VictorJuiz/thesis huggingface dataset:
Reliable Prediction and Decision-Making in Sequential Environments

Building autonomous agents that learn to make predictions and take actions in sequential environments is a central problem in artificial intelligence, with applications as diverse as personalized medicine, self-driving cars, finance, and scientific discovery. Despite impressive success in certain areas such as natural language, games, and robotic demonstrations, sequential prediction and decision-making remains challenging in the absence of known models, accurate environment simulators, short-range dependencies, and large and diverse datasets./nIn this thesis, we formulate problems to capture challenging yet prevalent settings encountered in the real-world. Given the formulations, we then design reliable and efficient learning algorithms, leveraging recent advances in statistics and optimization. In the first part of the thesis, we consider the problem of learning to make predictions in unknown and only partially observed linear dynamical systems. Contrary to prior predictive models which fail in the presence of long-range dependencies, we design an algorithm that provably returns near-optimal predictions regardless of the system's degree of stability and forecast memory./nIn the second part, we shift our attention to reinforcement learning (RL), the problem of learning to make decisions in an unknown sequential environment. We start by focusing on the offline setting, where the agent is only provided with a previously-collected dataset of interactions and does not have further access to the environment. We propose a new framework to study offline learning problems given datasets of any composition, ranging from expert-only to uniform coverage, and thus unifying two main offline learning paradigms: imitation learning and vanilla offline RL. Equipped with this framework, we design an algorithm based on pessimism in the face of uncertainty and prove that it is nearly optimal for any, possibly unknown dataset composition./nWe then turn to the online setting, where the agent learns while interacting with the environment. In this setting, the agent faces a dilemma in each step: whether it should exploit the current knowledge and select a seemingly optimalaction or it should explore and visit different regions of the environment. We propose a framework that unifies common exploration methods by adding an adaptive regularizer to the standard RL objective. We show that a particular regularizer design yields a simple optimistic exploration strategy that enjoys fast optimization and efficient exploration, achieving state-of-the-art performance in several locomotion and navigation tasks when combined with deep neural networks.

Advisor: Russell, Stuart
Jiao, Jiantao
Learning-Based Program Synthesis: Towards Synthesizing Complex Programs from Multi-Modal Specifications in the Wild

With the advancement of modern technologies, programming becomes ubiquitous not only among professional software developers, but also for general computer users. However, gaining programming expertise is time-consuming and challenging. Therefore, program synthesis has many applications, where the computer automatically synthesizes programs from specifications such as natural language descriptions and input-output examples. In this dissertation, we present our work on learning-based program synthesis, where we demonstrate deep learning techniques for synthesizing programs from different specification formats./nFirst, we present our work on synthesizing programs from multi-modal specifications with real-world applications. In particular, our SpreadsheetCoder work has been integrated into Google Sheets to support the formula suggestion feature, showing the power of learning-based program synthesis in real products. Second, we present our work on execution-guided program synthesis, which brings significant performance gain for synthesizing more complex programs from input-output examples. Our work on program translation and code optimization then demonstrate the importance of representing the program structures and designing learning algorithms correspondingly, which improve the generalization of the learned model and the complexity of programs that can be correctly generated. Finally, our work on neural-symbolic frameworks show that integrating symbolic components into neural networks empower the models with better reasoning and generalization capabilities.

Advisor: Song, Dawn
Mechanisms of Lineage Divergence in the Radiation of Fanged Frogs (Genus: Limnonectes)

In the 300 million years since the divergence of anurans and caudates, frogs have come to represent nearly 90% of all amphibians and (with the exception of Antarctica) occupy most of the major landmasses on Earth. The range of ecological specializations and phenotypes characteristic of frogs facilitated their colonization of every major habitat type from deserts to rainforests. Given current global ecological conservation concerns, there is an ever-increasing need to assess the drivers of radiative herpetological diversity in areas of especially high species richness and endemism. One such hotspot of global endemism and diversity is the Indonesian island of Sulawesi. Though the biological significance of Sulawesi taxa was noted by Alfred Russel Wallace as far back as the 1800’s, in recent years, investigators have not only identified a preponderance of new species on Sulawesi, but several assemblages representing remarkable radiations of novel species./nHerein, I present research that focuses on a recently discovered Sulawesi radiation: that of the so-called ‘fanged frogs’ of genus Limnonectes. This poorly studied assemblage likely includes ~40 species, though only five of have been formally described due to unresolved degrees of morphological, ecological, and molecular disambiguation across species. Very little is known about Sulawesi fanged frog natural- and life histories; thus, the research presented in this compendium explores the ecological mechanisms that facilitated the Sulawesi Limnonectes radiation in effort to counterbalance the current dearth of baseline knowledge about the species comprising this assemblage. To accomplish this objective, I conducted extensive field work across the island and subsequently amalgamated molecular, morphometric, behavioral, physiological, and ecological research applications to characterize differential niche use, reproductive biology, and lineage divergence. In Chapter 1, I describe and diagnose a new terrestrially-nesting Limnonectes species from South Sulawesi. In Chapter 2, I explore cryptic speciation and highlight the discovery of replicate elevational speciation events in a locally sympatric cohort of fanged frog eco-morphs. In Chapter 3, I characterize the eco-physiological drivers of niche partitioning as they apply to cutaneous water loss, behavioral hydro-regulation, and desiccation tolerance. This body of work advances our understanding of Sulawesi fanged frog natural history and underscores the utility of integrative biological research applications for the purposes of determining the interplay between speciation mechanisms and ecological interactions.

Advisor: McGuire, Jimmy A
Singularity Theory for Extended Cobordism Categories and an Application to Graph Theory

In low-dimensional topology one often decomposes a space into pieces that can be individually understood then put back together, yielding a combinatorial description, and such descriptions can be used for diagrammatic reasoning, computations of algebraic invariants, constructions of new objects, and so on. A powerful tool for this is singularity theory, where a classification of the types of singularities that generically might be present (for example, Morse critical points) leads to systematic approaches to decompositions, and in the ideal case the classification reduces to analyzing singularities of polynomial functions. We develop singularity theory relevant to "n-categorical" decompositions of smooth manifolds, in which pieces each have a recursive decomposition of their boundaries. In particular, we study smooth functions from manifolds to flag-foliated R^n, which serves as a model for the composition laws of an n-category. We use a refinement of the jet transversality techniques for the Thom--Boardman singularity types to define dense sets of functions that are suitably generic with respect to the foliations. We show how to compute codimensions of the submanifolds that correspond to a singularity type. For a few situations, for instance surfaces with embedded curves, we carry out a classification of the germs that appear in this dense set up to structure-preserving diffeomorphism. To this end, we prove stability results that aid in proving equivalence of germs and in finding polynomial representatives./nWe explain how to use this classification of singularities to give a presentation of the symmetric monoidal 2-category of curves with embedded curves. As an application, we give a way to compute the Krushkal polynomial, which is a combinatorial invariant of graphs in surfaces, using an extended TQFT on the symmetric monoidal 2-category of surfaces that are decomposed into black and white regions.

Advisor: Agol, Ian
From Test Tube to YouTube: Public Displays of DNA Ancestry Tests Results and the Performance of Race in America

The consumer DNA ancestry testing industry was estimated to have served two million customers in 2015 (Nelson, 2016). DNA ancestry testing is permeating media outlets and proliferating in forms of social media and popular culture. Viewers see performances of genetic ancestry test results on news programs, talk shows, educational programming, and social media sites. Given how ubiquitous DNA ancestry testing is in American households, and how often test results are being shared on social media sites such as YouTube, I argue that we can use DNA test reveals to ascertain the way race is viewed, performed, and negotiated. My research asks, what do the social media performances of DNA Ancestry tests tell us about how test takers see race and ethnicity? Do test-takers change their identity affiliations or the ways they view themselves after reading the results? If so, do test-takers rely more on the genetic component of identity or are there other criteria that have more weight? The performative, dramatic nature of genetic results being read in front of an audience means we can use these reveals to understand what people consider the appropriate criteria to belong to an ethnic or racial group, and to see what conditions cause people to change identity or reject the test results.

Advisor: Elliott, Chiyuma
Exploration of Surface Processes for Semiconductor Processing and Heterogeneous Catalysis

Surface properties can behave drastically different from bulk properties. Moreover, these properties are harder to model and compute. However, key industrial processes such as catalysis, plasma etching, and solution-processed assembly techniques are surface driven, and thus computing, modeling, and exploring these surface phenomena are of importance. In this dissertation, the surface driven phenomena of photocatalytic CO2 reduction is explored through the lens of high-throughput density functional theory. Methodology is developed to screen potential photocatalysts and applied to novel tellurium-containing semiconductors. As a result, important chemisorption, charge transfer, and bonding trends are discovered. Additionally, a high-throughput methodology is developed to screen potential etchant gases on amorphous materials. This methodology is critical to the processing of microelectronic devices. Finally, in the realm of semiconductor processing, an experimental methodology to assemble perovskite nanoparticles into superlattices is researched. Emergent properties of these superlattices are explored. The processing techniques exploits surface driven phenomena. The findings discovered and presented in this dissertation will be impactful for two critical areas of industry: catalysis and semiconductor processing.

Advisor: Persson, Kristin
Integrated Node-Pore Sensing Techniques for Label-Free Single-Cell Measurements

Microfluidic devices that can analyze single cells at high throughput are powerful tools in biomedical research, and many such technologies have been developed in recent years. Node-pore sensing (NPS) is one such platform, and NPS devices have been developed that measure a variety of both physical and molecular cell properties. The NPS platform benefits from being inherently label-free, meaning that cells do not need to be tagged with fluorescent or magnetic labels. Additionally, as NPS relies on an entirely electronic measurement, it is a robust system that is relatively low-cost to implement. These factors make NPS an attractive platform for biomedical research because it is accessible and easy to use while enabling multi-parameter single-cell measurements. This platform has already led to several new biological discoveries, although it still has some technical limitations. This dissertation focuses on characterizing the NPS platform and extending its technical capabilities. First, we present a quantitative study demonstrating the user-friendliness and reliability of our custom NPS data processing software. Second, we describe the theoretical framework and experimental characterization of a novel method to achieve multichannel NPS measurements for the first time. Finally, we introduce a new NPS platform with the unique ability to combine single-cell label-free mechanical and biomolecular measurements. These advancements pave the way toward next-generation, fully integrated NPS devices that can provide new insight into biologically and clinically important questions.

Advisor: Sohn, Lydia
The Serverless Datacenter: Hardware and Software Techniques for Resource Disaggregation

Datacenters have grown beyond a simple collection of independent computers. They are now a complex and interconnected ecosystem of heterogeneous hardware and software services: a warehouse-scale computer. These computers are wildly expensive to provision and operate, yet we struggle to effectively utilize them. It is not uncommon to have half of allocated resources unused, while other resources cannot be allocated at all. Resource needs vary widely, both between jobs, and even over time within a single job. When we aggregate resources into fixed “slots” (i.e., servers), we take away the flexibility needed to accommodate these varying needs. I propose a different approach: resource disaggregation. Rather than requiring the system to fit jobs into fixed-sized servers, we make any resource in the system available to any job (physical disaggregation). Rather than requiring jobs to allocate all their resources up-front, we allow them to allocate resources only when they actually need them (logical disaggregation). I argue that unlocking the full potential of physical disaggregation requires moving logical interfaces to a fundamentally disaggregated paradigm. Likewise, logically disaggregated systems can provide some benefit on today’s hardware, but only reach their full potential when co-designed with physically disaggregated hardware. In this dissertation, I present tools and methodologies I developed to support that hardware/software co-design. I then describe how I used these tools to implement a simple hardware accelerator that works with the operating system to improve the performance of physically disaggregated memory. I evaluate it with end-to-end benchmarks in RTL simulation and find that it reduces the latency of remote memory access by 2.2x and improves end-to-end performance by 20% over a software-only approach. Next, I show how I extended the logically disaggregated serverless programming model to heterogeneous compute resources. My prototype achieves 50x better performance with fewer resources than today’s aggregated approaches. Together, these techniques form a vision of a serverless datacenter that unlocks the promise of pay-per-use and rapid innovation that warehouse-scale computers should provide.

Advisor: Gonzalez, Joseph E
Katz, Randy H
Leveraging Xenopus frog species to study architectural diversity of the mitotic spindle and mechanisms underlying hybrid inviability

Cell division is crucial to life. Across all eukaryotes, this dramatic and complex process relies on the mitotic spindle to segregate chromosomes faithfully into new daughter cells, ensuring the health and survival of the next generation. Despite its conserved components and universal function of transmitting a complete genome, the cell division machinery, including the central mitotic spindle, varies widely across the tree of life in size and morphology. Interestingly, chromosome segregation defects, genome elimination, and embryonic lethality are frequently observed upon hybridization of closely related species. However, whether and how divergent cell division machinery leads to functional defects is unknown. This dissertation is comprised of four projects that span comparative evolutionary, cell, and developmental biology to examine the basis and consequences of divergent cell division machineries by using X. laevis, X. tropicalis, and X. borealis frogs. I develop the use of X. borealis to understand divergence in spindle assembly mechanisms and examine how spindle architecture varies even among closely related frog species. I adapt expansion microscopy for Xenopus extract systems, combining an unparalleled in vitro physiological reconstitution system with high-resolution imaging to measure and dissect microtubule organization to better define spindle morphometrics. We leverage the full power of the Xenopus systems to establish the most tractable vertebrate model for investigating the cellular and molecular mechanisms underlying hybridization outcomes, incompatibility, and genome elimination. Altogether, this work provides important molecular insight and understanding to mechanisms that contribute to spindle assembly and scaling, hybrid inviability, and speciation.

Advisor: Heald, Rebecca
Building Assistive Sensorimotor Interfaces through Human-in-the-Loop Machine Learning

One of the outstanding challenges in the field of human-computer interaction is building assistive interfaces that help users with the perception and control of complex systems, such as cars, quadcopters, and prosthetic limbs. In this thesis, we propose machine learning algorithms for automatically designing personalized, adaptive interfaces that improve users' performance on sequential decision-making tasks. First, we present work that uses theory of mind to model irrational user behavior as rational with respect to incorrect internal beliefs about how the world works, and leverages this assumption to assist users by modifying their observations and actions. Second, we present work that uses model-free reinforcement learning from human feedback to fine-tune user actions, with minimal assumptions about user behavior. We demonstrate the effectiveness of our methods through experiments with human participants, in which users play the Lunar Lander video game, perform simulated navigation tasks, and land a quadcopter.

Advisor: Dragan, Anca
Levine, Sergey
Parental Expressions of Love and Care among Chinese Immigrant Families

Parents’ communication of love and care to their child is arguably a universal behavior found among all species. Yet expressions of human love and care are multi-faceted and found to be culturally-varied. In this dissertation, I examine Chinese American parent-child conversations as a case for the multiple ways in which parents express love and care to their child, building on cultural theories about emotion and parenting./nIn a sample of 1st generation Chinese American immigrant parents (N =110) and their children (M = 9.16 years old), I measured cultural orientation, parenting style, and expressions of love and care during a dyadic affection discussion task. Three types of affection styles were found: training (guan), relational affirmation, and validation. Results showed that Chinese parents with lower American orientation discussed training more often to express affection. In addition, socioeconomic status (SES) was found to be a robust predictor of affection style. Higher SES was associated with validation, whereas lower SES was associated with training and relational affirmation. Finally, in moments when parents spoke Chinese, they were more likely to discuss training and relational affirmation, whereas parents who used more English were likely to use validation to express affection toward their child./nCultural theories of emotion and parenting may explain varied expressions of affection styles, including training, relational affirmation, and validation. Beyond the static view that immigrant families espouse heritage and host cultural values, the present dissertation demonstrates how these multiple cultural practices dynamically unfold in conversation. Bilingual code-switching found in these family conversations of love and care may reflect one way in which immigrant parents adopt the new unfamiliar context, while also respecting the old familiar context, thereby bridging the emotional acculturation gap.

Advisor: Zhou, Qing
Cell death in the intestinal epithelium: the molecular basis for mouse resistance to Shigella flexneri infection

Shigella species, the causative agents of bacillary dysentery or shigellosis, infect more than 250 million people each year and are a major driver of diarrheal associated morbidity and mortality world-wide. Shigella induces severe gastrointestinal disease by colonizing and disseminating within the epithelial lining of the human colon and rectum, events that drive significant inflammation in the gut. Despite this heavy disease burden, little is known about the molecular determinants of pathogenesis during infection and the adaptive immune correlates of protection up re-infection, due largely to the lack of an inexpensive, tractable, and physiologically relevant mammalian model of shigellosis. Indeed, a detailed understanding of the immune response to this pathogen is critical for the development of vaccines to Shigella, none of which has been effective enough at eliciting protection to be licensed for use in humans. Mice are an ideal system to model human specific disease because of their short generation time, the relatively low cost and effort to maintain a mouse colony for research, and the extensive immunological and genetic tools that have been established in this organism over the past half-century. Wild-type laboratory mice, however, are intrinsically resistant to high doses of oral Shigella challenge and the genetic and mechanistic underpinnings of this resistance have remained largely unknown. My dissertation examines the host innate immune factors in mice that dictate resistance to oral Shigella infection. In the first chapter I review what is currently known about Shigella pathogenesis, discuss the animal models that have been used to establish this understanding, and provide background on innate immune pathways in humans and mice that sense Shigella. In the second chapter I describe the genetic basis of resistance to Shigella infection in mice and show that NAIP–NLRC4 inflammasome-deficient mice are susceptible to Shigella infection and serve as a physiologically relevant model of dysentery. In the third chapter, I use this model to expand our understanding of innate immune resistance to Shigella infection and show that a redundant, layered hierarchy of cell death in intestinal epithelial cells is key to this resistance. Finally, in the fourth and final chapter, I describe how our findings advance the field and comment on how our new model of Shigella infection can be leveraged to learn more about the immune response to this deadly intestinal pathogen.

Advisor: Vance, Russell E