VictorJuiz/thesis huggingface dataset:
Modeling cardiovascular risks and diseases using human induced pluripotent stem cells and tissue engineering

Cell-based, in vitro models of the cardiovascular system are invaluable for enhancing our understanding of cardiovascular health and disease. In recent years, the development of induced pluripotent stem cell (iPSC) technology has enabled effective modeling of cardiovascular diseases. In the first part of my thesis work, we utilized iPSC-derived cardiomyocytes (iPSC-CMs) to investigate the effects of microgravity on cardiomyocyte function and gene expression. This study represented both the first implementation of long-term human cell culture in space and the first use of iPSC technology to model the effects of spaceflight on human cardiomyocyte structure and function. The major drawback of using iPSC-CMs for cardiovascular modeling is their immature, fetal-like phenotype. In the second part of my thesis work, we utilized EHTs to enhance iPSC-CM maturation and further validate the results from two iPSC-CM studies that investigated (1) the mechanism by which a mutation in MYBPC3, a contractile protein, causes hypertrophic cardiomyopathy and (2) patient-specific responses to calcium channel blockers, a class of drugs commonly used to treat hypertension. In the final part of my thesis work, we utilized a sheep carotid interposition graft model to investigate whether a biodegradable external sheath could reduce graft maladaptation and failure. Collectively, this work describes the use of animal models, iPSCs, and tissue engineering to effectively model cardiovascular risks, disease, and drug responses

Keywords: Bioengineering.
Advisor: Wu Joseph Ching-Ming
Marsden Alison (Alison Leslie)
Woo Joseph
Modeling human driving from demonstrations

For autonomous agents to coexist and cooperate with humans, it is important for them to anticipate human behavior. Models of human behavior can be used by autonomous agents to plan in response to human actions and proactively coordinate with humans. Such models can also be used to build simulators for testing autonomous agents by replicating the environment of operation. Modeling human behavior is challenging because of multiple reasons such as stochasticity, multi-modality, unobservable intents, high dimensional state action spaces, and nonlinear dynamics. In the literature, both ontological and phenomenological approaches have been used to model human behavior. While ontological approaches use rules, phenomenological approaches are data-driven. This thesis presents techniques to model human behavior from demonstrations. The techniques proposed in this thesis are evaluated on their ability to model human driving behavior which is important in autonomous driving for both planning and safety validation. First, this thesis adapts the technique of Generative Adversarial Imitation Learning to the problem of driver modeling. It extends the GAIL formulation to work in the multi-agent setting where observations gathered from multiple agents are used to inform the training process of a learning agent. The proposed method is shown to better imitate demonstrated driving as opposed to single agent learning method. Since driving has associated rules, the second part of this thesis introduces a method to provide domain knowledge to the imitation learning agent through reward augmentation. The proposed method, which relies on reward augmentation, is shown to provide better emergent driving performance and overall traffic flow in the recreated traffic simulations. Many of the applications of autonomous agents are safety-critical including that of autonomous driving. This makes it important for models to be interpretable. This thesis proposes a hybrid rule-based and data-driven method that relies on the technique of particle filtering to learn parameters of underlying rule-based models from human driving demonstrations. The proposed method is demonstrated on the problem of highway merging and shown to generate realistic driving behavior as assessed by a driving Turing test

Keywords: Aeronautics and Astronautics.
Advisor: Kochenderfer Mykel J.
Sadigh Dorsa
Schwager Mac
Modeling multiple infectious diseases for cost-effectiveness analysis

Cost-effectiveness analyses can quantify and compare the benefits, harms, and costs of potential health interventions. Often, researchers will model a single disease for a cost-effectiveness analysis. However, some interventions can prevent multiple infectious diseases. For example, the Aedes aegypti and Aedes albopictus mosquitos transmit chikungunya, Zika, dengue, and yellow fever, and thus controlling these mosquitos can prevent cases of all four diseases. This dissertation focuses on applications of and methods for modeling multiple infectious diseases in cost-effectiveness analyses. First, I investigate if the results of a cost-effectiveness analysis can depend on the set of diseases that are modeled if some interventions prevent more than one disease. Next, I model both chikungunya and dengue to conduct a cost-effectiveness analysis of prevention measures for both diseases in Colombia. Finally, I develop conditions under which it is necessary to model multiple diseases when conducting a cost-effectiveness analysis and propose methods for using parallel modeling to simplify multi-disease modeling

Keywords: Management Science and Engineering.
Advisor: Brandeau Margaret L.
Bendavid Eran
Goldhaber-Fiebert Jeremy D.
Modeling multivariate time-series variables in healthcare using deep learning methods

Well-being of patients or operation of healthcare systems can be evaluated by monitoring certain health metrics or workflow parameters. This monitoring is done by periodically measuring those metrics and evaluating their variations and changes over time. The resulting measurements create a set of data points along the temporal dimension, which are called time series data. Decisions to improve patient outcome or healthcare operation often depend on accurately forecasting future trends in those time series variables. Thus, time series analysis and prediction is of very high importance in healthcare provisioning. Variables in healthcare are often interrelated, and future values of a given variable depend on past observation and trends of that variable as well as other related variables. Thus, in many cases, time series prediction tasks in healthcare are multivariate. In this dissertation, we develop a multivariate time series predictive model that can simultaneously learn from multiple time series variables over a long temporal window. We implement this model as a convolutional neural network, which we call PatientFlowNet. We use this model to predict patient flow in hospital emergency departments. Specifically, we predict the rates at which patients arrive, are treated, and are discharged from the hospital. We benchmark our model against the state-of-the-art methods in patient flow prediction using data from emergency departments in three different hospitals. We then reuse our model for predicting vital signs for patients under anesthesia, namely heart rate as well as systolic and diastolic blood pressure. We observe that our model has superior prediction accuracy in the case of patient flow in hospital emergency departments where short-term and long-term trends are present but hidden in the data and variables are significantly interrelated. However, where long-term trends are not present, as is the case with patient vital signs under anesthesia, the performance of our model is similar to the best baseline method. The convolutional design of PatientFlowNet allows us to extract dependencies between input and output variables by examining the values of convolutional filters in the first layer of the network. We provide visual and interpretable representations of learned dependencies between time series variables in each study

Keywords: Electrical Engineering.
Advisor: Bayati Mohsen
Weissman Tsachy
Widrow Bernard
Modeling the subglacial environment with geostatistics

The conditions beneath glaciers and ice-sheets including the topography, hydrology, and geology are essential for understanding ice dynamics, interpreting ice-sheet history, and predicting ice-sheet retreat and sea-level-rise in response to a changing climate. Located in remote regions and overlain by several kilometers of ice, subglacial conditions are difficult to characterize at ice-sheet scales. In this thesis, I explore the use geostatistical simulation and machine learning methods to characterize subglacial hydrologic and geologic conditions and produce the first ensemble realizations of stochastically simulated subglacial topography. In Chapter 2 of this thesis, I consider the physical and geological controls on the sizes and locations of Antarctic subglacial lakes. In Chapter 3, I investigate uncertainty in subglacial water routing at Jakobshavn Glacier in Greenland. In Chapter 4, I provide subglacial geological estimates for the Amundsen Sea Embayment in West Antarctica

Keywords: Geophysics.
Advisor: Schroeder Dustin
Caers Jef 
Klemperer Simon
Models to inform the safe collection and transfusion of donated blood

Donated blood is a critical component of health systems around the world, but its collection and transfusion involve risk for both donors and recipients. Transfusion-transmitted diseases and non-infectious adverse events pose a risk to transfusion recipients, and repeat blood donation can cause or exacerbate iron deficiency among donors. This dissertation describes four decision-analytic modeling projects that inform blood safety policy. In Chapter 2, I integrate epidemiological, health-economic, and biovigilance data to estimate the efficacy and cost-effectiveness of a 2016 policy mandating that all blood donations are screened for Zika virus in the U.S. The analysis uses a novel microsimulation of individual transfusion recipients that captures the relationship between disease exposure risk and the number and type of blood components transfused. In Chapter 3, I develop the first health-economic assessment of whole blood pathogen inactivation. The analysis is for Ghana and improves on prior blood safety assessments for sub-Saharan Africa by considering the likelihood and timing of clinical detection for chronic viral infections. In Chapter 4, I develop an optimization-based framework for identifying the optimal portfolio of blood safety interventions that overcomes some limitations of traditional cost-effectiveness analyses for blood safety. By applying this framework retrospectively to evaluate U.S. policies for Zika and West Nile virus, I show that the optimal policy can vary by geography, season, and year. Chapter 5 focuses on how frequently donors are allowed to give blood. I develop a machine learning-based decision model that tailors the inter-donation interval to each donor's risk of iron-related adverse outcomes to balance risks to donors against risks to the sufficiency of the blood supply. Together, these model-based analyses introduce novel methods and provide guidance for efficient and effective use of resources for blood safety

Keywords: Management Science and Engineering.
Advisor: Brandeau Margaret L.
Goldhaber-Fiebert Jeremy D.
Owens Douglas K.
Modern ethnic entrepreneurship : a pathway of economic mobility?

The growth in the number of Latino-owned businesses is now outpacing the growth rate of the Latino population and the start-up growth of all other racial and ethnic groups in the United States. The sociological literature on ethnic entrepreneurship has tended to depict Latino-owned businesses as driven by necessity, operating in largely resource-constrained environments, and focuses heavily on first generation immigrants. This dissertation explores modern ethnic entrepreneurship including its potential for economic return, the interplay between business strategies and ethnic identity, and the role of institutions in advancing minority entrepreneurship. I use quantitative and qualitative methods to understand what it is like to be an ethnic entrepreneur today including 101 interviews with entrepreneurs and institutional investors. Through census data, I find entrepreneurship to be an alternative and lucrative pathway among high-skilled Latinos. This is reinforced by a subset of interviews with microbusiness and scaled business owners that examines how they make sense of their ethnoracial identity in relation to their business. I find that ethnic strategies can yield benefits as a business strategy but choosing when and how to leverage an ethnic identity is largely reserved for entrepreneurs who have obtained higher education, the later generations, and those operating in professional industries. Interviews with investment professionals shed light on the institutional logics that systematically block minority-owned investment firms' wealth-generating opportunities. Together, these findings aim to illuminate the experience of modern Latinx entrepreneurs seeking economic mobility outside of formal labor structures, with important implications for increasing diversity and capital allocation to minority-owned businesses

Keywords: Sociology.
Advisor: Jiménez Tomás R. (Tomás Roberto)
Hwang Jackelyn
Saperstein Aliya
Modern financial markets

In this dissertation, I assess how well different types of financial markets function and draw implications for policy reforms in three chapters. In the first chapter, co-authored with Jason Allen (Bank of Canada), I focus on over-the-counter (OTC) markets. Traditionally, investors trade bilaterally through intermediaries, referred to as dealers, in OTC markets. An important regulatory question is whether to centralize OTC markets by shifting trades onto centralized platforms. I address this question in the context of the liquid Canadian government bond market. I document that dealers charge markups even in this market and show that there is a price gap between large investors who have access to a centralized platform and small investors who do not. I specify a model to quantify how much of this price gap is due to platform access and assess welfare effects. The model predicts that not all investors would use the platform even if platform access were universal. Nevertheless, the price gap would close by 32%-47%. Welfare would increase by 9%-30% because more trades are conducted by dealers who have high values to trade. In the second chapter, I analyze why securities are traded in isolation in most financial markets. Such a disconnected market design can be inefficient if agents trade more than one security. I assess welfare effects of connecting markets by allowing orders for one security to depend on prices of other securities. I show that everyone trades identical amounts under both market structures if and only if the clearing prices are perfectly correlated or all are price-takers. Prices in disconnected markets might allow strategic traders to extract higher rents from non-strategic traders. In expectation, connected markets generate higher welfare, but all markets become efficient as they grow large. In the third chapter, co-authored with Jason Allen (Bank of Canada), I study if and how the capitalization of dealers affects asset demand and prices. I introduce a model, in which capital-constrained dealers compete for an asset, and test its predictions with trade-level data on Canadian Treasury auctions by leveraging balance sheet movements and policy interventions during the COVID-19 pandemic. I show that both higher capitalization and weaker capital requirements flattens dealer demand. This increases the asset's price via market clearing. Based on the findings, I propose to use the slope of demand curves in Treasury auctions as a new indicator for dealer capitalization. The slope correlates with existing indicators of the literature but can be estimated easily and frequently for each dealer without balance sheet information

Keywords: Economics.
Advisor: Einav Liran
Gentzkow Matthew
Duffie Darrell 
Milgrom Paul R. (Paul Robert)
Piazzesi Monika
Modern programming models for GPU-accelerated heterogeneous supercomputers : computational fluid dynamics and in situ data compression

This work presented in this thesis is concerned with large-scale computational fluid dynamics simulations and can be divided into two main parts. The first is an investigation into the suitability of asynchronous, task-based parallelism for computational fluid dynamics simulations targeting exascale, heterogeneous architectures. The second part is concerned with the incorporation of data compression algorithms into computational fluid dynamics simulations using interpolative decomposition methods. The first chapter is a brief introduction to computational fluid dynamics, as well as an overview of traditional parallel computing practices in the field. The second chapter details the Legion programming system, which is used in this work to develop asynchronous, task-parallel solvers. Chapter Three is a case study on the implementation of a high-order, Navier-Stokes solver using the Regent programming language and the Legion runtime; this is compared to an existing, data-parallel MPI+Fortran version of the solver. The fourth chapter provides an in-depth discussion of interpolative decomposition methods for data compression, and the fifth chapter presents results for the incorporation of a novel interpolative decomposition algorithm into the Legion flow solver described in Chapter Three. Finally, Chapter Six concludes with "lessons learned" and possible avenues for future work

Keywords: Mechanical Engineering.
Advisor: Iaccarino Gianluca 
Aiken Alexander
Darve Eric
Modernizing retailers in emerging markets : investigating externally-focused and internally-focused approaches

In the retail sector globally, millions of traditional stand-alone shops — such as the Kiranas of India, Tienditas of Mexico and Xiao Mai Bu of China — co-exist alongside modern organized retail chains and compete with them to serve end-customers. In this dissertation, I study the impact of business modernization on the sales performance of these traditional retailers. I define modernization as adopting physical structures and tangible practices ubiquitous in organized retail chains(for example, exterior signage with store name and logo, or a database to record product-level information). To address this research question, I implement a randomized field experiment in Mexico City with 1148 traditional retail firms. The sample is randomized into three groups: 385 firms that are externally modernized in ways that are visible to customers; 383 firms that are internally modernized in ways that are not visible to customers; and 380 firms form a control group. I find a significant and persistent main effect of modernization on sales: firms in both treatment groups increase monthly sales by 15% to 19%, even 24 months after study recruitment. In terms of novel mechanism evidence, I find that externally-modernizing firms improve their store-level branding, while internally-modernizing firms strengthen their product management. These results have important implications for multinational managers who distribute products through traditional retail channels, and for policymakers interested in improving firm performance in the retail sector of emerging markets

Keywords: Business.
Advisor: Anderson Stephen J.
Narayanan Sridhar
Hartmann Wesley R. (Wesley Robert)
Sahni Navdeep
Mod-p Poincaré duality in p-adic analytic geometry

We review the theory of almost coherent modules that was introduced in "Almost Ring Theory" by Gabber and Ramero. Then we globalize it by developing a new theory of almost coherent sheaves on schemes and on a class of "nice" formal schemes. We show that these sheaves satisfy many properties similar to usual coherent sheaves, i.e. the Almost Proper Mapping Theorem, the Formal GAGA, etc. We also construct an almost version of the Grothendieck twisted image functor f^! and verify its properties. We then study sheaves of p-adic nearby cycles on admissible formal models of rigid spaces and show that these sheaves provide examples of almost coherent sheaves. This gives a new proof of the finiteness result for ́etale cohomology of proper rigid spaces obtained before in the work of Peter Scholze "p-adic Hodge Theory For Rigid-Analytic Varities". Finally, we use these results to prove the Poincare Duality Theorem for F_p-etale cohomology groups of a smooth proper rigid space over a p-adic field K. It positively answers the question raised by P. Scholze. We also show versions of Poincare Duality for Z/p^nZ, Z_p, an Q_p-coefficients

Keywords: Mathematics.
Advisor: Conrad Brian
Taylor R. L. (Richard Lawrence)
Vakil Ravi
Modulation of muscle stem cell function through fasting

Fasting is a dietary restriction paradigm that promotes stress resistance, modulates tissue function, and extends lifespan in a variety of organisms. Some of the benefits of fasting are attributed to its ability to influence stem cell function, though the effects of fasting on muscle stem cells (MuSCs) and muscle regeneration are unknown. Here, we show that fasting increases the depth of MuSC quiescence, driving them into a state of deep quiescence (DQ) that is characterized by heightened stress resistance and delayed cell cycle entry. While DQ is not conducive to rapid muscle repair, DQ MuSCs are resistant to apoptotic stimuli, including oxidative stress, irradiation, nutrient deprivation, and proliferative stress. DQ MuSCs also exhibit improved transplantation potency, reliably outcompeting co-transplanted MuSCs from ad lib fed animals in competitive transplantation assays. Mechanistically, induction of MuSC DQ is dependent on the nutrient- and stress- responsive mTORC1 pathway. Indeed, downregulation of mTORC1 is both necessary and sufficient for induction of DQ, as revealed by pharmacological and MuSC specific genetic manipulations. Furthermore, ketosis, a physiological adaptation to fasting, contributes to the effects of fasting on MuSCs. The ketone body β hydroxybutyrate (BHB) downregulates mTORC1 and drives DQ in MuSCs from fed animals in a p53 dependent manner. Indeed, pharmacological activation of p53 phenocopies the effects of BHB on MuSCs, and BHB-induced DQ is abrogated in MuSCs that lack p53. We conclude that the effects of exogenous ketosis derive from the established role of BHB as an endogenous histone deacetylase (HDAC) inhibitor, as pharmacological HDAC inhibition mimics the effects of fasting on MuSCs. These findings are consistent with numerous reports of increased stress resistance in response to fasting, and they establish ketosis as a novel physiological regulator of stem cell quiescence and resilience

Keywords: Stem Cell Biology and Regenerative Medicine.
Advisor: Rando Thomas A.
Brunet Anne
Chan Charles (Charles M.) 
Palmer Theo