Ph.D. Presentations

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  • Abstract digital illustration featuring gears and interconnected technology elements.

    Nava, A. (AM) – Machine-Learning Methods for Prediction of Biological Systems

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

    Advances in microscopy have enabled the collection of high-quality single-cell datasets, providing new opportunities to identify the mechanisms underlying complex biological processes. In this work, we develop machine-learning frameworks using single-cell temporal data with the goal of predicting and providing insights into these mechanisms. We produce frameworks for two biological systems, bacterial spore germination, the […]

  • Close-up abstract image of a circuit board with glowing lines and interconnected pathways.

    Huang, X. (CSE) – Scalable and Verifiable Reasoning for Medical Foundation Models

    Virtual Event

    This PhD research focuses on developing reliable medical foundation models capable of reasoning across textual, visual, and interactive clinical information. The work investigates three complementary directions: improving medical reasoning through test-time scaling, training multimodal medical models with verifiable rewards, and synthesizing high-quality visual question-answering data from biomedical literature using generator-verifier frameworks. Building on these efforts, […]

  • Close-up abstract image of a circuit board with glowing lines and interconnected pathways.

    Pawar, M. (CSE) – Understanding Representations, Reasoning, and Decision-Making in Autonomous Driving Models

    Virtual Event

    Modern autonomous-driving models increasingly rely on learned representations and generated reasoning to interpret complex scenes and produce predictions or actions. However, it remains unclear what information these models encode, how that information is exposed through common interpretation methods, and whether their stated reasoning meaningfully influences their behavior. This research investigates these questions across motion-forecasting and […]

  • Close-up abstract image of a circuit board with glowing lines and interconnected pathways.

    Kramer, A. (BMEB) – Scalable phylo-pangenomics

    Biomedical Sciences Building 575 McLaughlin Drive
    Hybrid Event

    The COVID-19 pandemic generated genomic data at unprecedented scale, with tens of millions of SARS-CoV-2 genomes deposited in public repositories and thousands of new sequences added each day. This dissertation develops methods for analyzing genomic datasets at this scale, unified by the idea that encoding genomes according to their evolutionary relationships can make otherwise intractable […]

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

    Saleem, O. (ECE) – Coupled Evacuation Readiness and Post-Disaster Restoration for Vehicle-to-Grid Enabled Resilient Power–Transportation Networks

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

    The accelerating adoption of zero-emission vehicles (ZEVs) in California is reshaping both the transportation and electrical grids at the moment as climate-driven disasters are intensifying in frequency and severity. This dual transition exposes a critical structural gap: existing resilience research treats pre-disaster evacuation readiness and post-disaster grid restoration as separate problems, even though both are […]

  • Close-up abstract image of a circuit board with glowing lines and interconnected pathways.

    Nag, S. (BMEB) – Personalized Diploid Genome Graphs for Accurate Somatic Variant Discovery

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

    Many somatic variant-calling pipelines begin by aligning tumor and matched-normal sequencing reads to a single linear reference genome, such as GRCh38. Because every individual differs substantially from this reference, this approach can introduce reference bias, causing reads to map incorrectly or not at all and potentially leading to missed somatic variants or germline variants being […]

  • Close-up abstract image of a circuit board with glowing lines and interconnected pathways.

    Shen, J. (STAT) – Bayesian Modeling and Uncertainty Quantification for Verbal Autopsy Data

    Virtual Event

    Verbal autopsy (VA) is a well developed tool to collect information describing deaths outside of hospitals by conducting surveys to the relatives and caregivers of the deceased person. It is routinely-implemented in low and middle income countries, where it often lacks sufficient resources to conduct the autopsy. The main task is to estimate both individual […]

  • Abstract digital illustration featuring gears and interconnected technology elements.

    Chen, Y. (STAT) – Flexible Bayesian Models for High-Dimensional and Longitudinal Discrete Data in Microbiome Studies

    Virtual Event

    Multivariate dependent discrete data routinely arise in microbiome studies. Analyzing these data presents interesting statistical challenges, such as high dimensionality, excess zeros, large heterogeneity across samples, and temporal dependence in longitudinal studies. Drawing inferences about objects of primary scientific interest—such as temporal trajectories of microbial abundance, microbial interactions, clusters of microbes similarly associated with environmental […]