Ph.D. Presentations

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    Carrión, H. (CSE) – Deep Learning Algorithms for Medical Image Representation Learning and Understanding

    Virtual Event

    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 […]

  • Three silhouetted figures talking, overlaid with graphics of digital data, charts, and technology interfaces.

    Wang, Z. (CSE) – From Static Alignment to Adaptive Safety: Toward Reliable and Capable AI Systems

    Virtual Event

    Modern AI systems are rapidly moving beyond static text generation toward capable models and agents that reason, use tools, store memories, and update persistent state, yet safety methods still often assume a fixed model whose behavior can be controlled by output-level refusal. This leaves critical gaps in understanding why aligned models fail under adversarial pressure, […]

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    Burbano, L. (CS) – Security of autonomous decision-making agents: From control systems to embodied AI

    Engineering 2 Engineering 2 1156 High Street, Santa Cruz, CA
    Hybrid Event

    Due to their increasing complexity, autonomous decision-making agents rely on increasingly advanced algorithms, from classical control theory to reinforcement learning (RL) and, more recently, large vision-language models. While these algorithms help automate the decision-making in complex systems, they bring newer attack vulnerabilities that an adversary can exploit. In this dissertation, we study the security of […]

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    Carrión, H. (CSE) – Deep Learning Algorithms for Medical Image Representation Learning and Understanding

    Virtual Event

    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 […]

  • Three silhouetted figures talking, overlaid with graphics of digital data, charts, and technology interfaces.

    Levine, R. (CSE) – Validating GPU Memory Consistency and Safety at Scale

    Engineering 2 Engineering 2 1156 High Street, Santa Cruz, CA
    Hybrid Event

    Graphics Processing Units (GPUs) have become essential platforms for parallel computing, supporting applications far beyond graphics. Central to GPU programming models is its memory consistency specification (MCS), which defines the semantics of concurrent shared-memory operations and interacts with other language features to determine security guarantees such as memory safety. Understanding whether implementations conform to an […]

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    Scott, J. (CSE) – Mechanistic Specialization Does Not Guarantee Performance: Evidence from Dual AttentionTransformers

    Virtual Event

    Dual Attention Transformers (DATs) extend decoder-only Transformers with a dedicated relational-attention stream, making them a natural architecture for abstract identity rules such asABA and ABB. Surprisingly, we find that comparably sized GPT-2 models outperform DATs on these tasks. We investigate this gap with two complementary mechanistic analyses. First, causal mediation analysis shows that DATs exhibit […]

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    Kembay, A. (ECE) – Sparse and Continual Foundations for Adaptive General Intelligence

    Engineering 2 Engineering 2 1156 High Street, Santa Cruz, CA
    Hybrid Event

    While the human brain learns continually, mastering new tasks without forgetting the old and adapting to unfamiliar ones from context alone, modern neural networks still lack both. To bridge the gap between biological adaptivity and modern AI, we have established foundational work on sparsity as a computational principle at three levels of neural computation, through […]

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    Calicchio, A. (BMEB) – Comparison of long-read sequencing and analysis methods for transcriptome analysis

    Hybrid Event

    Alternative splicing, the process generating different RNA isoforms from a single gene, is considered one of the main factors driving increased organism complexity in eukaryotes. Variations in isoform and gene expression produce the functional differences that give rise to different cell types and, in some cases, result in disease. Long-read RNA sequencing has transformed our […]

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    Holmes, J. (CM) – Towards a Multi-dimensional Model of User Load

    Virtual Event

    Games user researchers (GURs) use various methods to understand when a game is overloading its players. In games research where data-driven multimodal approaches are necessary to drive insights, the currently available tools to measure user load are coarse, one-dimensional, and often aggregated. The more dominant instruments, such as the Cognitive Load Scale (CLS) and the […]

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    Chen, X. (STAT) – Changepoint Detection and Clustering Methods for Multivariate Time Series and Attributed Networks

    Virtual Event

    Time series data with dependence arise across a wide range of scientific and engineering disciplines, often presenting challenging inferential problems related to structural change and clustering. This Ph.D. proposal addresses several related problems in statistical inference for multivariate and network-indexed time series. First, we develop a weighted multivariate $U$-statistic procedure for detecting a single changepoint […]

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    Li, J. (CM) – Detecting Failure to Adapt: Reading Self-Regulated Learning Breakdowns from Game Telemetry through Plan Recognition

    Virtual Event

    Three learners who fail the same level of an educational game the same number of times can be failing in three different ways, and the difference determines what each should do next. Yet the measures a game’s logs are usually reduced to (completion time, error counts, mastery estimates) render the three identical. This proposal takes […]

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    Gholami, K. (ECE) – Efficient Language Model Construction and Inference via Sparsity

    Hybrid Event

    While large language models can match or exceed human performance, they do so with memory and energy costs orders of magnitude greater than biological cognition. We investigate sparsity as a brain-inspired computational principle to address both. We first establish a framework for evaluating small language model construction methods, using the next-token logit distribution as a […]

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    Fontana, J. (STAT) – When We’re Always Wrong: Scalable Variable Selection in M-Open Settings

    Hybrid Event

    A ubiquitous task in statistical practice is that of variable selection, identifying which of a large set of features are the relevant ones. As data sets with a large number of observations have become increasingly common, new theoretical and computational challenges for model selection have emerged. We consider the variable selection problem for linear models […]

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    Le, A. (STAT) – Bayesian Nonparametric Analysis of Densities for Replicated Point Patterns

    Virtual Event

    Many scientific applications produce repeated point pattern realizations across subjects, regions, or time. While such point patterns exhibit individual variation, we assume they arise from related point processes that share a common distributional structure. This dissertation develops a Bayesian nonparametric modeling framework built around an interpretable baseline. We work with Poisson processes, such that the […]

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    Zhao, Z. (CSE) – TOWARD VERIFIABLE REASONING IN LLMS

    Silicon Valley Campus 3175 Bowers Avenue, Santa Clara, CA, United States
    Hybrid Event

    Chain-of-thought (CoT) prompting can improve final-answer performance, but it does not guarantee that intermediate reasoning steps are faithful, valid, or checkable. This proposal studies how formal methods can make natural-language reasoning more reliable by translating CoT rationales into Lean artifacts, checking the resulting theorem statements and proofs, and using compiler feedback to diagnose and repair […]

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    Aliamooei Lakeh, S. (ECE) – Optimization and Decision-Support Frameworks for Resilient Power Systems Under Large-Scale Electrification

    Virtual Event

    The rapid electrification of transportation is creating new interdependencies between power and transportation systems, particularly during extreme events and disasters. As electric vehicle (EV) adoption increases, evacuation-related charging demand, infrastructure disruptions, and limited access to energy resources introduce challenges that conventional power system planning and operation frameworks were not designed to address. Wildfires provide a […]

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    Nikolakakis, M. (ECE) – Learned Gridless Representations of Cone Beam Computed Tomography Scans

    Hybrid Event

    Medical image representation has long been dominated by voxel-grid matrices. While their inherent structure and order work efficiently for various linear transformations and provide a seamless visualization method on monitors, they fail to preserve the topology of the scan and to encode sparse information in a memory-efficient way.   The recent emergence of machine learning-based continuous coordinate-based […]

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    Condon, C. (BMEB) – Genomic conflict across scales

    Biomedical Sciences Building 575 McLaughlin Drive

    Genomes are often viewed as cooperative systems in which genes work together to support organismal function. Yet genetic elements can also act in ways that favor their own transmission or persistence, creating conflict within the genome. In this talk, I examine the evolutionary and functional consequences of such genomic conflict across three systems. First, I […]

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    Gutie, J. (SciCAM) – SORh: Hyperbolic Relaxation Methods For Elliptic Problems In Computational Fluid Dynamics

    Virtual Event

    This thesis explores iterative methods for solving elliptic partial differential equations (PDEs), which are used in computational fluid dynamics (CFD) to model a wide range of physical phenomena. The primary application of interest here is self-gravity, modeled by Poisson’s equation. Although many numerical approaches exist, including direct matrix inversion, FFT-based methods, and classical iterative methods […]