VictorJuiz/thesis huggingface dataset:
Laplacian regularized stratified model fitting

A large number of practical problems in optimization involve the fitting of models with categorical features. These problems arise in a variety of fields, such as machine learning, statistics, finance, medicine, and data analysis. A common and simple paradigm parameterizes the model by a parameter vector, and uses convex optimization to minimize an empirical loss on a training data set plus a regularization term that (hopefully) skews the model parameter toward one for which the model generalizes to out of sample data. In this dissertation, we discuss a framework of model fitting methods that depend on a number of categorical features. We build a model of some data that depends in an arbitrary way on these categorical features, by building a separate model for the data that take on each of the possible values of these categorical features. We call these models stratified models. Furthermore, we propose augmenting a basic stratified model fitting problem with an additional regularization term, Laplacian regularization, which encourages the parameters found for each value to be close to their neighbors on some specified weighted graph on the categorical values. We begin this dissertation with an early work on solving Laplacian regularized problems. We then define these models as Laplacian regularized stratified models, and propose an efficient distributed solution method based on the alternating direction method of multipliers (ADMM), which allows us to solve the Laplacian regularized stratified model fitting problem in a distributed fashion and at large scale, in spite of the model parameters being coupled due to the Laplacian regularization term. We then provide an extension of Laplacian regularized stratified models, where we show that if the model parameters vary smoothly over the graph Laplacian, then it can be well approximated by a linear combination of a small number of the Laplacian eigenvectors, which can drastically reduce the number of variables in the problem. Finally, we provide two examples: one example of fitting Laplacian regularized Gaussian models, and another, much larger example which brings the ideas of this paper together by fitting Laplacian regularized stratified risk and return models to used in a realistic quantitative finance setting

Keywords: Electrical Engineering.
Advisor: Boyd Stephen P. 
Osgood Brad
Srivastava Ashok N. (Ashok Narain)
Large-scale simulation for embodied perception and robot learning

Being able to perceive and interact with complex human environments is an important yet challenging problem in robotics for decades. Learning active perception and sensorimotor control by interacting with the physical world is cumbersome as existing algorithms are too slow to learn in real-time, and robots are fragile and costly. This has given rise to learning in simulation, and to make progress on this problem, efficient simulation infrastructure needs to be developed to support interactive and long-horizon tasks, and sample-efficient learning algorithms need to be developed to solve these tasks. In this dissertation, I present two lines of work contributing to these topics. The first line of work is to create large-scale, realistic, and interactive simulation environments, including Gibson Environment and iGibson. Gibson Environment is proposed for learning real-world perception for active agents. Gibson Environment is built from the real world and reflects its semantic complexity. It has a neural network-based renderer and a mechanism named ``Goggle" to ensure no need to further domain adaptation before deployment of results in the real world. Gibson Environment significantly improves pixel-level realism over existing simulation environments. To build upon Gibson Environment and improve the physical realism of the simulation, I propose iGibson, a simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes. The simulated scenes are replicas of 3D scanned real-world homes, aligning the distribution of objects and layout to those of the real world. Novel long horizon problems including interactive navigation and mobile manipulation can be defined in this environment, and I show evidence that solutions can be transferred to the real world. The second line of work studies reinforcement learning (RL) for long-horizon robotics problems enabled by the interactive simulation environments. First, I introduce the interactive navigation problem and associated metrics. I leverage model-free RL algorithms to solve the proposed interactive navigation problems. Second, to solve challenging tasks in fully interactive simulation environments and improve sample efficiency of RL, I propose ReLMoGen, a framework to integrate motion generation into RL. I propose to lift the action space from joint control signals to motion generation subgoals. By lifting the action space and leveraging sampling-based motion planners, I can efficiently use RL to solve complex long-horizon tasks that existing RL methods cannot solve in the original action space

Keywords: Electrical Engineering.
Advisor: Guibas Leonidas J.
Savarese Silvio
Haber Nick
Sadigh Dorsa
Wetzstein Gordon
Las Historias Explosivas: Resisting Justice in Ricardo Piglia’s La ciudad ausente

Argentine post-dictatorship can be characterized by a series of legal actions to exculpate the dictatorship's accused, from the Ley de punto final of 1986 to the presidential pardons of 1990. From within this overarching narrative of reconciliation, Ricardo Piglia's La ciudad ausente tells the story of a storytelling machine, Elena, whose stories form a sort of resistance to state control—resistance, in particular, to the narrative that justice had already been served. Drawing on Wai Chee Dimock's theory of justice's residues, Walter Benjamin's theory of storytellers, and Paolo Freire's Pedagogy of the Oppressed, this thesis builds on Idelber Avelar's analysis in The Untimely Present to examine the role of storytelling within La ciudad ausente, and suggests that it may serve as a means of resistance to the narrative of achieved justice. This analysis of La ciudad ausente thus provides an example of the inadequacies of legal justice, and the importance of alternative strategies for achieving restitution and moving forward.

Keywords: Department of English
Ricardo Piglia
Post-dictatorship
Postdictatorial Latin American fiction
Neoliberalism
Post-dictatorship justice
Transitional justice
Critical legal studies
Storytelling
Testimony
Walter Benjamin
Paolo Freire
Cyborg theory
Wai Chee Dimock
Narrative resistance
Text
Thesis
Advisor: Kantor Roanne
Cohen David
Lay beliefs about self-identity: the case of gender

The current dissertation contains three series of studies that revealed laypeople's nuanced, meticulous, and early-emerging attentiveness to gender membership, gender self-identification, and gender roles and norms in their personal and social life. Specifically, the studies together suggest that laypeople's conceptualizations of gender seem to display a rather sophisticated structure that (1) allows for simultaneous and separate judgments about gender membership; (2) considers the distinct influences of one's internal sense of gender, gender roles ascribed by society, and the physical forms stereotypically associated with different gender groups; and (3) demonstrates remarkable variability across individuals, cultural and historical contexts, and one's own life span

Keywords: Psychology.
Advisor: Dweck Carol S.
Frank Michael C. (Professor of human biology)
Markman Ellen M.
Layout automation for analog and mixed-signal circuits using digital place-and-route tools

Today's analog and mixed-signal (AMS) layout flow requires long manual iterations and does not leverage computing resources for data-driven optimization. This issue is further compounded by the explosion of design rules and layout-dependent effects. We present an AMS layout generation flow that leverages digital place-and-route tools, amortizes setup cost with reusable primitives, and prunes layout candidates using time-efficient layout quality surrogates. We also analyze layout-dependent effects and parasitics and investigate unique challenges and mitigation strategies associated with using digital place-and-route tools for AMS circuits. These insights are validated with a generated 4x time-interleaved successive approximation analog-to-digital converter, a StrongARM comparator, and a voltage-controlled oscillator (VCO) in 16 nm FinFET CMOS. Silicon measurement results of the VCO closely track the simulation, showing only 6% in maximal oscillation frequency degradation from its manual counterpart, verifying the methodology from netlist to silicon

Keywords: Electrical Engineering.
Advisor: Murmann Boris
Horowitz Mark (Mark Alan)
Lee Thomas H.
Learning determinants of nucleosome positioning from sequence-based models of cell-free DNA

The human genome exists in cells as chromatin, a vastly complex 3D macromolecule with multiple hierarchical scales of structural and functional organization. Nucleosomes, small, radially symmetric protein octamers ~10-20nm in diameter, are the fundamental unit of chromatin organization from which higher order chromatin structures are derived. Nucleosomes act like passive beads on a string, controlled both by their own sterics and by higher-order processes such as TF binding and chromatin remodeling. This work centers on the relationship between nucleosomes and the DNA sequences they bind, measured using the mapping coordinates of (largely nucleosome bound) fragments of DNA isolate from blood plasma. In the first half, we develop a model called cfDNA-DeepNuc, which is a fully dense convolutional neural network trained to predict nucleosome occupancy from DNA sequence. We use the model as a basis to study the sequence determinants of nucleosomes positioning, identifying patterns that both coincide with and that extend beyond existing knowledge of nucleosome sequence determinants. In the second half, we present an approach to measuring chromatin state (i.e., enhancer accessibility) from cell-free DNA fragmentation patterns, and demonstrate proof of principle for providing estimates of tissue signal strength in a series of in silico titration and subsampling experiments. Our work is intended to highlight cell-free DNA fragmentation patterns as a functional genomic signal providing insight on genome structure and function, and to highlight the promise of using functional genomic annotation priors for the analysis of cell-free DNA in liquid biopsies

Keywords: Genetics.
Advisor: Curtis Christina
Kundaje Anshul
Greenleaf William James
Sidow Arend
Learning, fast and slow : computation, plasticity and metaplasticity in a neural circuit

Memory—--the ability to transform transient information into a more persistent form---is a fundamental property of neural systems, allowing them to learn from experience. Memories are stored in a variety of substrates, from persistent neural activity to changes in synaptic weights. Experience may even alter the interactions of the molecular networks that support synaptic weight changes themselves. The processes by which memories are stored, maintained and transformed are dynamical, resulting from the interplay of the substrate elements across spatial and temporal scales, fast and slow. Here I use mathematical and computational methods to investigate the dynamics of the storage of a long-term memory during synaptic plasticity, the transfer of long-term memory between synaptic sites during systems consolidation, and the tuning of the rules for inducing synaptic plasticity as a result of metaplasticity. I perform a conceptual unification of short-term and long-term memory, which have long been considered unrelated phenomena, by demonstrating that the consolidation of graded long-term memories in persistent synaptic weight changes is conceptually analogous to the storage of graded memories in persistent neural activity in short-term (working) memory, both processes being described by line attractor dynamics and the operation of temporal integration. Finally, I propose biologically plausible mechanisms for a newly discovered form of metaplasticity that each generate associative synaptic plasticity rules tuned to different features of the correlation structure of inputs to a neuron

Keywords: Bioengineering.
Advisor: Goldman M. R. (Mark R.)
Raymond Jennifer L.
Covert Markus
Nuyujukian Paul Herag
Learning from communities in Oceania to build equitable, just, and resilient fisheries

The nature of seafood in fishing communities is multifaceted. Seafood provides food for billions and is a vital source of food and nutritional security among the world's coastal populations. Seafood is also a globally traded commodity, and luxury seafood products such as sea cucumbers are targeted not only for food security but also for distant, wealthy consumers. Seafoods are also organisms serving ecosystem functions, and their depletion can have downstream effects on ecosystem health. Seafoods also carry cultural meanings, personal associations, and social and political significances. In this dissertation, I examine the multifaceted nature of seafood in Pacific coastal communities, the conflicts that emerge at the intersections, and the ways communities respond and adapt to marketization and depletion of their marine resources. Throughout, I use the lenses of equity and intersectionality to challenge the narrative of fishing communities as monolithic and reveal the necessity of understanding the diversity of assets, access, power, and knowledge of different fishers. In Chapter One, I show how the commodification of sea cucumbers in Palau threatens the intangible cultural heritage of gleaning and its significance as an intergenerational classroom for Indigenous ecological knowledge and values. In Chapter Two, I use an intersectional lens to examine the inequitable impacts of the international sea cucumber trade in Palauan fishing communities, finding that degradation of the fishery disproportionately impacted the least resourced women, entrenching existing inequalities. In Chapter Three, I investigate how and why resistance to the sea cucumber trade emerged in fishing communities in Palau, Yap, and Pohnpei, finding that Indigenous values and struggles for self-determination were central to motivating these movements and that a coalition of actors worked together to ban the trade from local waters. Finally, in Chapter Four, I examine the food security impact of the COVID-19 crisis on rural fishing communities across the Pacific region and the local food practices that were deployed to adapt to food systems interruptions

Advisor: Durham William H.
Micheli Fiorenza
Richmond Robert H. (Robert Hilary)
Seetah Krish
Learning on the job search : how application experiences shape labor market outcomes

Looking for work can be a very fraught experience, filled with immense stress and pressure. The sociological literature, however, has tended to view job searches as simple matching processes, connecting potential employees to employers. In my dissertation, I argue for further interrogation of the job search itself from the perspective of job seekers. In doing so, I highlight the ways in which experiences on the job search come to shape individuals' conceptions about work and about themselves in unequal ways as well as important meaning- and decision-making processes that occur over the course of job search. Drawing on longitudinal interviews with 63 men and women seeking internships and full-time employment in computer science, I find that the actors, processes, and procedures of the job search teach heavily gendered conceptualizations of the field and disproportionately alienate women who were more likely to question their passion for the field and express intentions to leave within five to ten years than their men counterparts. I further show how the processes, actors, and experiences that make up a job search contribute to individual perceptions of competition in the labor market, frequently increasing feelings of pressure and precarity beyond the economic realities of supply and demand. While the impact of this increased pressure was broad, increasing worries and concerns about being competitive enough even among the most privileged job seekers, job seekers with less access to status either through their education institutions or their family class background were more likely to find these experiences played into existing concerns about their belonging in the field. Finally, I additionally examine how job seekers aim to evaluate companies on factors like their diversity and societal impact in the course of a job search, but that doing so is often challenging and ambiguous. Because of this, job seekers frequently considered and applied to companies that they did not feel acted in socially responsible ways and had to manage the tension of seeking employment at firms that do not meet their stated goals. Together, these findings aim to illuminate not just the outcomes but the experience of seeking work in order to begin to unpack the black box of the job search. In doing so, they underscore the unequal and consequential challenges and opportunities that this presents, with important implications for workforce diversity and inclusion as well as the ways in which companies are able to manage potentially negative assessments of their broader social impact

Keywords: Sociology.
Advisor: Grusky David B.
Correll Shelley Joyce
Pedulla David S.
Learning perception and control from rich interactions

Building robotic systems that can perform a wide range of tasks in the real world would require generalizable perception and control. Despite recent advances in deep learning, existing paradigms often rely on extensive human engineering and suffer from limited generalization capability. To handle the desired diversity and complexity in challenging tasks, we would need to further scale up learning by rethinking the methodology for collecting and utilizing data for robots. In this dissertation, we discuss methods that enable robots to enhance perception and control by learning from rich interactions with the environment. We develop structured models and learning algorithms for robots to effectively acquire sensorimotor skills and solve sequential tasks. To scale up learning, we design mechanisms to provide data from various sources. We further propose frameworks that adaptively generate tasks from parameterized task spaces to facilitate curriculum learning and skill discovery

Keywords: Electrical Engineering.
Advisor: Li Fei Fei
Savarese Silvio
Guibas Leonidas J.
Sadigh Dorsa
Learning preferences from choices and rankings

A large and growing experimental literature has shown that individual choices and judgements can be affected by irrelevant aspects of the context in which they are made. Despite these findings, much of the existing modeling work in preference learning still relies on the simplifying assumption that choices come from the maximization of a stable utility function. In this dissertation, we discuss our progress in tractably modeling violations of utility-based reasoning in choices and rankings at scale. First, we describe the context dependent random utility model (CDM), our choice model that captures a broad class of context effects while remaining inferentially tractable. Second, we consider testing when violations of a popular notion of rationality, the Independence of Irrelevant Alternatives (IIA), exist in practice. Our work contributes effective methods for testing IIA and characterizes the fundamental statistical limitations of doing so. Third, we show how our advances in choice modeling can be leveraged to develop the contextual repeated selection (CRS) model of ranking, a model that brings a natural multimodality and richness to the rankings space along with strong statistical guarantees

Keywords: Electrical Engineering.
Advisor: Ugander Johan
Boyd Stephen P. 
Van Roy Benjamin
A less thoroughly holistic neobehaviorism

This dissertation begins to explore the philosophical significance of the thesis that some facts about an agent's mind are facts about the agent's abilities. This is done in two parts. First, certain philosophical accounts of knowledge as a kind of ability are discussed and amended with support from original exegetical work on what Ryle calls 'semi-hypothetical' descriptions. Second, an argument is made that certain psychological explanations (of an agent's conduct) underwritten by facts about the agent's knowledge (understood as an ability) are less holistic in a certain way, and consequently that a philosophical Neobehaviorism that incorporates such forms of explanation may be less thoroughly holistic than it otherwise might be

Keywords: Philosophy.
Advisor: Hills David
Icard Thomas
Lawlor Krista
Leveraging bioinformatics methods to examine sex-related variability in genetic and transcriptomic data

Women are at more than 1.5-fold higher risk for clinically-relevant adverse drug events. While this higher prevalence is partially due to women being overdosed, research suggests that biological sex differences play a role. However, the biological mechanisms behind how sex differences impact drug response are poorly understood. Gene expression and genetic data have the potential to help us investigate these effects, but many studies and samples neglect to include sex labels or examine the impact of sex. Simultaneously, another challenge stems from the overemphasis of certain sex- specific associations without sufficient replication or consideration of other covariates. Consequently, strategic use of data analytic methods is essential for studying sex-related effects. In this thesis, I argue that novel bioinformatics approaches have the potential to improve our understanding of sex as a variable in genetic and gene expression data, and I describe three con- tributions toward this effort. First, I developed a Bayesian Mixture Model approach for identifying sex-specific genetic effects in Genome Wide Association Studies (GWAS). By applying this model to UK Biobank biomarker data, I found that the majority of these clinical lab values do not show sex differences in their genetics, with the exception of testosterone. Second, I addressed the problem of missing sex labels in gene expression data by training models to infer sample sex, and applying these models to over 600,000 publicly available human and mouse samples. I then used these labels to assess sex bias at scale and in drug studies, examine the complexity of cell line sex, and identify mislabeled samples. Finally, I applied our sex labeling models to smoking-related gene expression data, compared the contributions of sex and smoking to expression, and highlighted limitations in our ability to examine sex-differential smoking interaction effects

Keywords: Biomedical Informatics.
Advisor: Altman Russ 
Khatri Purvesh
Rivas Manuel (Manuel A.)
Stefanick Marcia Lynn