Autonomous systems, such as Autonomous Vehicles (AVs) and drones, are increasingly deployed across a wider array of contexts for both civilian and military use. As these systems become more common, they may be targeted by malicious actors seeking to exploit and abuse them, compromising safety-critical operations. Among the ways to protect these systems simulation based […]
A central goal on the path toward general AI is to build systems capable of deliberative reasoning before action. Such systems should inspect what they know, identify what they need, seek or construct useful information, and revise their reasoning through intermediate cognitive states. This dissertation studies this goal through the lens of Inner Monologue (IM), […]
This dissertation investigates how to make modern AI systems safer as they grow more capable. It addresses two central sources of risk: malicious misuse, in which adversarial users coerce models into harmful behavior, and internal misalignment, in which models themselves pursue goals that diverge from human intent through deception, sandbagging, or other covert behaviors. The […]
Electrochemical impedance spectroscopy (EIS) is the workhorse measurement behind lithium-ion battery diagnostics, biosensing, and corrosion science — yet no integrated circuit has ever delivered the complete capability of a benchtop analyzer on a single die. This dissertation presents ISoC, the first universal Impedance Spectroscopy instrument-on-chip. Designed in SkyWater 130 nm CMOS process, ISoC supports all […]
This dissertation investigates how neuromorphic and brain-inspired principles can guide the design of efficient neural language architectures. It addresses two central limitations of modern Transformer-based language models: memory growth with context length and high computational cost from dense matrix multiplication. Through studies of spiking neural networks, linear-recurrent language models, hybrid attention architectures, MatMul-free models, and […]
Recently, a class of non-invasive hardware side-channel attacks has been discovered in field-programmable gate arrays (FPGAs). These attacks extract remnants of prior users’ activity that persist as transistor defect states within reconfigurable routing resources. These remnants are known as FPGA Pentimenti. Resistive random-access memory (ReRAM) is a compelling candidate for pentimenti-like attacks beyond FPGAs. However, […]
Ion transfer as the movement of charged species across spaces and interfaces is the basis of signaling in nearly all biological systems. My research is grounded in the idea that precise control over ion transfer enables direct manipulation of biological function. Specifically, I focus on how ion transport can be engineered to regulate both collective […]
The electric grid is essential to modern society, and recent developments such as renewable energy sources (RESs), battery energy storage systems (ESSs), and microgrids (MGs) have necessitated novel computational methods for planning and operations. Machine learning offers a promising lever here, both as an accelerator for and proxy to traditional optimization-based problems. In this thesis, […]
Intracellular symbionts are microorganisms, such as bacteria, that live within host cells. These associations are widespread throughout the invertebrate tree of life, and can perform a diversity of key metabolic, immune-response, or other functions that the host is dependent on for survival or reproduction. Intracellular symbioses allow both the host and the symbiont to occupy […]
This event celebrates and highlights the work of UCSC graduate students in all academic divisions. Enrolled graduate students will present either a poster, talk, or mixed media presentation. Judges will select and award a top prize for each academic division. This event is free and open to the public. Location : Science Hill Research talks […]
Cooperative two-player play produces distinctive social experiences between players: intimacy, trust, cooperation, communitas. Since Huizinga, the frame within which these experiences arise has been called the Magic Circle: a temporarily-set-apart space through which play does its social work. It has been a central organizing concept across game studies, performance theory, and HCI because it points […]
As quantum computing transitions from theory to practice, understanding which algorithms suit near-term devices becomes critical. Current quantum computers are severely constrained by limited qubit counts, short coherence times, and high error rates that quickly degrade computation into noise. This thesis addresses two interconnected questions: what non-trivial computational tasks can near-term devices execute and how […]
Many genetics pipelines start by aligning sequencing reads to a reference genome. Aligners attempt to find the position in the reference sequence which best matches the read sequence, but this breaks down when the reads come from a sample with variation relative to the reference. A proposed alternative, pangenome graphs, is supposed to fix such […]
Self-reflection holds significant potential for learning, behavior change, and emotional processing, yet designing technologies that effectively support it remains challenging, particularly when reflection involves difficult experiences such as failure. Most current technologies avoid negative experiences altogether, leaving users without support at precisely the moments when reflection could be most valuable. This dissertation investigates how technology […]
The growing complexity of high-performance computing (HPC) systems poses a fundamental challenge for domain scientists, whose primary objective is to obtain scientifically valid results rather than to optimize resource utilization. Modern leadership-class facilities combine heterogeneous CPUs, GPUs, and specialized accelerators across systems that simultaneously support traditional scientific simulations and AI-driven workloads. This creates a vast, […]
Modern high-performance GPU kernels increasingly rely on subgroup-level execution, including subgroup-level communication, subgroup operations, and matrix operations. These features are essential for workloads such as matrix multiplication and FlashAttention, but their language-level guarantees remain difficult to reason about. Existing programming models often leave unclear which threads participate in subgroup operations, when subgroup threads are required […]
Modern software increasingly relies on distributed systems to provide accessible, scalable, and reliable services. Choreographic programming brings a global perspective to distributed system development: programmers write a single program that describes the behavior of a whole system, and a compiler projects that global description into local programs run by each node. By making distributed control […]
Edge computing and distributed AI systems increasingly operate under heterogeneous resources, dynamic workloads, and frequent failures, requiring both adaptivity and fault tolerance for efficient execution. In heterogeneous edge clusters, nodes differ significantly in CPU throughput, memory capacity, and network bandwidth, while modern distributed GPU clusters supporting agentic LLM inference must recover large amounts of runtime […]
AI-assisted clinical decisions in medicine, and particularly in dermatology, demand fine-grained understanding across diverse skin tones, body sites, and disease types, yet expert-annotated datasets are scarce, demographically imbalanced, and almost devoid of rare presentations. This dissertation develops four deep learning systems for this low-label, low-coverage regime. We introduce HealNet, which learns wound healing stages from […]