. In this dissertation, we take an information-theoretic approach to reason about how an agent should acquire information in an environment to improve decision-making. We generalize the information-directed sampling (IDS) decision rule from online decision-making literature to reinforcement learning. This decision rule aims to acquire useful information about the environment while also taking into consideration the costs of information acquisition. We argue that IDS can demonstrate desirable information-seeking behaviors in a reinforcement learning problem where existing methods fail. We hypothesize that in practical environments that are typically rich in observations, IDS has the potential to significantly improve data efficiency relative to existing exploration schemes. Furthermore, we analyze the expected regret of IDS for three stylized classes of environments, linear bandits, tabular Markov decision processes (MDPs), and factored MDPs. We derive regret bounds that are nearly competitive with state-of-the-art regret bounds, which demonstrate promise of our information-theoretic design concept. Lastly, the form of IDS studied in this dissertation should be viewed as an agent design concept rather than a concrete algorithm. Major work needs to be done to design practical algorithms that preserve the benefits of this conceptual decision rule while being computationally tractable. We highlight some key aspects for designing a practical IDS agent and propose several research directions for addressing each aspect