VictorJuiz/thesis huggingface dataset:
Can we improve quality of care in private health sectors? Evidence from a randomized field experiment in Kenya

At least half the world's 7.5 billion people lack access to essential health services, and many of the world's poor who seek care are at risk of catastrophic and impoverishing out-of-pocket spending. In low- and middle-income countries, well-established private sectors and low levels of quality, even in places with high access, contribute to the challenges of improving health care for the disadvantaged. To address these issues across Kenya, the 2012-2019 African Health Markets for Equity (AHME) program delivered a comprehensive package of interventions at private clinics to improve clinical decision making and to expand high quality health care access for the poor. By taking advantage of a randomized field experiment, we examine AHME's effects on quality of care and answer the question, “Can we improve quality of care in private health sectors?” We collected and analyzed unique data (including data from standardized patients, the state-of-the-art method to assess provider practice) to examine the program's effects on three quality of care dimensions: structures, processes, and health care outcomes. We find some significantly positive effects on structural quality, and surprisingly, AHME reduced correct care by 12% (p-value = 0.021). This relative reduction was consistent but not significant for rates of unnecessary testing and medicines, which dropped by 12.3% and 8.5% respectively, suggesting reduced waste. Since average knowledge of correct care was remarkably high (90% for diarrhea; 98% for malaria), effects may be because lowering quality is in the financial interest of these for-profit clinics, especially since patients and households did not recognize reductions in quality. We examine this hypothesis with a modified dictator game and find that the least altruistic providers at AHME clinics were the ones to reduce correct care, while charging 121% more than the least altruistic at non-AHME clinics. Across all clinics, we further uncover alarming deficits in laboratory quality and in the care given to poor clients compared to the non-poor. Since highly competent private providers give lower quality care to patients due to patient characteristics and provider preferences, the success of quality improvement in the private sector requires (1) stronger accountability with careful monitoring and (2) nuanced policies to account for patient and provider heterogeneity.

Advisor: Gertler, Paul J
Machine Learning in Compiler Optimization

The end of Moore's law is driving the search for new techniques to improve system performance as applications continue to evolve rapidly and computing power demands continue to rise. One promising technique is to build more intelligent compilers.Compilers map high-level programs to lower-level primitives that run on hardware. During this process, compilers perform many complex optimizations to boost the performance of the generated code. These optimizations often require solving NP-Hard problems and dealing with an enormous search space. To overcome these challenges, compilers currently use hand-engineered heuristics that can achieve good but often far-from-optimal performance. Alternatively, software engineers resort to manually writing the optimizations for every section in the code, a burdensome process that requires prior experience and significantly increases the development time./nIn this thesis, novel approaches for automatically handling complex compiler optimization tasks are explored. End-to-end solutions using deep reinforcement learning and other machine learning algorithms are proposed. These solutions dramatically reduce the search time while capturing the code structure, different instructions, dependencies, and data structures to enable learning a sophisticated model that can better predict the actual performance cost and determine superior compiler optimizations. The proposed techniques can outperform existing state-of-the-art solutions while requiring shorter search time. Furthermore, unlike existing solutions, the deep reinforcement learning solutions are shown to generalize well to real benchmarks.

Advisor: Asanovic, Krste
Stoica, Ion
Social Evaluative Threat in Urban Schools: Who Benefits from a Value Affirmation?

A value affirmation writing task is a psychological threat-reduction nudge identified by past research to reduce the racial achievement gap within schools (Cohen et al., 2006, 2009). However, racial and economic segregation contribute to the nationwide achievement gap (Orfield et al., 2012; Reardon, 2013). Few studies have specifically examined value affirmation effects for Black, Latinx, and students with disabilities within urban schools where students of color are the numeric majority. The present study employs a value affirmation intervention in four urban schools, and uses 808 students’ self-report surveys to investigate the contribution of psychosocial variables (e.g., belonging, institutional trust, social evaluative threat) towards student outcomes (e.g., student grades and attendance). Spearman and Pearson correlation coefficients were calculated and indicated social evaluative threat in the fall had a small but statistically significant negative relationship with Semester 2 grades (-.12, p < .05). A hierarchical regression demonstrated the value affirmation intervention did not significantly improve end-of-term grades for Black or Latinx students, nor did social evaluative threat effects moderate intervention effectiveness. However, students with disabilities who completed the value affirmation intervention showed an improvement in grades compared to their special education peers who did not complete the exercise (m = 4.11, p < 0.00). Using a quantile regression, attendance for Black, Latinx, and students with disabilities who completed the value affirmation did not significantly differ from students who did not complete the intervention. Finally, the value affirmation intervention did not appear to positively impact students trust or belonging compared to students who did not engage in the intervention. The findings are not consistent with peer-reviewed value affirmation studies (Cohen et al., 2006, 2009), and suggest school context and methodological execution play a critical role when examining social-psychological variables, social evaluative threat effects, and value affirmation nudge interventions. The present study highlights the challenge of scaling threat-reduction interventions in urban schools.

Advisor: Worrell, Frank C
Prinz, Philip M
Factors that Contribute to Protective CD8+ T Cell Responses to Chronic Infection

The CD8+ T cell response to the intracellular parasite Toxoplasma gondii varies dramatically between mouse strains, resulting in differences in control of the parasite. This difference is linked to MHC haplotype, and protection in BALB/c mice has been attributed to an unusually strong MHC-1 Ld-restricted CD8+ T cell response directed against a peptide derived from the parasite protein GRA6. Our lab has previously shown that the GRA6-Ld specific response is maintained by a proliferative intermediate population that gives rise to large numbers of armed effector T cells throughout chronic infection and shows no signs of functional exhaustion. To further investigate this robust CD8+ T cell response, we characterized the TCR repertoire of GRA6-Ld specific CD8+ T cells after T. gondii infection. We showed that the GRA6-Ld specific T cell response displays a TCR repertoire dominated by the Vβ2 variable chain, limited clonal diversity, a strongly selected CDR3β motif, and a public TCR. Additionally, we showed that another Ld-restricted CD8+ T cell response specific for the F2 peptide from Vaccinia virus also has a preference for Vβ2 and a similarly focused TCR repertoire./nThe MHC-1 Ld molecule has limited peptide binding compared to conventional MHC molecules such as Kb or Db, which correlates with polymorphisms associated with “elite control” of HIV in humans. To investigate the link between this unusual MHC-1 molecule and the generation of “elite controller” CD8+ T cell responses, we compared the GRA6-Ld specific T cell response to the OVA-Kb specific response restricted to the conventional MHC-1 Kb molecule. GRA6-Ld specific T cells are significantly more protective and resistant to exhaustion in chronic T. gondii infection. Next, we used CRISPR/Cas9 to generate mice with a point mutation (W97R) in the peptide-binding groove of Ld that results in broader peptide binding and investigated the effect of this Ld W97R mutation on another robust Ld-restricted response against the IE1 peptide during Murine Cytomegalovirus (MCMV) infection. We observed an increase in exhaustion marker expression on the IE1-Ld specific CD8+ T cells during chronic MCMV infection. In both systems, T cells restricted to MHC-1 Ld exhibited resistance to exhaustion compared to T cells restricted to an MHC-1 with broader peptide binding, supporting a model in which limited peptide binding my MHC favors protective responses during chronic infection.

Advisor: Robey, Ellen
Short-Sequence Approach to Uncovering Regulatory Mechanisms in the Human Immune System

Short DNA sequences play an important role in the immune response to pathogens. As part of the non-coding regions of the genome, short DNA sequence motifs regulate cell activation and maturation by binding chromatin modifiers and transcription factors. They also determine the ability of each cell in the adaptive immune system to respond to a specific pathogen by forming the antigen-recognizing region of their receptors. This dissertation outlines computational tools I developed for utilizing and integrating high-throughput sequencing data to study the functions of short DNA sequences in the human immune system. I focus on two main aspects of short DNA sequences: (1) As components of the regulatory landscape that control the activation of dendritic cells (DCs) in response to lipopolysaccharide (LPS), and (2) as the determinants of the specificity of T cells and B cells./nThe first part of my dissertation investigates the regulatory landscape of DC activation following LPS stimulation. In chapter two I present a model which predicts gene induction based on sequence motif occurrences in the regulatory regions of each gene and show that this regulatory logic is conserved between human and mouse. Chapter three describes a supervised learning pipeline I devised to study the contribution of short sequence motifs to temporal epigenetic changes in human DCs. The second part of my dissertation describes my work on determining the specificity of T and B cells from single-cell RNA-sequencing data. Chapter four presents software I developed to reconstruct the full sequence of T cell receptors from short read single-cell RNA-sequencing. An application of the software links the length of the antigen-recognizing region of the receptor to the state of the cell, demonstrating the importance of such combined analysis in studying the immune response to viral infections. Chapter five describes an extension of the software to reconstruct B cells receptor sequences.

Advisor: Yosef, Nir
Secure Computation Systems for Confidential Data Analysis

A large number of services today are built around processing data that is collected from or shared by customers. While such services are typically able to protect the data when it is in transit or in storage using standard encryption protocols, they are unable to extend this protection to the data when it is being processed, making it vulnerable to breaches. This not only threatens data confidentiality in existing services, it also prevents customers from availing such services altogether for sensitive workloads, in that they are unwilling / unable to share their data out of privacy concerns, regulatory hurdles, or business competition./nExisting solutions to this problem are unable to meet the requirements of advanced data analysis applications. Systems that are efficient do not provide strong enough security guarantees, and approaches with stronger security are often not efficient./nTo address this problem, the work in this dissertation develops new systems and protocols for securely computing on encrypted data, that attempt to bridge the gap between security and efficiency. We distill design principles based on the properties of the two primary approaches for secure computation—advanced cryptographic protocols and trusted execution environments. Informed by these principles, we design novel cryptographic protocols and algorithms with strong and provable security guarantees, using which we show how to build systems that are both secure and efficient.

Advisor: Popa, Raluca A
Sharing without Showing: Building Secure Collaborative Systems

The recent revolution in advanced data analytics gave rise to a growing demand among organizations for high quality data. However, in many domains such as finance and medicine, organizations have encountered obstacles in data acquisition because their target applications need sensitive data that reside across multiple parties. One promising solution to this data scarcity problem is collaborative computation, where several organizations pool together their data and compute on the joint dataset. This type of computation enables parties to acquire a larger volume of data, as well as more diverse data. Unfortunately, organizations are often unwilling or unable to share their data in plaintext due to business competition or government regulation./nMy dissertation focuses on solving this problem by enabling organizations to run complex computations on the joint dataset without revealing their sensitive input to the other parties. This dissertation presents four systems that utilize hardware enclaves as well as advanced cryptographic techniques for secure computation on workloads that range from SQL analytics to machine learning. By utilizing a wide range of tools from both systems and cryptography and also innovating on them, my systems provide strong and provable security guarantees and are often orders of magnitude faster compared to prior work or the more straightforward ways of integrating cryptography into systems.

Advisor: Popa, Raluca Ada
Stoica, Ion
Adapting Across Domains by Aligning Representations and Images

The advent of deep convolutional networks has powered a new wave of progress in visual recognition. These learned representations vastly outperform hand-engineered features, achieving much higher performance on visual tasks while generalizing better across datasets. However, as general as these models may seem, they still suffer when there is a mismatch between the data they were trained on and the data they are being asked to operate on. Domain adaptation offers a potential solution, allowing us to adapt networks from the source domain that they were trained on to new target domains, where labeled data is sparse or entirely absent. However, before the rise of end-to-end learnable representations, visual domain adaptation techniques were largely limited to adapting classifiers trained on top of fixed, hand-designed visual features. In this thesis, we show how visual domain adaptation can be integrated with deep learning to directly learn representations that are resilient to domain shift, thereby enabling models to generalize beyond the source domain./nIn Chapter 2 we demonstrate how we can design losses that attempt to measure how different two domains are. We show that by optimizing representations to minimize these losses, we can learn representations that generalize better from source to target. In Chapters 3 and 4, rather than hand-designing these domain losses, we show that we can train models that attempt to measure domain discrepancy. Since these models are themselves end-to-end learnable, we can backpropagate through them to learn representations that minimize the learned discrepancy. This is similar in concept to Generative Adversarial Networks, and we additionally explore the relationship between the two and how we can use techniques developed for GANs in an adversarial setting as well. Finally, in Chapters 5 and 6, we show that adaptation need not be limited to intermediate features of a deep network. Adversarial adaptation techniques can also be used to train models that directly alter the pixels of images, transforming them into cross-domain analogues. These transformed images can then be used as a labeled pseudo-target dataset to learn supervised models better suited for the target domain. We show that this technique is complementary to feature-based adaptation, yielding even better performance when the two are combined.

Advisor: Darrell, Trevor
Elucidating the Effects on Proteome Regulation under Caloric Restriction

Cellular energy homeostasis contributes to normal cell growth, functioning as a biological checkpoint of life. There have been numerous genes and metabolic pathways known to regulate the energy status of the cell, and how they contribute to cellular maintenance is of great interest; Caloric Restriction (CR) takes advantage of these metabolic pathways, retarding the aging process by slowing down the metabolic rate. However, despite the many studies on cellular energy homeostasis, much work still needs to be done./nMany of the applications of CR in various organisms have established life-extending benefits by regulating age-related diseases through convergent mechanisms. In fact, a number of signaling pathways, as well as master regulator proteins, act through these mechanisms to interact and regulate protein expressions. CR, as well as other lifespan extending interventions such as rapamycin treatment and inhibition of insulin growth factors, are subject to translational regulation. Increased caloric intake and obesity related comorbidities, in turn, can also work through these pathways to induce translation and cause disease, indicating the role of protein synthesis in health and disease is of great importance. Stable isotope-labeling proteomics are a powerful strategy that enable the assessment of proteome-wide dynamic fluxes in energy regulating biological interventions such as CR and mimetics. Using mass spectrometric (MS) strategies has afforded scientists a never before seen understanding of how proteome dynamics stand at the center of phenotype, physiologic adaptation, and disease pathogenesis. Through this approach, researches have been able to measure protein synthesis and turnover rates, both for targeted proteins an unbiased screening./nIn this dissertation, I present a thorough review of our current understanding of metabolic pathways in the context of lifespan extension interventions like CR. I also discuss the underlying principles for measuring protein dynamics, focusing on metabolic labeling with 2H2O (heavy water) combined with tandem MS analysis of mass isotopomer abundances. Next, I demonstrate its application in four separate studies. First, I use 2H2O labeling in CR mice in a time-course to identify whether the protein turnover rate slowdown occurs immediately, gradually, or within a discreate time period. Next, I explore separately whether two potential CR mimetics, exercise and metformin administration, are able to slow down the fractional synthesis rate of proteins in a manner comparable to CR. Finally, I discuss a potential CR modulator in Nitric Oxide, a short-lived bioactive molecule known to be induced under CR conditions and reduced during aging and disease. Overall, the work reported in this dissertation demonstrates how global proteome dynamics require NO for proper regulation, CR mimetics influence the proteome in similar mode, aerobic exercise has very different effects and CR effects on proteome fluxes are activated within a narrow and discrete time period.

Advisor: Hellerstein, Marc K
Lived Experience in New Mexico, 1754-2019: A Historical Archaeology With and For a Genizaro Community

Deep contestations of essentialized identity categories are a contemporary reality for communities for whom cultural patrimony of land and water resources play a crucial role. Yet, archaeology has not been able to adequately recognize the dynamics of the changing nature of identity practices which shaped interactions between groups of people, particularly in areas with a sustained colonial presence and resource-challenged ecologies. The high-elevation, semi-arid climate and historical complexity of Northern New Mexico provide such a context. My research objective is to understand how Genizaro Indian communities are sensitive to the historically particular dynamics of ethnopolitical empowerment and racialization in the 18th to early 20th centuries. This project uses an examination of the documentary record, faunal remains, and commensurate data from excavated materials from Genizaro communities in New Mexico to build upon existing models of cultural hybridity and ethnogenesis.

Advisor: Sunseri, Jun U
The Design & Implementation of Modular Smart Dust

Advancements across the computing stack now allow the realization of self-contained, sensing, computing, and communication devices in less than a cubic millimeter of volume—Smart Dust. Today’s millimeter-scale systems are often one-offs, however. They are built to prove they can be built or for one particular application, but they are not yet so numerous and diverse to satisfy the vision of ubiquitous, ambient intelligence. This work aims to identify and address challenges in the move from tens to tens of millions of Smart Dust computing systems./nThe core contribution of this dissertation is bringing modularity to the millimeter-scale computing class. Much of the rich panoply of conventional systems is driven not by the diversity of individual chips alone but by the number of different ways in which they can be synthesized into novel designs. Composition-oriented design of systems is enabled in part by the interconnect technologies that lie between components. This dissertation identifies the limitations of extant interconnect technologies for the millimeter-scale computing class and then designs, implements, and evaluates a new interconnect bus and interconnect protocol that enables composable Smart Dust systems. As a final contribution, this dissertation shows how this same interconnect can be leveraged to support key operations for bringup and deployment of Smart Dust systems, in particular programming and debugging.

Advisor: Dutta, Prabal K.