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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 […]

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    Lupin-Jimenez, L. (AM) – Data-Driven Deep Learning for Turbulent Phenomena: Regional Ocean Prediction and Assimilation, Spectral Bias in Diffusion Models, and Equation Discovery

    Hybrid Event

    Deep learning models trained on simulation and reanalysis data can now emulate turbulent geophysical flows at a small fraction of the computational cost of numerical solvers. Their scientific utility depends on physical consistency, which for the systems studied here rests in large part on spectral fidelity, the accurate reconstruction of variance across spatial scales. This […]

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    Penunuri, G. (BMEB) – Genomic, Proteomic, and Computational Approaches to the Study of Host-Microbe Systems

    Hybrid Event

    Host-microbe systems are core to some of biology’s most consequential interactions, from the pathogens that drive infectious disease to symbionts affecting agricultural pest control and vector-borne disease transmission. Yet unlike the model organisms that have driven most of modern molecular biology, the microbes at the center of these interactions are rarely genetically tractable: many cannot […]

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

    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 […]

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    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, […]

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    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 […]

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    Gomez, J. (CSE) – Toward Sustainable and Secure Open Source Software: Discovery, Measurement, and Defense

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

    In March 2024, a backdoor was discovered in xz Utils, a widely used open source data compression library present in nearly every major Linux distribution. The attack was discovered days before merging into major distributions, and if this had happened, it would have allowed attackers to execute arbitrary code on millions of systems worldwide via […]

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    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 […]

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    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 […]

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    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 […]

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    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 […]

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    Zhang, X. (STAT) – Predictive Generalized Variational Inference for Spatial Gaussian Process Model

    Virtual Event

    Spatially dependent data are common in environmental and climate studies, where Gaussian process models are used for prediction and uncertainty quantification. Learning their covariance structure involves both statistical and computational challenges. Likelihood-based inference may be sensitive to covariance misspecification, while repeated evaluation of prediction-oriented criteria can be computationally demanding. To study these issues, we develop […]

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    Devarajan, K. (ECE) – Towards Multimodal Detection of Neuronal Signaling using Graphene Heterostructures

    Understanding neuronal function requires measurements that capture both the rapid dynamics and spatial organization of neural activity. Conventional neural interfaces often prioritize either high temporal resolution through electrophysiological recording or high spatial resolution through optical imaging, while integrating these modalities within a single interface remains challenging. This work seeks to develop a transparent, flexible, and […]

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    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 […]

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    Abdulaziz, A. (ECE) – Learning-Based Channel Estimation for Next-Generation Wireless Communications

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

    Coherent wireless reception depends on accurate channel state information (CSI), yet acquiring CSI through pilot transmission consumes time–frequency resources that could otherwise carry data. This tradeoff becomes more pronounced in time-varying and frequency-selective channels and in short-packet communications, where accurate channel estimation is challenging and pilot overhead can be significant. This dissertation investigates how CSI […]