UC Berkeley

Quantum locally recoverable codes (QLRCs) have recently gained attention as a framework for achieving efficient quantum storage with local recovery capabilities. Analogous to their classical counterparts, QLRCs allow a lost qudit to be reconstructed using only a small subset of other qudits, thereby reducing the resource and operational overhead in recovery. In this work, we extend the study of QLRCs by considering (r, δ) QLRCs characterized by locality parameter r and local distance δ ≥ 2. We present constructions of both random and explicit (r, δ) QLRCs, including explicit families based on the quantum Tamo—Barg construction. We also present an efficient decoding algorithm for these quantum Tamo–Barg codes.Furthermore, we introduce quantum hierarchical locally recoverable codes (QHLRCs), which extend local recovery to multiple hierarchical levels. For any integer h ≥ 2, we construct both random and explicit h-level QHLRCs—the latter being h-level quantum Tamo—Barg codes—and establish a Singleton-like bound for these codes using a CSS framework built from dual-containing classical codes. These results advance the theoretical foundations of quantum erasure recovery and contribute to the design of efficient quantum storage architectures.

One of the largest obstacles in modern drug discovery is that a significant portion (>90%) of the proteome is considered “undruggable,” in that these proteins lack a characterized, functional binding pocket or ligandable hotspot which small molecules can bind to and modulate the protein’s function for therapeutic benefit. To overcome such disease-causing proteins, targeted protein degradation (TPD) strategies have arisen, where the cell’s endogenous degradation machinery is hijacked to ubiquitinate and degrade the classically undruggable protein. Molecular glue degraders serve as a promising modality to achieve TPD. These are monovalent compounds that induce the proximity of a target protein with a component of the ubiquitin proteasome system to degrade the protein of interest. While our research group has previously identified a fumarate-based electrophilic handle that covalently modifies the E3 ligase RNF126 to enable degradation of multiple protein targets, the high intrinsic reactivity and cytotoxicity of the fumarate handle limited its translational utility. This work describes the development of an optimized and metabolically stabilized RNF126-targeting covalent handle incorporating a trans-cyclobutane linker that exhibits reduced glutathione reactivity and diminished cytotoxicity while retaining robust degradative activity. Using BRD4 as a benchmark target, we demonstrate that this optimized handle yields a potent and selective BRD4 degrader whose activity is dependent on RNF126. We further extend this strategy to the androgen receptor (AR) and its clinically intractable splice variant AR-V7, demonstrating selective degradation of both AR and AR-V7 in androgen-independent prostate cancer cells and robust inhibition of AR transcriptional activity that surpasses the established AR antagonist enzalutamide. Together, this dissertation establishes a generalizable, chemistry-centric framework for converting small-molecule ligands into covalent molecular glue degraders, offering a roadmap for exploiting event-driven pharmacology against the most intractable targets in the human proteome.

The works investigate reliability and performance enhancement for ultra-scaled advanced CMOS and memory devices through experimental characterization, physical modeling, and device simulation. The study first examines random telegraph noise (RTN) in advanced transistors under cryogenic operation, revealing the strong dependence of carrier trapping dynamics on temperature and bias conditions. A physics-based RTN modeling framework, combined with experimental results, is developed to predict RTN behavior in nanoscale transistors. In addition, the impact of ultra-thin aluminum incorporation within high-k metal gate stacks (HfO₂/TiN) is investigated. Electrical characterization and material analyses reveal that aluminum modifies the effective work function through oxygen scavenging from the TiON layer. The reliability trade-offs associated with Al incorporation in the metal gate are further clarified. Next, to mitigate the side effects of Al-incorporated gates, an oxygen insertion (OI) technology is proposed as an alternative approach for controlling the flat-band voltage. Lastly, a silicon-germanium/silicon (SiGe/Si) heterojunction drain transistor is proposed for 3D NAND flash memory to enhance gate-induced drain leakage (GIDL) and improve erase speed through increased band-to-band tunneling. Simulation results demonstrate significant improvement in erase performance without degrading inhibit-mode operation.

This dissertation studies Large Language Model (LLM) inference through the lens of statistical decision-making theory, with the goal of advancing prevailing inference paradigms along two principal axes: scalability and provenance. In the domain of scalability, this dissertation investigates the efficiency of inference-time scaling paradigms and parallel decoding schemes. We formally characterize the sample complexity of self-consistency and best-of-n sampling methodologies, and demonstrate how self-correction can substantially expand the expressive power of Transformer architectures in multi-task settings. We analyze the information-theoretic bottlenecks associated with parallel sampling in diffusion language models, and introduce Explore-Then-Exploit, a scalable decoding strategy that improves inference throughput while preserving generation quality. In the domain of provenance, the dissertation develops theoretical foundations and practical schemes for statistical watermarking. We mathematically formalize statistical watermarking as a hypothesis-testing problem, providing a comprehensive characterization of the optimal Type II error, token efficiency, and robustness against perturbations. Guided by the theoretical insights, we propose SEAL, a semantic-aware watermarking scheme demonstrating strong detection efficiency and tamper resistance. We further generalize the statistical watermarking framework to support anytime-valid detection and derive the optimal e-value-based detection scheme. Collectively, our work connects theoretical insights with algorithmic innovations for understanding and improving the efficiency and responsible use of LLM inference.

The Death of the Asylum: How Federal Policy, Popular Culture, and Drugs Killed the American Mental Hospital (1940-1946) is a historical study of three interwoven elements that led to the end of asylums or psychiatric institutions in mid-20th century America. The dissertation is an interdisciplinary project that brings together methodologies from the history of architecture, psychiatry, and law to tell the story of the extinction of an American institution and building type. The work argues that 1946 was a pivotal year for the beginning of “deinstitutionalization,” defined as the phasing out of institutional treatment of mental illness in asylums established in the late 19th-century and towards an outpatient medical model. This shift fundamentally altered the profession and practice of psychiatry.The first chapter explores the legacy of journalistic exposés and literature starting in the late-19th century by writers who were hospitalized for feigned or real mental illness. These writers, such as Nellie Bly and Mary Jane Ward, used their experience in the asylum to write books and articles that chronicled their often-shocking journeys and critiqued the treatment they received. The chapter begins with a historical overview of the original ideals and humanitarian intentions of the 19th-century asylum. A new architectural typology in the late 1800s, this building type was rooted in the healing power of nature and the built environment. This historical and architectural exposition provides a stark contrast to what became of asylums by the mid-20th century, which the conscientious objectors of Chapter 2 sought to change. The chapter then primarily analyzes Ward’s popular novel The Snake Pit (1946) and its Hollywood film adaptation (1948), and Ward’s courageous but costly involvement in mental health reform. The second chapter begins in 1940 with the Selective Training and Service Act to tell the story of the four conscientious objectors who created the Mental Hygiene Program of the Civilian Public Service during World War II. I argue that these leaders—Leonard Edelstein, Hal Barton, Phil Steer, and Will Hetzel—changed the trajectory of mental health treatment in the United States through their activism. After being assigned to work in understaffed asylums as part of their alternative service because of their pacifist beliefs that protested the war, these conscientious objectors led a movement to document widespread abuses among hundreds of mental hospitals, analyze existing mental health commitment laws, and educate and retrain future employees of mental hospitals.The final chapter follows the career of journalist and wartime correspondent Albert Q. Maisel, who worked with the conscientious objectors in Chapter 2 who had formed the National Mental Health Foundation at the conclusion of World War II. In partnership with the conscientious objectors, Maisel procured eye-witness testimonies and haunting photographs to write a scathing and damning exposé article titled “Bedlam 1946,” which was published in Life Magazine on May 6, 1946. The chapter concludes with the National Mental Health Act (NMHA), which was signed into law by President Harry Truman on July 3, 1946. The Act funded the creation of the National Institute of Mental Illness (NIMH), catalyzed the development of psychopharmacological treatment, and was the final nail in the coffin for the American asylum. Through archival research, this dissertation demonstrates how, in 1946, the dream of the curative mental asylum was over.

Artificial intelligence (AI) is increasingly used by individuals, firms, and organizations around the world, offering an opportunity to expand capabilities and improve livelihoods. Yet adopting and adapting to any new technology is difficult, and these challenges are compounded when the technology is as complex as AI. This dissertation examines how AI could shape strategic decision processes, with a focus on understanding the implications of AI system capabilities for low-resource contexts. Underlying the dissertation is the idea that if we want AI systems to have a positive impact, we must begin by understanding model performance. My first two essays explore this within the context of AI-generated advice: if we can understand where generative AI provides high-quality advice, then we can identify cases where AI should be deployed and cases where additional input may be needed in order to be beneficial to the user. I start by exploring this within the context of a field study in Kenya which varies the extent to which individuals receive advice from an AI system as opposed to traditional human advice. Results from this study suggest that AI-generated advice and expertise may reinforce each other, while highlighting the risks that can emerge if individuals begin to use AI at the expense of vetting the advice they receive by engaging with external sources of information. I then explore the extent to which the recommendations produced by AI systems may inadvertently privilege some contexts over others. Specifically, I test whether the default response from AI systems provides higher quality advice to individuals living in high- versus low-resource contexts. Results from this study provide evidence that default responses from AI are more aligned with high-resource contexts, which I document both within and across countries. However, I find that much of the difference in the quality of responses provided by AI stems not from fundamental differences in the model's underlying capabilities, but instead from the default context assumed by the model. Finally, I examine the performance of AI in the context of a specific use case, evaluation. I develop a test of whether AI systems prefer proposals that were written by AI through a novel design which holds proposal quality constant, varying only the extent to which language inordinately used by AI models is included in the proposal. I provide evidence of self-preference in model evaluation, and show that these distortions are substantial enough to change the ranking of proposals, while also highlighting that because of the low cost of using AI for evaluation, it can still be beneficial to use AI systems, even given these errors, if the alternative evaluation strategies are sufficiently costly. Together, these essays highlight the potential of generative AI systems to improve people's livelihoods, while also cautioning that understanding the capabilities of these systems is fundamental to using them effectively. By better understanding AI system capabilities, we can identify when more contextual information or guidance is needed, and when AI advice, while imperfect, may still be efficient due to its lower cost. As the capabilities of AI systems continue to rapidly improve, ensuring that these tools are deployed with the right enabling resources will be an important part of realizing their potential for people around the world.

The holographic principle posits that the physics within a region of spacetime can be encoded in degrees of freedom living on its geometric boundary. This principle is most robustly exemplified by the AdS/CFT correspondence, which provides a dictionary between quantum gravitational degrees of freedom in Anti-de Sitter (AdS) spacetime and those of the conformal field theory (CFT) at its asymptotic boundary. Beyond AdS/CFT, the precise structure of holographic encoding is not fully understood. This dissertation, divided into two parts, addresses various aspects of holography beyond AdS/CFT.In Part I, we study how degrees of freedom outside causal horizons holographically encode the spacetime beyond them. We examine the near-horizon mode structure of Schwarzschild and de Sitter horizons and argue that, from the perspective of semiclassical observers whose ability to resolve fine-grained quantum information is inherently limited, the physics beyond the horizon emerges from near-horizon dynamics. We discuss the relationship between this framework and the standard global framework used in AdS/CFT, emphasizing the complementary mechanisms through which they incorporate unitarity and the equivalence principle.In Part II, we investigate the relationship between the geometry of generic spacetime subregions and their quantum information content, as captured by entanglement entropy and computational complexity. We first use the relationship between random tensor networks and fixed-geometry states to motivate a gravitational path integral derivation of the quantum extremal surface prescription for bulk subregions of AdS. In particular, we show that the von Neumann entropy of a bulk subregion is equal to the generalized entropy of the bulk entanglement wedge containing it. We then propose a notion of generalized complexity for bulk subregions as the sum of their volume and the complexity of the semiclassical bulk fields they support. We demonstrate that if the semiclassical complexity is quantified by a codimension-0 measure which satisfies strong subadditivity, then time-like focusing arguments imply a quantum generalization of the strong energy condition, as well an upper bound on the semiclassical complexity of a slice by its volume.

Modern deep learning has succeeded in regimes where data is abundant and high-quality, the training and deployment distributions match, and the analyst keeps close control of the modeling pipeline. Real-world deployments rarely satisfy all of these conditions. This dissertation studies three such hard regimes for deep learning, distribution shift, agentic data science, and data scarcity, through the lens of the Predictability--Computability--Stability (PCS) framework for veridical data science. The first project addresses distribution shift in the simplest tractable setting. We prove the first non-asymptotic excess risk bounds for benignly-overfit minimum-norm linear interpolators evaluated on a target distribution that differs from the source. From these bounds we propose a taxonomy of beneficial and malignant covariate shifts parameterized by the degree of overparameterization, and corroborate the taxonomy empirically on real image data and on fully-connected neural networks. The second project tackles agentic data science, where a large language model executes the entire analytical workflow and the analyst can no longer audit the intermediate decisions. We propose a pair of lightweight sanity checks that apply targeted perturbations to the data and the analytic prompt and ask whether the agent's conclusion survives. The checks are validated on synthetic data with controlled signal-to-noise ratios and applied to eleven real-world datasets, identifying six on which an affirmative agentic conclusion is not well-supported. The third project develops a deep learning pipeline for pediatric focused assessment with sonography for trauma (FAST), a bedside ultrasound exam used to detect hemorrhage from intra-abdominal injury (H-IAI) in injured children. The pipeline first localizes Morison's pouch and then classifies frames for hemorrhage. We evaluate on a pediatric cohort of 207 exams, considerably larger than previous pediatric FAST datasets but containing only 17 H-IAI positive cases. We use Monte Carlo cross-validation to report metrics stable under this small positive count.

Many industrial processes produce hot multicomponent mixtures that require extensive separations. The work presented in this dissertation describes the design, synthesis and characterization of metal–organic frameworks for industrially relevant separations conducted at temperatures above 200 °C. The modularity of metal–organic frameworks enables the rational design of metal sites for reversible binding of adsorbates at these temperatures informed by molecular principles. Chapter 1 introduces metal–organic frameworks as tunable adsorbents and motivates their development for gas separations conducted at elevated temperatures. High-temperature post-combustion carbon capture is introduced as an exemplar application and a brief survey of carbon dioxide-selective frameworks is presented to inform design principles for this application. Next, high-temperature separations of commodity chemicals including carbon monoxide (CO), ethylene (C2H4) and propylene (C3H6) are proposed and motivated, followed by a brief survey of frameworks employing metal-centered π-backbonding interactions to conduct separations of olefins and CO. Chapter 2 describes the synthesis and characterization of a porous metal–organic framework featuring terminal zinc hydride sites for high-temperature post-combustion carbon dioxide (CO2) capture. The framework reversibly captures CO2 at temperatures above 200 °C, conditions unprecedented for gas adsorption to a porous material. Gas adsorption and spectroscopic analysis reveal that CO2 reversibly inserts into the terminal zinc hydride sites, and that this mechanism is kinetically limited at ambient temperatures. Finally, extended cycling and breakthrough experiments confirm the robustness of the material and its potential to conduct deep capture under low CO2 concentrations and at high temperatures. Chapter 3 describes the synthesis and characterization of a metal–organic framework featuring open trigonal pyramidal copper(I) sites for high-temperature purification of π-acidic commodity chemicals. Gas adsorption, diffraction, spectroscopy, and density functional theory calculations reveal that the copper(I) sites activate adsorbates through metal–ligand π-backbonding interactions. Breakthrough experiments at elevated temperatures demonstrate that separation of these π-acids from one another and from other components is feasible, favoring CO over C2H4 and C2H4 over C3H6, and separation of CO is accomplished under humid conditions.

Healthcare analytics increasingly supports communication audits, individualized treatment, payment reform, and real-time quality and safety monitoring. Across these settings, methods must be interpretable so stakeholders can understand and act on model outputs, and robust so conclusions remain reliable under heterogeneity, dependence, confounding, and strategic behavior. This dissertation develops a coherent set of statistical learning tools built around a common principle: introducing explicit structure that turns complex data into transparent, auditable decision objects and supports valid inference and monitoring in real-world healthcare settings. The dissertation is arranged to mirror a lifecycle for trustworthy deployment. First, I develop an aspect-based NLP measurement framework for pediatric progress notes that quantifies family-centered communication and enables evaluation of note-sharing policies such as OpenNotes, with inference procedures designed for dependent comparisons. Second, I propose an interpretable semiparametric model for personalized rules with continuous treatments and binary outcomes. The method separates patient-specific risk from treatment-effect heterogeneity into two clinically legible scores, linked by a flexible nonparametric component that yields visualizable dose-response surfaces and actionable individualized recommendations. Third, I study end-of-life payment design through a principal–agent formulation and derive incentive-compatible mixed contracts combining fee-for-service and pay-for-performance, with transparent parameters that clarify how optimal reimbursement depends on patient heterogeneity and provider risk preferences. Finally, I develop robust monitoring and detection methods for deployed dynamical systems under drift or adversarial manipulation by combining physically consistent observer design with dependence-aware statistical testing, providing a general approach to trustworthy detection when standard independence assumptions fail. Together, these contributions unify measurement, individualized decision-making, incentive design, and monitoring within a single methodological agenda for interpretable and robust learning in complex healthcare systems.