VictorJuiz/thesis huggingface dataset:
Robustness in machine learning and optimization, with limited structural knowledge

In this dissertation, we develop and analyze algorithms for robustness in three different machine learning settings. In the first part of the dissertation, we introduce the problem of hidden stratification — which is when a classification model substantially underperforms on certain unlabeled subclasses of the data — and propose a method to detect and mitigate this issue. Previous works studied how to address this in the setting where the subclass labels are known. Based on the empirical observation that unlabeled subclasses are often separable in the feature space of deep neural networks, we instead estimate subclass labels for the data using clustering techniques. We then use the estimated subclass labels as a form of noisy supervision in a distributionally robust optimization objective, in order to train a model that is more robust to inter-subclass variations. We demonstrate the effectiveness of our approach on several robust image classification benchmarks. We briefly discuss alternative methods for 1) utilizing a limited number of subclass labels to further improve performance, and 2) using contrastive learning to learn representations less susceptible to hidden stratification. In the second part of the dissertation, we study the problem of evaluating classification models under structured distribution shifts. Given a labeled sample from a "source" distribution and an unlabeled sample from the "target" distribution, importance weighting is the standard approach to perform such evaluations; however, importance weighting can struggle in high-dimensional settings, and fails when the support of the target distribution is not contained in that of the source. We show that one can sidestep these issues with some foreknowledge of the nature of the distribution shift; specifically, we present an algorithm that uses user-defined "slicing functions" — binary functions intended to capture possible axes of distribution shift — to estimate performance on the target distribution. We theoretically characterize the robustness of our approach to noise and incompleteness in the slicing functions, and empirically verify its effectiveness on a variety of classification tasks. In the third part of the dissertation, we develop an accelerated gradient method to efficiently minimize a class of smooth structured nonconvex functions which we term "quasar-convex" functions. Our algorithm is a generalization of the classic accelerated gradient descent method for convex functions, and is robust to possible nonconvexity between algorithm iterates. We provide upper and lower bounds on the number of first-order evaluations that our algorithm requires to find an approximate optimum, which show that our algorithm has optimal complexity up to logarithmic factors

Advisor: Re Chris
Hashimoto Tatsunori
Sidford Aaron
The role of hyaluronic acid in regulating inflammation and the immune response

Hyaluronan (HA) is a disaccharide polymer consisting of glucuronic acid and N-acetyl glucosamine that dominates the extracellular space in the eukaryotic body. As part of the pericellular matrix (PCM), HA is extruded outward from the cell membrane in the mesh of proteins and sugars that make up the glycocalyx. This thick coat, consisting predominantly of HA, dominates the interface between a cell and its environment. HA plays an important role in cell communication within the extracellular space as well as tissue structure. In this thesis I will discuss the biological role of HA in chronic and acute disease. I focus on how dysregulation of HA production potentiates pathophysiology of three different inflammatory states: chronic asthma, type II diabetes, and solid organ transplantation. Moreover, I present investigations into 4-MU treatment to protect against inflammatory pathologies. Lastly, I present investigations looking at developing new interventions that can add to our repertoire of HA targeting therapies

Keywords: Microbiology and Immunology.
Advisor: Bollyky Paul
Engleman Edgar G.
Heilshorn Sarah
Maltzman Jonathan
Meyer Everett
The role of response modes in consumer judgment and choice

Marketing research has predominantly focused on how and what consumers choose. However, most options considered by consumers are rejected (i.e., not chosen); yet, the study of how consumers make rejections has not received much attention. While choosing a good option and rejecting a bad option had long been regarded as nominally the same decision, a growing body of research has uncovered that these two response modes differ in their psychological processes and behavioral consequences. Recognizing this discrepancy between choice and rejection, my dissertation explores the impact of response modes on product evaluations and decisions, with a focus on contexts in which consumers make rejections

Keywords: Business.
Advisor: Simonson Itamar
Huang Szu-chi
Levav Jonathan
RRAM compute-in-memory hardware for efficient, versatile, and accurate AI inference

Performing ever-demanding artificial intelligence (AI) tasks directly on the resource-constrained edge devices calls for unprecedented energy-efficiency of edge AI hardware. AI hardware today consumes most energy through data movement between separate compute and memory units. Compute-in-memory (CIM) architectures using resistive random-access memory (RRAM) integrated on the CMOS logic platform alleviate this challenge by storing AI model weights in dense and analog RRAM devices, and performing AI computation directly within RRAM, thus eliminating explicit weight memory access. To date, AI application-level benchmarks on fully integrated RRAM-CIM hardware have been limited in diversity and complexity. Meanwhile, the energy-efficiency benefit of CIM usually comes at the cost of functional flexibility and computational accuracy, hampering its practical use at the edge. Such trade-offs between efficiency, versatility and accuracy cannot be addressed by isolated improvements on any single layer of the design. As our attempts to ameliorate this fundamental trade-off, this thesis will present two RRAM-CIM chips that I led in design, tape-out, and test. The first chip contains a single RRAM-CIM core with 65K RRAM devices; the second chip, named NeuRRAM, integrates 48 RRAM-CIM cores with a total of 3M RRAM devices. The two chips integrate multiple innovations including a voltage-mode sensing scheme, a transposable neurosynaptic array architecture, and non-ideality-aware model training and fine-tuning techniques. By co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, the two chips simultaneously deliver a high degree of versatility in reconfiguring CIM cores for diverse model architectures, higher energy- and area-efficiency than prior arts across various computational bit-precisions, and fully hardware measured accuracy comparable to software models across diverse AI benchmarks, including MNIST and CIFAR-10 image classifications using convolutional neural networks, Google speech command recognition using a long short-term memory, and MNIST image recovery using a Restricted Boltzmann Machine

Keywords: Electrical Engineering.
Advisor: Wong Hon-Sum Philip
Cauwenberghs Gert
Raina Priyanka (Assistant Professor of Electrical Engineering)
Rural and urban board members' identities and their attitudes toward representation in school policy and decision making

With over 90,000 school board members in the United States, school boards have been influential in the desegregation of schools. More recently, have influenced mask mandates in schools, banning of books, and policies regarding equitable education. Previous research has addressed the impact of direct representation and the positive influence that it may have on communities and students. Additionally, research shows how responsive and sensitive communities can be to school boards. Communities respond in elections, re-elections, and attitudes towards schools. My research asks about how rural and urban board members align their identities with their decisions on the board. Using qualitative data, I find that the most significant differences between urban and rural board members are the values they call upon to enact. These values are experiences as people of color and being a conservative religious person.

Keywords: Education Honors Program
School board members
School boards
Representation
Rural
Urban
Decision Making
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Thesis
Book
Advisor: Bromley Patricia
Sandlin Honors Thesis: Urban Indigenous Self-Expressionism from 1960s to Present and its Impact on Indigenous Identity

This paper aims to study how urban North American Indigenous communities represent their identities and cultures; specifically, it will analyze how these practices have evolved since the rise of Native activism in the mid-20th century, catalyzed by the creation of the American Indian Movement (AIM) in the late 1960s. As there is little existing research in this field, especially by Indigenous scholars, my argument will be primarily circumstantial and will advocate for further development of the analysis. Furthermore, as my research is interdisciplinary, I will utilize multiple theoretical frameworks and contextual analyses to develop my argument and subsequent close visual analysis. More specifically, I will utilize the frameworks from the fields of visual studies and Native studies in formulating my work. I assert that AIM and the rise of Native representation has forced the American public to reconcile with contemporary self-representation which has subsequently shaped modern Native and urban Native identity.

Keywords: Indigeneity, photography
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Thesis
Scalable inference for crossed random effects models

Crossed random effects models are widely used in statistics for modeling data with crossed structure with applications ranging from political science to recommendation systems. However, the computational costs of estimation and inference of the crossed random effects models limit its application on large-scale data sets. With N observations, the cost of computing maximum likelihood estimates scales as O(N^{3/2}) at best. A similar situation arises for Bayesian inference, where the Gibbs sampler takes O(N^{1/2}) iterations to converge, leading once again to a total cost of O(N^{3/2}). In this talk, we propose a variant of backfitting algorithm in the context of likelihood based inference to compute a generalized least squares estimate each iteration of which costs O(N) and provably converges in O(1) iterations. We illustrate our method on a data set from Stitch Fix. We shall also briefly discuss provably-scalable variants of standard approaches besides likelihood based inference

Keywords: Statistics.
Advisor: Hastie Trevor
Owen Art B.
Athey Susan
Linderman Scott
Scattering studies of magnetic and charge correlations in two-dimensional quantum magnets

The study of quantum magnets in two-dimensional systems is a forefront field in both materials science and condensed matter physics research. Such materials not only provide rich platforms for potential applications of novel materials but also further our understanding of basic physics. Neutron and x-ray scattering are powerful tools to explore the magnetic and charge correlations in these materials. This thesis describes the experimental investigation of two model systems in bulk crystalline form, one with a two-dimensional kagome lattice and one with a two-dimensional square lattice, both consisting of Cu2+ ions with spin-1/2. Strong quantum fluctuations give competing ground states or phases in both systems. Recently, barlowite Cu4(OH)6FBr has attracted much attention as the parent compound of a new kagome quantum spin liquid candidate. Our new synthesis method produced large single crystals of a new variant of barlowite, which has a higher symmetry space group compared to the previously reported one. Our elastic magnetic neutron scattering measurements reveal that the magnetic ground state of the new high-symmetry barlowite at low temperatures has a special pinwheel q = 0 magnetic order. In the intermediate temperatures, our numerical calculations and inelastic neutron scattering indicate that the kagome spins are in a potential pinwheel valence bond crystal state. Both of the two ground states are in close proximity to the long-sought quantum spin liquid state. The second part of my thesis is focused on the spin and charge correlations in high-Tc cuprate superconductors. Cuprate materials display intriguing physical phenomena due to the coexistence of various phases, which may interact with the superconductivity. The La-based family is a canonical example where both the spin and charge correlations could form "stripes". Magnetic neutron scattering reveals a rather surprising feature of the spin stripes in the model material La1.88Sr0.12CuO4: regardless of whether they are static or fluctuating, these stripes are always tilted from the high-symmetry direction of underlying lattices. Comparison with numerical simulations reveals the important role of the next-nearest neighbor electron hopping. Resonant soft x-ray scattering measurements offer important information about the charge order in another cuprate material stage-6 O-doped La2CuO4+y. By tuning both temperature and the dopant disorder level, we clearly observe two types of charge orders with distinct correlation lengths and behaviors at low temperatures. A coherent picture based on microscopic phase separation is proposed to explain the relationship between charge and spin orders with superconductivity in these materials

Keywords: Materials Science and Engineering.
Advisor: Lee Young Sang
Lindenberg Aaron Michael
Clemens Bruce A.
Scope 3 Emissions from Purchased Goods: Using Food Purchasing as a Case Study for Best Practices

This work answers questions relevant to the determination of Scope 3 emissions from purchased goods: 1) how does the use of weight-based versus spend-based emissions factors (EF) affect the magnitude of emissions calculated, 2) how does using different sources for emissions factors within a same type of EF affect overall estimated emissions, and 3) what is needed to standardize and ease calculation of Scope 3 emissions across institutions, countries, and backgrounds such that the process can be automated and ensure replicable results. To answer these questions, a collaboration with seven universities across the United States was formed to share best practices and food purchasing data. This collaboration enabled the development of a python-based categorization tool to ease and standardize calculation of Scope 3 emissions from purchased foods and helped validate and expand the applicability of the results and methodologies. To understand the impact of different emissions factors on calculated emissions outcomes, the Stanford 2019 food purchasing data was used as a case study and both weight-based (kg CO2-eq/kg item purchased) and spend-based (kg CO2-eq/$ item purchased) EFs were applied to this data set. It was found that for the 2019 Stanford food purchasing case study, the largest estimated emissions were about 2.5times larger than the smallest estimated emissions, and although the range of estimated emissions for the weight-based factors is larger than the range for the spend-based factors, the average is remarkably similar at 10,543 and 11,282 metric tons of CO2-eq respectively.

Keywords: Scope 3 Emissions
Food Categorization
Purchased goods
Food purchasing emissions
Categorization Script
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Thesis
Advisor: Benson Sally
A search for non-resonant HH -> bbbb at sqrt(s) = 13 TeV with the ATLAS detector -or- 2b, and then another 2b ... now that's the thesis question

The Standard Model (SM) characterizes the fundamental building blocks of nature, with its experimental characterization culminating with the discovery of the Higgs boson a decade ago. While subsequent Higgs measurements have thus far confirmed the SM predictions, a critical test for our understanding of electroweak symmetry breaking is to study the shape of the Higgs potential by measuring the Higgs self-coupling — which can be probed by searching for events with two Higgs bosons (HH). This thesis presents a search for HH production with each Higgs subsequently decaying into two b-quarks. The extremely small predicted HH signal, coupled with a complex background that cannot be effectively simulated, makes this analysis an ideal test bed for machine learning applications. This thesis also presents the developments in novel algorithms for b-quark jet identification and background estimation that build on recent advancements in machine learning (including deep sets, neural reweighting methods, and normalizing flows) that have been crucial to the HH analysis. The HH -> 4b analysis sets an observed (expected) upper limit on the HH SM cross-section of 5.4 (8.1), a 30% improvement on the previous analysis strategy. We additionally constrain the Higgs self-coupling in the kappa framework with observed (expected) limits of [-3.9, 11.1] ( [-4.6, 10.8] )

Keywords: Physics.
Advisor: Su Dong (Computer scientist)
Burchat P. (Patricia)
Kagan Michael
Peskin Michael Edward
Searching for Compassion in Compassionate Release: Protecting the Health of People Experiencing Incarceration

Compassionate release - the release of incarcerated people with medical conditions from
prisons and jails - is a widely underutilized mechanism. Releasing people with medical
conditions from carceral settings can protect individuals’ health - especially during crises such as
the COVID-19 pandemic - and can help promote the health of all people experiencing
incarceration by reducing overcrowding. More literature is needed to understand the barriers and
facilitators to compassionate release to encourage its utilization. The first section of this thesis is
a scoping review that maps existing literature on compassionate release to Bronfenbrenner’s
Ecological Systems Theory to understand key recommendations for improving compassionate
release and where there are gaps in the literature. Findings from this review demonstrate key
recommendations such as establishing greater transparency in the application process, ensuring
accessibility of applications based on literacy levels, and establishing medical-legal partnerships.
Identified gaps for future research include understanding racial disparities in compassionate
release as well as understanding perspectives of caregivers of previously incarcerated
individuals. The second section of this thesis presents a survey study on perceptions towards
compassionate release from jail staff in California during the COVID-19 pandemic. Results
highlight that not all jails in California use compassionate release widely and compassionate
release remains inaccessible for people such as those with respiratory illnesses and people
convicted of violent crimes. Engaging carceral staff using evidence-based recommendations is
crucial in expanding compassionate release. Implementing these recommendations and
researching identified gaps can inform policy and practice to promote the health of people
experiencing incarceration.

Keywords: Medicine
Incarceration
Public health
Social justice
Health disparities
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Thesis
Advisor: Halpern-Felsher Bonnie
Brown-Johnson Cati
Kramer Camille
Segregation and inequality across neighborhoods, classrooms, and police jurisdictions

Our lives are profoundly shaped by our interactions with public institutions and by the characteristics of those particular institutional units with which we interact. Institutions group populations and spaces into manageable units that operate under distinct conditions. This differentiation is often accompanied by unequal provisions of public goods across units, promoting social inequality, and frequently contributes to disparities as these unequal provisions map onto the segregation of social groups across units. Improving our public institutions requires a detailed picture of how geographic and institutional boundaries divide populations and configure the unequal distribution of public goods. To that end, this dissertation considers spatial segregation, institutional segregation, and institutional inequality with respect to three institutions: housing, schooling, and policing. Chapter 1 provides a formal framework for understanding and overcoming bias in measures of residential income segregation. Chapter 2 uses an innovative approach to studying segregation at small scales to uncover and explain the high nationwide prevalence of racial classroom segregation in Brazil. Chapter 3 uses novel data to estimate benchmarked police homicide rates, finding substantial inequality in police deadliness among US police departments that face similar risks to officer and public safety

Keywords: Sociology.
Advisor: Grusky David B.
Reardon Sean F.
Finch Brian K. 
Torche Florencia
Selectional preferences and semantic similarity in adjectives

In this thesis, I study the variables that affect the selection between semantically similar adjectives in English, such as ``big''/``large'', ``high''/``tall'', and ``fast''/``quick''. Currently, there is no general model for predicting the selection between adjectives that compete on a given meaning. The proposals that do exist focus on specific pairs of adjectives rather than the selection between them across the board. I attempt to bridge this gap by integrating models of lexical competition with theories of adjective semantics. I propose a novel distinction between an indicative property of an adjective and its focal dimensions. The former is the core meaning of the adjective, and it is an abstract property with undetermined variables. The latter are possible value assignments for these undetermined variables, which can saturate the indicative property in different ways depending on which ones are selected. I show how these two meaning components interact to yield the selectional preferences expressed by adjectives. Motivated by this theoretical analysis, as well as well-known approaches to pragmatics, I raise the hypothesis that semantically similar adjectives should express a distributional pattern in which the rarer one conveys a more specialized meaning than the more frequent one. I operationalize this hypothesis using the Rational Speech Acts framework, and demonstrate through a series of quantitative studies how it predicts the empirical distribution of adjectives

Keywords: Linguistics.
Advisor: Potts Christopher
Condoravdi Cleo A.
Lassiter Daniel
A self-resistance guided approach to the discovery of novel antibiotics

Due to the rise in antibiotic resistance among common pathogens, there is a clear and growing need for novel antibiotics that might bypass existing resistance or breath new life into old drugs. Traditional methods of antibiotic drug discovery have increasingly failed due to rediscovery of old antibiotics. In this dissertation, we first review many of the current strategies towards antibiotic drug discovery and explore emerging tools in this area. We then leverage one of these tools, self-resistance guided genome mining, in combination with an in-house list of polyketide synthase containing biosynthetic gene clusters. Using this approach, we are able to identify and characterize a novel antibiotic from a source that has not traditionally been considered a major source of useful natural products. In doing so, we not only provide a new molecule to aid in the fight against growing drug resistance, but provide validation for the utility of self-resistance guided genome mining as a tool in this effort

Keywords: Chemistry.
Advisor: Khosla Chaitan
Du Bois Justin
Wandless Thomas