VictorJuiz/thesis huggingface dataset:
Designing for Reliability in Algorithmic Systems

As we introduce complex algorithmic systems into decision-making in high-stakes domains, system designers need principled approaches to help people set their expectations of these systems, and give them mechanisms for recovery when these systems fail. For example, a doctor using a machine learning-based system in clinical care needs to know when they can expect the model to perform well, and when they should not rely on its output. However, supporting these kinds of judgments is difficult when stakeholders have conflicting needs and goals, or cannot directly assess the quality of a system's output. This dissertation examines two such contexts: matching algorithms for assigning students to public schools; and machine translation systems, which use machine learning to translate between natural languages. I take three approaches to design for reliability in these contexts: first, teaching users what a system can and cannot do; second, aligning system evaluations with people's actual use cases, needs, and goals; and third, helping users recover from failures. By making it easier for users to understand what kinds of inputs are supported by a system, and how they can express their intent within that supported scope, we can increase users' agency to make appropriate decisions about how and when to use these systems in high-stakes settings.

Advisor: Salehi, Niloufar
Parameswaran, Aditya
An Integrated Circuit Design Framework for Human, Computer, and ML Designers

Analog and custom circuits have long been a bottleneck to the integrated circuit design process. Automation generation of such circuits has long been a topic of research, but has failed to break through to popular practice. This work introduces a modular framework including a cloud-native IC design database, an analog circuit programming framework, a web-native schematic system, and tools for directed programming and automatic compilation of semi-custom IC layout. Highlighted applications include wireline transceivers and data converters, including a recent prototype ADC targeted for neural sensing applications, and research infrastructure for distributed, machine learning based circuit optimization.

Advisor: Stojanovic, Vladimir
“Discarding That, Adopting This” 去彼取此: The Northern Wei and Stories of Chinese Legal History

Whether they know it or not, many views of Chinese legal history continue to rely on the idea that Chinese law became thoroughly Confucian thousands of years ago and has since resisted all efforts to adjust that underlying philosophy. This “Confucianization” hypothesis represents a consequential misunderstanding: in the US, it adds fuel both to increasingly dangerous Sino-American hostilities and anti-Asian violence, while in China it underpins the government’s ethno-nationalist expansionism in Tibet, Xinjiang, and Hong Kong. This dissertation begins by examining the historical roots and some of the present-day effects of this view./nA key assertion of the Confucianization hypothesis is that Chinese law was never significantly influenced by any of the “non-Chinese” groups who governed the territory administered today by the People’s Republic of China. In fact, supporters of this idea claim, Chinese culture in general and law in particular was so attractive to these outside groups that they adopted it almost wholesale. A prime example offered as evidence of this picture of largely untroubled cultural homogeneity is the Northern Wei 北魏 (386-535), a dynasty founded by a formerly nomadic group which conquered and then ruled China for a century and a half. In a speech several years ago, Chinese President Xi Jinping 習近平 singled out the Northern Wei and its adoption of Chinese practices as proof of the unique power and worth of Chinese culture./nI challenge such claims by examining two major texts relating to Northern Wei law: the administrative and legal treatises in the History of Wei 魏書, a government-sponsored history written in the sixth century by Wei Shou 魏收 (506-572). Many of the most reductionist views of Chinese legal history draw on these texts, while many of the scholars focusing on the ethnic and cultural complexity of Northern Wei (contra the Confucianization hypothesis) have turned away from the History of Wei, leaving it primarily to those with the most polemical ends. I argue that these texts actually reflect a diversity of theory and practice far beyond what is generally recognized, and that the origins of important features of imperial Chinese law and administration can be found in the synthesis of approaches these treatises record. By focusing on that diversity, this dissertation hopes to revive interest in works that offer the potential to further complicate some of the simplistic but still-influential attitudes to both the Northern Wei and to Chinese history as a whole.

Advisor: Nylan, Michael
Learning and Decision-Making in Complex Environments

In this dissertation, we present several forays into the complexity that characterizes modern machine learning, with a focus on the interplay between learning processes, incentives, and high-dimensional models. We aim to uncover new principles that address the challenges that arise at the frontiers of this rapidly advancing field. This work is structured into two parts, each exploring a different facet of these complexities./nIn Part I, we examine complexity arising from strategic and adversarial environments. We present two studies. The first explores learning and decision-making in a matching market, where a platform hopes to learn a market equilibrium amidst uncertainty about user preferences. The second investigates the robustness of safety-trained large language models to adversarial "jailbreak" attacks. We identify and exploit failure modes of safety training and discuss the implications of these findings for language model safety going forward./nIn Part II, we study complexity arising from the high-dimensional models that are by now ubiquitous in machine learning. We start by investigating what mathematical foundations lead to an accurate predictive theory of high-dimensional generalization, and identify models based on random matrix theory as a promising candidate. We then delve further into the theoretical underpinnings of random matrix theory for high-dimensional linear regression to shed light on phenomena such as double descent, benign overfitting, and scaling laws.

Advisor: Haghtalab, Nika
Steinhardt, Jacob N
Switching Schemes for Hybrid Switched-Capacitor DC-DC Power Converters

Power converter topologies are continually evolving and improving especially as new applications such as space exploration mature, and new power architectures emerge. One such evolutionary branch of power converters is hybrid switched-capacitor (SC) converters which leverage capacitors as highly energy-dense components, high figure-of-merit switching devices, and small magnetics to realize high-performance power electronics. To fully exploit the benefits of this relatively new class of power converters requires investigation into control schemes. Whether using conventional switching control strategies or developing novel arrangements, this work explores the theoretical and practical intricacies of applying various switching schemes to hybrid SC converters./nDue to the reliance on capacitors for energy processing, SC converters typically suffer from charge-sharing losses when capacitors are charged/discharged into other capacitors or sources. However, adding a small inductance to the circuit and incorporating clever switching patterns can reduce or eliminate these losses. Furthermore, with an increase in the number of switching devices over traditional converters, losses associated with those switches may also increase. This work examines soft-switching techniques and how application to hybrid SC converters differs from previous works./nFinally, a hybrid SC converter is designed and tested for low-voltage automotive powertrain applications. In an industry that has strict regulations, this work seeks to demonstrate these new topologies can meet the required specifications and can do so with high performance as well. Switching techniques for mitigation of electromagnetic interference are evaluated against regulated limits and against efficiency performance./nTheory, analysis, and experimental results are expounded upon for several switching control strategies of hybrid SC converters addressing challenges of high-efficiency, low-noise, and high-density for applications to data center power delivery, space technology, and automotive powertrains.

Advisor: Pilawa-Podgurski, Robert C. N.
A Modular Design Flow for NoC-embedded FPGAs

With the increasing growth of complexity and heterogeneity of modern FPGA fabrics, the conventional digital design flow relying on the standard vendor tools, from synthesis to implementation, has become more arduous than ever. This leads to an inordinate turn-around time which severely impacts the productivity of application developers in their quest to explore the design space. We propose an open-source tool flow built upon three principles: modularity, composability, and reusability to address the FPGA tooling productivity issue. Our tool flow features the design and implementation of Spatially Distributed Ensemble of Sockets (SPADES). SPADES modularizes an application into a parallel system of socket engines interconnected by a Network-on-chip. Each socket contains a fixed and reusable component for control and communication in addition to a custom partition tailored to specific application computation and memory demands. SPADES also presents a customized backend tool that allows flexible and rapid composition of the socket physical implementations to generate a complete design. The results demonstrate that SPADES is 7.1x faster on average by reducing hours of compile time to minutes, while achieving comparable system-level performance when compared to a standard vendor tool for a set of benchmarks targeting a state-of-the-art FPGA architecture.

Advisor: Wawrzynek, John
Computational and Machine Learning Methods for Understanding Gene Regulation and Variant Effects

The field of genomics has been advancing at a fast pace ever since the development ofhigh-throughput sequencing technologies. While we have access to more data than ever before, the number of open questions has only increased. In this dissertation, I present novel machine learning techniques to draw insights from genomic data. First, I tackle the analysis of alternative splicing — a crucial but overlooked step in gene regulation — from short-read single-cell RNA-seq data. To account for the large scale and sparsity of such data, I develop scQuint, a suite of efficient probabilistic methods for dimensionality reduction and differential splicing. Next, I approach the problem of genome-wide variant effect prediction with a new direction: DNA language models. We first propose GPN, trained on unaligned genomes, and apply it to study genetic variants in Arabidopsis thaliana. GPN shows an improved power for highlighting variants under negative selection as well as those affecting traits. Furthermore, I show that GPN learns important genomic features such as gene annotations and transcription factor binding site motifs, without any supervision. We then present GPN-MSA, a DNA language model trained on whole-genome alignments of vertebrates, and showcase its excellent performance predicting deleteriousness across the entire human genome. These contributions not only pave the way for enhanced genomic understanding but also propose a methodological shift in genome analysis.

Advisor: Song, Yun S
The Concept of Saṃvega (Distress) in Early Buddhist Scripture

The Concept of Saṃvega (Distress) in Early Buddhist Scripture is a study dedicated to the conception of emotions in the earliest strata of Buddhist literature. At the heart of this study lies the concept of saṃvega—a Sanskrit and Pāli word that covers a wide range of emotional states, including distress, fear, melancholy, awe, revulsion, shock, agitation, and shame. The use of saṃvega in the early Buddhist canonical texts is rife with tensions. Saṃvega is depicted as a horrifying and dejecting experience, yet at the same time, it is unequivocally recognized as beneficial and even essential. The complexity of saṃvega is the product of articulating a distinctly Buddhist emotional state, an occurrence and disposition carefully attuned to the reality of impermanence and suffering disclosed by the Buddha’s teaching. This extensive inquiry of saṃvega unpacks the rich meaning of this philosophical concept and challenges some of the commonly held assumptions and beliefs about emotions in Buddhist thought. The textual sources presented in this dissertation reveal a largely neglected component of the early Buddhist vocabulary, imagination, and practice, providing the reader with materials to reconsider the role of emotions in early Buddhism.

Advisor: von Rospatt, Alexander
Sharf, Robert
Designing Customization Technologies That Support Teachers to Integrate Social Justice into Science Teaching

Over the last decade, the demands on secondary science teachers have shifted to encompass a new range of objectives. Science teachers need support in their efforts to be responsive as they navigate shifting demands in the teaching profession: new state standards, an ever-diversifying student body, and a drive to integrate social justice into science learning. In the US, for example, the K-12 population has immensely diversified, necessitating curriculum adaptations to support the diverse lived experiences and range of prior knowledge represented in their classrooms. Moreover, new curriculum standards such as the Next Generation Science Standards in the US, have required teachers to adapt curriculum materials to meet new performance expectations. Further, technology has become ubiquitous in classrooms, requiring teachers to learn how to meaningfully integrate new tools into their practice. My dissertation project explores how to support teachers as they navigate these emerging trends within the context of a Research Practice Partnership devoted to promoting coherent science learning in technology-enhanced learning environments./nThe studies presented in this dissertation are guided by the overarching research questions: How can we leverage technology to support teachers in their efforts to be more responsive to the needs and ideas of their students? Taken together, the three studies presented in this dissertation shed light on teachers’ trajectories for customizing curriculum to be more responsive, their trajectories for teaching science for social justice, and their trajectories for leveraging technology as a support for their teaching practice./nSpecifically, the first study leverages the Knowledge Integration Framework to design and study a professional development workshop to empower teachers to customize their curriculum to better support their students develop understanding of NGSS performance expectations. The workshop activities and supporting technologies foreground pedagogy and emphasize using student work as evidence to inform customization decisions. The second study applies the professional development model from the first study to support teachers to customize the curriculum to feature social justice issues. The study follows the development and implementation of the units with three teachers across two school years. The third study focuses on the design and implementation of automatic scoring models that increase teachers’ access to the social justice and science ideas students use to explain the focal phenomena in the units designed in the second study. Across the studies, the findings have implications for expanding our understanding of Knowledge Integration pedagogy by integrating social justice principles and for the design of technologies that support teachers in principled customization.

Advisor: Linn, Marcia C
Measurement of Muon Capture on Argon with a Pixelated Liquid Argon Time Projection Chamber

Studies of fundamental interactions have played a principle role in the development of our understanding of particle physics, but the \ac{SM} remains incomplete.The phenomenon of neutrino oscillation lies outside of the prescriptions of the \ac{SM}, driving current experimental efforts. The \acf{DUNE} is being built to provide a comprehensive study of neutrino oscillation and to search for \ac{BSM} physics. To accomplish this, \ac{DUNE} utilizes a massive-scale far detector, a sophisticated near detector, and a high-intensity, broad-spectrum neutrino beam. The near detector adopts a new design of \ac{LArTPC} with pixelated charge readout and a modular structure to resolve events in the high-intensity neutrino beam. A prototype of this detector design was successfully operated at the University of Bern, Switzerland in 2021. From the cosmic-ray data collected with this prototype, a study of muon capture on argon has been performed, resulting in the first measurement of the Huff factor for argon ($R_H = 1.29 \pm 0.15$) and measurements of the muon capture rate ($\lambda_c = 1.53\pm0.19~\mu$s$^{-1}$) and muon disappearance rate ($\lambda_d = 2.11\pm0.24~\mu$s$^{-1}$) in argon. These results can help to reduce the uncertainty in neutrino-nucleus cross-section modelling at low $Q^2$.

Advisor: Luk, Kam-Biu
Gaussian Entropy Inequalities

We give a broad overview of Gaussian entropy inequalities, discuss their scope, analyze their structure, and introduce novel ones. Our new inequalities highlight the connection between information theoretic and analytical inequalities. We then derive a Gaussian comparison inequality that unites a bulk of pre-existing Gaussian entropy inequalities. We present the equality conditions for the Anantharam-Jog-Nair inequalities, and thus derive equality conditions for a wide class of inequalities including the entropy power inequality, the Zamir-Feder inequality, and the Brascamp-Lieb inequalities. We conclude with a discussion of the extremizers of Forward-Reverse Brascamp-Lieb inequalities.

Advisor: Courtade, Thomas
Decentering the Hexagon: Towards a sociolinguistically informed French language curriculum

Resulting from France’s centuries-long colonial project across several continents, French is estimated to be the fifth most widely spoken language globally. Yet, despite its presence across continents and despite the linguistic diversity within France itself, notions of ‘good’ and ‘correct’ French continue to be associated with the image of the white, upper-class, metropolitan Français de souche. As a result, second-language French instruction often adopts and perpetuates this monolithic image of Frenchness and Francophonie, peripheralizing the richly diverse range of communities that use French in social life. This underrepresentation of global Frenches not only harms the development of sociolinguistic competence but may also have negative effects on language students’ sense of belonging. Drawing methodologically from applied linguists working in the Spanish language context, I adapt approaches to representing sociolinguistic variation in language teaching to the French context to address the sociolinguistic lacunae common in United States, university-level elementary French courses. This dissertation describes the development, implementation, and assessment of a series of lessons that foreground language variation in the Francophone context, integrated into UC Berkeley’s existing Elementary French curriculum. The learning outcomes observed in this implementation differ from those of previous studies in the Spanish context, notably with respect to students’ sociolinguistic knowledge and language attitudes. While quantitative findings suggest that UC Berkeley students enter the French language sequence with existing sociolinguistic awareness and relatively accepting language attitudes, we observe improvement in their ability to produce non-standard forms after receiving the curricular intervention. Furthermore, contrary to prior assumptions, we observe that exposure to sociolinguistic variation in French does not undermine learners’ proficiency in the prescriptive, pedagogical standard, but instead may even facilitate their acquisition of the so-called ‘standard’. Additionally, qualitative findings suggest that there is a clear, present interest among students in seeing more of the wider Francophone world represented in their learning experience. As such, I argue that our conversations as applied linguists need to move beyond debating whether sociolinguistic variation should be taught more explicitly in French language courses, toward how we can best do so for the benefit of our students.

Advisor: Kern, Richard
Davidson, Justin S
Machine Learning-Assisted Simulation and Design for Functional Nanomaterials

Often deemed as a "wonder material", graphene has exhibited remarkable promises in a broad range of research fields thanks to its exceptional electronic, thermal, and mechanical properties. However, issues such as the inevitable existence of defects and the complex microstructures of graphene-based materials stand as a bottleneck in realizing its full potential in real-life applications. With the fast growth of big data, machine learning has been widely applied in many fields such as finance, biology, and healthcare. The advent of machine learning approaches also offers solutions to learning patterns from complex data in material design and discovery, reducing the need for expensive, time-consuming, and tedious laboratory experiments or numerical simulations. In the present thesis, machine learning-assisted simulation and design approaches for functional nanomaterials are demonstrated, with a focus on the graphene family. Molecular dynamics simulations are conducted to numerically investigate the mechanical behavior of graphene-based materials such as graphene, graphene oxide and graphene aerogel, and various machine learning techniques including kernel ridge regression, Gaussian process metamodels, and deep reinforcement learning are used in the predictive and generative modeling of these materials. Finally, the concept and the promise of machine learning interatomic potentials in achieving efficient and accurate simulations for metal-organic framework materials are presented. The research constituting the present thesis may shed light on some new possibilities of simulating and designing functional nanomaterials, which may further improve the performances of applications such as stretchable electronics, supercapacitor devices, carbon sequestration technologies, among others.

Advisor: Gu, Grace
Migration Corridors – Governance at the Systemic Edge

The three papers that comprise this dissertation contribute key building blocks for my analysis of migration corridors as critical spatialities with the potential to significantly rework our approach to global migration governance—including in legal, political, and scholarly discourses. The study is a multi-site ethnographic account of migration corridors—circuits of human mobility within and across national borders that are governed by nation states as well as transnational financial, political, and social forces. It examines governance of migration corridors traversed by migrant agricultural, domestic, and garment workers in relationship to three building blocks: (1) expulsions that propel migration (e.g. national/global patterns of uneven development, environmental devastation, corporate land grabs, and conflict); (2) junctions where disparate migration flows converge and are redirected, including urban production and service hubs, special economic zones (SEZs), and territorial borders; and (3) forces that direct migration flows (e.g. legal regimes, product and labor supply chains, securitization, patriarchal norms, and local processes shaped by women labor migrants, recruitment intermediaries, and kinship and social networks).

Advisor: Morrill, Calvin