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DTSTART;TZID=America/Los_Angeles:20260803T160000
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DTSTAMP:20260724T212920Z
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SUMMARY:Le\, A. (STAT) -  Bayesian Nonparametric Analysis of Densities for Replicated Point Patterns
DESCRIPTION: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 point process stochastic mechanism is characterized by the total intensity and a density with compact support. Flexible\, parsimonious weighted combinations of beta densities represent both the baseline and the replicate-specific densities. The weights corresponding to each replicate encode the features that characterize its density and are pooled hierarchically across replicates to estimate the shared baseline. Throughout\, we illustrate the framework using bike-share demand at a Chicago Divvy station\, where weekly demand shares a common daily pattern while its features evolve across weeks. \nWe lay the foundation with a conditionally independent model\, relating the replicates through a Dirichlet process prior centered on the shared baseline. The model admits a Pólya urn representation that yields fully conjugate posterior updates and partially parallel computation across replicates. Inference proceeds at both the shared and replicate-specific levels\, and predictive inference on the density of demand for a new week follows readily through the baseline. \nMoving to a dynamic extension\, we use the representation of the replicate-specific discrete random distributions to express structured dependence separately through the atoms and the weights. Stochastic processes on these components relate the locations of density features and their relative importance. The weights follow a geometric construction that keeps the model parsimonious. For the atoms\, we develop a novel stochastic process with the baseline as its marginal distribution\, so its interpretation is unchanged under temporal dependence. Across the Divvy weeks\, the contributions of these two forms of dependence are visible in the forecast uncertainty\, and both forecast the weekly densities more precisely than the conditionally independent model. \nFinally\, we model the full intensity of the underlying nonhomogeneous Poisson process by exploiting its factorization into a total intensity and a density. The factorization keeps the likelihood tractable and separates the volume of demand from its shape across the day. The dynamic density model carries directly over\, while the total intensity follows a stationary autoregressive process centered hierarchically on a baseline total intensity. This gives a baseline intensity\, extending the idea from densities to intensities. In the Divvy application\, the model yields smoother intensity estimates and sharper forecasts of the weekly intensities. \nThe common thread throughout the framework is the preservation of the baseline\, which retains the same interpretation as dependence and intensity modeling are introduced. Together\, the models give a unified characterization of the shared\, replicate-specific\, and dynamic structure of replicated point patterns. \nEvent Host: Andrew Le\, Ph.D. Candidate\, Statistical Science \nAdvisor: Athanasios Kottas \nZoom: https://ucsc.zoom.us/j/94954212320?pwd=lmcDG6LvDQTb73BOE2NqabU9M2Uzms.1 \nPasscode: 772566 \n 
URL:https://events.ucsc.edu/event/le-a-stat-bayesian-nonparametric-analysis-of-densities-for-replicated-point-patterns/
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260810T103000
DTEND;TZID=America/Los_Angeles:20260810T123000
DTSTAMP:20260721T182603Z
CREATED:20260721T182603Z
LAST-MODIFIED:20260721T182603Z
UID:10015110-1786357800-1786365000@events.ucsc.edu
SUMMARY:Zhao\, Z. (CSE) - TOWARD VERIFIABLE REASONING IN LLMS
DESCRIPTION: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 failures. The completed work evaluates direct zero-shot and few-shot auto-formalization pipelines for quantity- and logic-focused reasoning problems\, measuring proof type-check rate\, theorem-statement validity\, assumption faithfulness\, and repair behavior. The ongoing work extends this pipeline with AMR-guided semantic representations and altered-rationale stress tests. The planned work proposes methods of modeling LLM Agent thinking in Lean. Together\, these components separate two questions that are often conflated: whether a model translated the reasoning into the right formal goal\, and whether that goal can be proved once translated. The research goal is to develop an evaluation framework and a tool-supported workflow to improve the reliability\, auditability\, and semantic faithfulness of LLM reasoning. \nEvent Host: Zekun Zhao\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Jeffrey Flanigan \nZoom: https://ucsc.zoom.us/j/96068207641?pwd=anTpLhBXhIdaIhXKDTB32HlDMA6uIO.1 \nPasscode: 656037
URL:https://events.ucsc.edu/event/zhao-z-cse-toward-verifiable-reasoning-in-llms/
LOCATION:Silicon Valley Campus\, 3175 Bowers Avenue\, Santa Clara\, CA\, 95054\, United States
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260814T083000
DTEND;TZID=America/Los_Angeles:20260814T103000
DTSTAMP:20260811T162729Z
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SUMMARY:Krishnaswamy\, L. (CSE) - Network Load Balancing for Geographically Distributed Datacenters
DESCRIPTION:As datacenters scale up and become more geographically distributed\, wide-area network inter-datacenter traffic\, which typically consists of data-heavy tasks\, has become increasingly prevalent. Some of the noteworthy challenges raised by the coexistence and interaction between inter- and intra-datacenter traffic are the differences in their QoS requirements\, link utilization\, and round-trip times. To the best of our knowledge\, these challenges have not yet been addressed by current datacenter load balancers. To highlight this gap\, we conducted a comparative performance study of state-of-the-art datacenter load balancers. Through extensive simulations\, we study how they perform under different network topologies and workloads\, including intra-datacenter\, inter-datacenter\, and mixed intra- and inter-datacenter workloads that reflect how datacenters have evolved to keep up with their continuously changing driving application landscape. Our study shows that current load balancers are not able to adequately distribute load under inter-DC workloads as well as mixed intra- and inter-datacenter traffic coexistence.\nMotivated by our observations\, we introduce Balancia\, a transport agnostic\, lightweight network load balancer that dynamically switches between per-flow and per-packet control in order to provide adequate performance for both intra- and inter-DC traffic given their different characteristics and quality-of-service (QoS) requirements. We show that\, when compared against state-of-the-art load balancers\, Balancia achieves close to 80% reduction in the 99% tail flow completion times for inter-datacenter traffic in the presence of intra- and inter-datacenter workload coexistence. Further in this work\, we explore proactively monitoring for congestion with the help of phantom queues and rerouting flows in a timely manner. Through Balancia2.0 we decouple congestion control and load balancing signaling\, and examine its effects on DC and WAN traffic. \nEvent Host: Lakshmi Krishnaswamy\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Katia Obraczka \nZoom: https://ucsc.zoom.us/j/94414934371?pwd=7P3Umt0QQ930ESV02jMvCVHVbkIp9r.1 \nPasscode: 905786
URL:https://events.ucsc.edu/event/krishnaswamy-l-cse-network-load-balancing-for-geographically-distributed-datacenters/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260814T141500
DTEND;TZID=America/Los_Angeles:20260814T161500
DTSTAMP:20260810T163804Z
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SUMMARY:Aliamooei Lakeh\, S. (ECE) - Optimization and Decision-Support Frameworks for Resilient Power Systems Under Large-Scale Electrification
DESCRIPTION: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 critical example: transmission outages and public safety power shutoffs can reduce network capacity while evacuation simultaneously concentrates charging demand along affected transportation corridors. Improving resilience therefore requires coordinated decision-making across the full disaster lifecycle\, from infrastructure preparedness to emergency operation and post-disaster recovery.\nThis research develops optimization and decision-support methods for resilient power systems under large-scale transportation electrification\, addressing three complementary stages of resilience. First\, the research will extend existing infrastructure planning models through a two-stage stochastic mixed-integer programming framework for the strategic siting and sizing of distributed generation\, energy storage systems\, and EV charging infrastructure under disaster uncertainty. Second\, building on a developed single-period nonlinear AC optimal power flow formulation\, the research will extend the framework to multi-period operation to coordinate priority-based EV evacuation charging with mobile EV charger dispatch during grid contingencies while explicitly representing voltage and thermal operating constraints. Third\, a mixed-integer routing and scheduling framework is proposed for the deployment of mobile energy resources\, including energy tankers and vehicle-to-everything (V2X)-capable fleets\, to support electric transportation and critical loads when conventional infrastructure is disrupted.\nTogether\, these components connect long-term infrastructure planning\, emergency grid operation\, and post-disaster energy recovery within an integrated optimization and decision-support framework. The research will build on preliminary results obtained using IEEE benchmark systems and will incorporate California case studies representing wildfire and flooding scenarios. Resilience will be evaluated using technical and operational metrics such as load not served\, priority-weighted EV energy served\, and recovery time. The overall goal is to provide decision-support tools for utilities\, transportation agencies\, and emergency planners to support resilient planning\, operation\, and recovery in increasingly electrified energy and transportation systems. \nEvent Host: Saeed Aliamooei Lakeh\, Ph.D. Student\, Electrical & Computer Engineering \nAdvisors: Keith Corzine and Leila Parsa \nZoom: https://ucsc.zoom.us/j/95295718011?pwd=1Y5vBhoBX9V5OVQ3MJzy4FgyhtO9eb.1 \nPasscode: 545834
URL:https://events.ucsc.edu/event/aliamooei-lakeh-s-ece-optimization-and-decision-support-frameworks-for-resilient-power-systems-under-large-scale-electrification/
CATEGORIES:Ph.D. Presentations
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LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260817T100000
DTEND;TZID=America/Los_Angeles:20260817T120000
DTSTAMP:20260810T162940Z
CREATED:20260810T162940Z
LAST-MODIFIED:20260810T162940Z
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SUMMARY:Nikolakakis\, M. (ECE) - Learned Gridless Representations of Cone Beam Computed Tomography Scans
DESCRIPTION:Medical image representation has long been dominated by voxel-grid matrices. While\ntheir inherent structure and order work efficiently for various linear transformations and\nprovide a seamless visualization method on monitors\, they fail to preserve the topology\nof the scan and to encode sparse information in a memory-efficient way.   The recent emergence of machine learning-based continuous coordinate-based\nscene representations such as neural radiance fields and Gaussian splatting has provided alternative representation techniques. These approaches overfit the weights of\na model by iterative differentiable rendering and have been shown to be more compact than grid representations. They are then able to perform novel view\nsynthesis from any given camera pose.\nOff-grid representations translate directly to Cone Beam Computed Tomography\nsparse-view acquisitions\, where streaking and quantum noise artifacts are dominant.\nUsing differentiable rendering\, a continuous representation is achieved\, with interpolation providing a path to recover some of the lost signal.\nIn this dissertation\, we apply a variety of methodologies\, including Gaussian splatting\, implicit occupancy fields\, and Neural Attenuation Fields regularized with an\nanatomic prior\, to Cone Beam Computed Tomography reconstruction\, and evaluate\ntheir performance across a range of anatomic datasets. Our models show that learned\ngridless representations achieve substantial memory reduction\, recover signal under\nextreme view sparsity\, and preserve scene topology. \nEvent Host: Manolis Nikolakakis\, Ph.D. Candidate\, Electrical and Computer Engineering  \nAdvisor: Razvan Marinescu \nZoom: https://ucsc.zoom.us/j/5964517596?pwd=c1AwRlJLNk5pVzFBUENibEw3by85Zz09
URL:https://events.ucsc.edu/event/nikolakakis-m-ece-learned-gridless-representations-of-cone-beam-computed-tomography-scans/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260817T130000
DTEND;TZID=America/Los_Angeles:20260817T150000
DTSTAMP:20260813T194341Z
CREATED:20260813T194341Z
LAST-MODIFIED:20260813T194341Z
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SUMMARY:Condon\, C. (BMEB) - Genomic conflict across scales
DESCRIPTION: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 investigate segregation distortion in Arabidopsis hybrids and its potential role in the early evolution of reproductive isolation. Second\, I characterize the population dynamics and functional effects of introners\, mobile elements that generate new introns in the green alga Micromonas pusilla. Finally\, I explore widespread splicing dysfunction in algal mating-type chromosomes and its consequences for transcript diversity. Together\, these studies highlight how departures from genome cooperation can shape inheritance\, genome evolution\, and gene regulation. \nEvent Host: Chris Condon\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Russell Corbett-Detig
URL:https://events.ucsc.edu/event/condon-c-bmeb-genomic-conflict-across-scales/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260818T100000
DTEND;TZID=America/Los_Angeles:20260818T110000
DTSTAMP:20260810T162245Z
CREATED:20260810T162245Z
LAST-MODIFIED:20260810T162245Z
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SUMMARY:Gutie\, J. (SciCAM) -  SORh: Hyperbolic Relaxation Methods For Elliptic Problems In Computational Fluid Dynamics
DESCRIPTION: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 such as Jacobi and Gauss-Seidel\, these approaches involve tradeoffs in computational cost\, scalability\, implementation complexity\, and adaptability to changing boundary conditions and problem configurations. \nTherefore\, we introduce SORh\, a simple and efficient relaxation method derived from a hyperbolic reformulation of Poisson’s equation. SORh generalizes classical successive over-relaxation (SOR) by providing independent control of residual relaxation and the directional propagation of Gauss–Seidel corrections. We present formulations of SORh in one and two spatial dimensions and investigate its stability\, accuracy\, and computational performance through analytical derivations and numerical comparisons with established relaxation methods. The results identify favorable SORh formulations\, clarify their relationships to classical relaxation methods\, and demonstrate improved convergence on selected test problems. Finally\, we demonstrate applications of SORh to astrophysical self-gravity simulations in the FLASH code and to magnetohydrodynamic (MHD) divergence cleaning. \nEvent Host: Jonathan Guite\, M.S. Candidate\, Scientific Computing & Applied Mathematics  \nAdvisor: Dongwook Lee \nZoom: https://ucsc.zoom.us/j/92153750104?pwd=ZdLiDZeLqOAlVNX9C4bCloKno9tAeB.1 \nPasscode: 769232
URL:https://events.ucsc.edu/event/gutie-j-scicam-sorh-hyperbolic-relaxation-methods-for-elliptic-problems-in-computational-fluid-dynamics/
CATEGORIES:Ph.D. Presentations
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LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260818T140000
DTEND;TZID=America/Los_Angeles:20260818T160000
DTSTAMP:20260817T160201Z
CREATED:20260817T155423Z
LAST-MODIFIED:20260817T160201Z
UID:10015341-1787061600-1787068800@events.ucsc.edu
SUMMARY:Lupin-Jimenez\, L. (AM) - Data-Driven Deep Learning for Turbulent Phenomena: Regional Ocean Prediction and Assimilation\, Spectral Bias in Diffusion Models\, and Equation Discovery
DESCRIPTION: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.\nTheir scientific utility depends on physical consistency\, which for the systems studied here\nrests in large part on spectral fidelity\, the accurate reconstruction of variance across spatial\nscales. This document presents two published studies and two studies in progress that develop\, analyze\, and apply data-driven methods for turbulent phenomena along that thread.\nThe first study develops FCDS\, a framework that autoregressively emulates surface ocean\ndynamics over the Gulf of Mexico at 8 km resolution and simultaneously downscales and\nbias-corrects the emulated fields to 4 km\, with a spectral loss that keeps decadal integrations stable and statistically consistent with a high-resolution reanalysis. The second study\ndevelops a neural-operator-conditioned denoising diffusion model that reconstructs regional\nsurface ocean states from Lagrangian-like observations at 99% and 99.9% sparsity without a\nbackground dynamical model\, and shows that the recovered small-scale dynamics are visible\nin spectral diagnostics but not in pointwise metrics. The third study derives a signal-tonoise theory of spectral bias in diffusion models for 2D turbulence\, organized around the\ncrossover wavenumber kc(τ) at which signal and noise contribute equal power\, and validates\nits predictions on a sweep of 28 models spanning seven forcing wavenumbers and four noise\nschedulers. The fourth study develops a window-pair spectral method for discovering governing equations from single-point sensor measurements of soliton dynamics in a superfluid\nwave flume\, replacing noise-amplifying instantaneous derivatives with finite-time spectral\nshifts and verifying the discovered equations against a measured-scalar null model. A concluding chapter summarizes the results and outlines future work on novel architectures and\nmethods for data-driven emulation of physical simulations. \nEvent Host: Leonard Lupin-Jimenez\, Ph.D. Student\, Applied Mathematics  \nAdvisor: Ashesh Chattopadhyay \nZoom: https://ucsc.zoom.us/j/97866640488?pwd=UJdTs3sxKfFbz5mabKLIyx5ZYF90J9.1 \nPasscode: 815911
URL:https://events.ucsc.edu/event/lupin-jimenez-l-am-data-driven-deep-learning-for-turbulent-phenomena-regional-ocean-prediction-and-assimilation-spectral-bias-in-diffusion-models-and-equation-discovery/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260820T130000
DTEND;TZID=America/Los_Angeles:20260820T150000
DTSTAMP:20260814T163909Z
CREATED:20260814T163815Z
LAST-MODIFIED:20260814T163909Z
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SUMMARY:Penunuri\, G. (BMEB) - Genomic\, Proteomic\, and Computational Approaches to the Study of Host-Microbe Systems
DESCRIPTION: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 be cultured outside a host\, resist standard tools for genetic manipulation\, and are annotated largely by homology to distantly related free-living relatives. This dissertation develops genomic\, proteomic\, and computational methods to work around this lack of infrastructure and contribute techniques and tools to the study and further understanding of host-microbe systems. Using Wolbachia cultured in Drosophila melanogaster cell lines\, I demonstrate that chemical mutagenesis can be used to perturb intracellular genomes leaving a detectable mutational signal. I employ a low error rate sequencing technique to record and model the mutational landscape left by the mutagen ethyl methanesulfonate (EMS) demonstrating its use for mutagenesis screens of intracellular bacteria. I next utilize structural proteome datasets to screen host-microbe proteomes for strong candidates of molecular mimicry\, microbe proteins that have coevolved a eukaryotic like domain or structure and suggest use for host manipulation or microbe survival in the host environment. Building off of this screen for novel effectors through structural alignments I develop and test a distributed computing system for performing large scale systematic literature reviews. Altogether these projects represent generalizable approaches to the study of host-microbe systems reaching from classically studied and thoroughly understood to novel and non-model systems. \nEvent Host: Gabriel Penunuri\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Russell Corbett-Detig \nZoom: https://ucsc.zoom.us/j/98216883331?pwd=uqmUSQba2X6GVNBhOhAGRwgCZyjAyj.1 \nPasscode: 730377
URL:https://events.ucsc.edu/event/penunuri-g-bmeb-genomic-proteomic-and-computational-approaches-to-the-study-of-host-microbe-systems/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260821T110000
DTEND;TZID=America/Los_Angeles:20260821T120000
DTSTAMP:20260820T171344Z
CREATED:20260820T171314Z
LAST-MODIFIED:20260820T171344Z
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SUMMARY:Nava\, A. (AM) - Machine-Learning Methods for Prediction of Biological Systems
DESCRIPTION: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 process in which bacteria begin metabolic activity\, and embryonic stem cell organization. Bacterial spore germination is a critical transition in which the spore becomes susceptible to control techniques\, however the mechanisms governing this transition are unknown. We develop a machine-learning framework that predicts germination timing at the single-spore level\, enabling identification of predictive features associated with germination that may reflect underlying biological mechanisms. Embryonic stem cell organization has been shown to closely recapitulate formations seen in embryonic development\, but the mechanisms driving their spatial organization remain unclear. Here\, we develop a simple agent-based model in which spatial organization is driven by cell-cell interaction parameters. We then train a machine-learning framework to infer these underlying interaction parameters from simulated data and propose that this approach can be extended to other agent-based models calibrated to experimental stem cell data. These studies demonstrate that machine-learning models can be used for prediction using single-cell temporal data\, as well as tools to develop mechanistic hypotheses. \n  \nEvent Host: Alexandra Nava\, Ph.D. Student\, Applied Mathematics  \nAdvisor: Marcella Gomez \nZoom: https://ucsc.zoom.us/j/98821445104?pwd=OFAKwGrObh02bPLgXieXsDcTxxS1Cj.1 \nPasscode: 392769
URL:https://events.ucsc.edu/event/nava-a-am-machine-learning-methods-for-prediction-of-biological-systems/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260821T150000
DTEND;TZID=America/Los_Angeles:20260821T160000
DTSTAMP:20260817T160248Z
CREATED:20260817T160020Z
LAST-MODIFIED:20260817T160248Z
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SUMMARY:Huang\, X. (CSE) - Scalable and Verifiable Reasoning for Medical Foundation Models
DESCRIPTION: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\, the proposed research will extend medical language and multimodal models toward agentic systems that can gather evidence\, use external tools\, integrate multimodal information\, and verify decisions over sequential interactions. Overall\, this research aims to improve the reliability\, efficiency\, and transparency of medical AI reasoning while supporting reproducible and human-supervised applications in healthcare. \nEvent Host: Xiaoke Huang\, Ph.D. Student\, Computer Science & Engineering \nAdvisor: Yuyin Zhou \nZoom: https://ucsc.zoom.us/j/8855787311 \nPasscode: 197379
URL:https://events.ucsc.edu/event/huang-x-cse-scalable-and-verifiable-reasoning-for-medical-foundation-models/
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260824T140000
DTEND;TZID=America/Los_Angeles:20260824T160000
DTSTAMP:20260818T161442Z
CREATED:20260818T161442Z
LAST-MODIFIED:20260818T161442Z
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SUMMARY:Pawar\, M. (CSE) - Understanding Representations\, Reasoning\, and Decision-Making in Autonomous Driving Models
DESCRIPTION: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 vision-language-action models. \nEvent Host: Manasi Pawar\, Ph.D. Student\, Computer Science & Engineering  \nAdvisor: Leilani Gilpin \nZoom: https://ucsc.zoom.us/j/92653015420?pwd=xEyOcy5ZuTcHN04La9w2KP1cmVY1ao.1 \nPasscode: 309299
URL:https://events.ucsc.edu/event/pawar-m-cse-understanding-representations-reasoning-and-decision-making-in-autonomous-driving-models/
CATEGORIES:Ph.D. Presentations
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LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260825T110000
DTEND;TZID=America/Los_Angeles:20260825T130000
DTSTAMP:20260812T161153Z
CREATED:20260812T161153Z
LAST-MODIFIED:20260812T161153Z
UID:10015333-1787655600-1787662800@events.ucsc.edu
SUMMARY:Gomez\, J. (CSE) - Toward Sustainable and Secure Open Source Software: Discovery\, Measurement\, and Defense
DESCRIPTION: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 SSH. \nThe success of this backdoor was enabled by two failures. The first was technical: weaknesses in the software supply chain allowed a malicious actor to inject code into a widely trusted release. The second was human: the project’s only maintainer\, overwhelmed and burned out after years of maintaining critical infrastructure alone\, was the target of a multi-year social engineering campaign\, in which a malicious actor built trust under a false identity and gradually obtained commit access to the project. This incident shows that software security failures and sustainability failures are not independent: an overburdened\, unsupported maintainer is itself an attack surface. \nAcademic and scientific open source software (OSS) faces both of these crises simultaneously. Projects that critical infrastructure depends on are maintained by researchers\, students\, and faculty who contribute in their spare time\, without dedicated security training or institutional support. Existing security frameworks including NIST’s SSDF\, OWASP’s SCVS\, and SLSA were not designed with these communities in mind\, and policy efforts such as the EU Cyber Resilience Act have shown that mandates developed without community input risk harming the ecosystems they are meant to protect. \nThis dissertation addresses the sustainability and security of academic open source software through two parallel empirical research tracks. The sustainability track combines GitHub’s REST API with LLM-based filtering to discover and characterize over 216\,000 institutionally affiliated repositories across 32 academic and research institutions\, finding that while 84\% include a README\, only 23.4% carry a detectable license and fewer than 2% include a Contributing Guide. Building on this dataset\, we develop a maturity-staged sustainability framework that classifies projects into four lifecycle stages and generates targeted recommendations for Open Source Program Offices (OSPOs). \nThe security track examines whether post-9/11 trade security programs offer a workable model for OSS supply-chain policy\, finding that effective frameworks require voluntary incentives and direct community engagement rather than top-down mandates. We further evaluate five large language models on the OWASP Benchmark for vulnerability triage\, finding that o1-mini reduces false positives by 20% over the Semgrep baseline\, demonstrating the potential for automation to reduce the security burden on individual maintainers. \nTogether\, these contributions treat sustainability and security as interconnected problems. A project that cannot sustain itself cannot secure its code\, and this dissertation takes steps toward closing both gaps. \n  \nEvent Host: Juanita Gomez\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Alvaro Cardenas  \nZoom: https://ucsc.zoom.us/j/91057980344?pwd=XMMjHZVgbbLXfwxKehrTEbat18066o.1 \nPasscode: 292091
URL:https://events.ucsc.edu/event/gomez-j-cse-toward-sustainable-and-secure-open-source-software-discovery-measurement-and-defense/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260827T150000
DTEND;TZID=America/Los_Angeles:20260827T170000
DTSTAMP:20260814T163137Z
CREATED:20260814T163137Z
LAST-MODIFIED:20260814T163137Z
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SUMMARY:Kramer\, A. (BMEB) - Scalable phylo-pangenomics
DESCRIPTION: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 computations practical. First\, I evaluate online phylogenetic inference\, in which new genomes are continuously added to an existing tree\, and compare parsimony-based methods with maximum-likelihood approaches under pandemic time constraints. For densely sampled SARS-CoV-2 genomes\, online inference with UShER and matOptimize produces trees comparable to established maximum-likelihood methods while requiring orders of magnitude less time and memory. I then develop tools that make phylogenies containing millions of genomes useful for downstream analysis and visualization. ShUShER enables privacy-preserving phylogenetic placement within a web browser\, allowing laboratories to analyze sensitive sequences without transmitting them to an external server. Treenome Browser co-visualizes the genomic variation of millions of samples alongside their phylogenetic relationships by operating directly on a compressed mutation-annotated tree. Finally\, I describe Panmap\, which uses Pangenome Mutation-Annotated Networks (PanMANs) to place\, align\, and genotype sequencing reads and to estimate haplotype abundances against reference collections containing up to millions of genomes. Panmap produces indexes hundreds of times smaller than graph-based alternatives\, improves genome reconstruction over single-reference workflows at low coverage\, and supports applications ranging from pathogen genome assembly to ancient environmental DNA analysis. Together\, these results show that evolutionary history can serve not only as an object of inference but as a scalable computational infrastructure for phylogenomic and pangenomic analyses as genomic datasets continue to grow. \nEvent Host: Alexander Kramer\, Ph.D. Candidate\, Biomolecular Engineering & Bioinformatics \nAdvisor: Russell Corbett-Detig \nZoom: https://ucsc.zoom.us/j/91364025182?pwd=Tq95CuaBqrePatRjopy6uJ9bbjsrIH.1 \nPasscode: 318268
URL:https://events.ucsc.edu/event/kramer-a-bmeb-scalable-phylo-pangenomics/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260828T090000
DTEND;TZID=America/Los_Angeles:20260828T120000
DTSTAMP:20260819T161142Z
CREATED:20260819T161142Z
LAST-MODIFIED:20260819T161142Z
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SUMMARY:Saleem\, O. (ECE) - Coupled Evacuation Readiness and Post-Disaster Restoration for Vehicle-to-Grid Enabled Resilient Power–Transportation Networks
DESCRIPTION: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 governed by the same underlying resource\, i.e.\, electric vehicle batteries. On the other hand\, same underlying constraint\, a damaged\, congested transportation network. This dissertation develops an integrated framework that spans the full disaster lifecycle\, coupling evacuation-phase EV readiness assessment with restoration-phase vehicle-to-grid (V2G) coordination. \nThe work begins by establishing a quantitative ZEV Evacuation Readiness Score (ZEV Score) that assess community preparedness across exposure of the region to various natural disasters based on historical data\, vulnerabilities of the available infrastructure and the adaptive capacity of the infrastructure under stress. This work was afterwards extended to 44-indicator resilience framework across 6 domains i.e. community engagement\, charging infrastructure\, mobile and backup power\, transportation routing\, exposure\, equity\, and community engagement. Building on this foundation\, the dissertation introduces a novel restoration architecture that couples vehicle-routing-problem-based crew dispatch with game-theoretic V2G aggregation through a physics-consistent islanding-duration variable — the first framework to close the loop between road/grid repair scheduling and battery energy management. Validated on the IEEE RTS-79 network with Tesla Model 3 battery dynamics\, this coupling eliminates the “battery dead-zone” failure mode entirely\, reducing cumulative unsupported outage time from 54.3 to 0 hours under worst-case coordination scenarios\, while cutting total load shed by up to 18% beyond standalone repair coordination. \nTogether\, these contributions reframe EV fleets not merely as evacuation liabilities to be planned around\, but as a coordinated\, timeline-aware energy resource spanning both the flight from disaster and the recovery that follows. The dissertation’s proposed aims extend this integration towards stochastic damage scenarios\, heterogeneous mobile energy resources\, and a unified pre- to post-disaster co-optimization pipeline. Which offer both a technical bridge between restoration engineering and evacuation planning\, and a policy-relevant tool for California communities navigating a fully electrified\, climate-exposed future. \nEvent Host: Osman Saleem\, Ph.D. Student\, Electrical & Computer Engineering \nAdvisor: Keith Corzine & Leila Parsa  \nZoom: https://ucsc.zoom.us/j/96031345847?pwd=bjLMYhuyPyIMt7deiEKWRh35I7vjUW.1 \nPasscode: 544944
URL:https://events.ucsc.edu/event/saleem-o-ece-coupled-evacuation-readiness-and-post-disaster-restoration-for-vehicle-to-grid-enabled-resilient-power-transportation-networks/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260828T100000
DTEND;TZID=America/Los_Angeles:20260828T120000
DTSTAMP:20260813T164521Z
CREATED:20260813T164521Z
LAST-MODIFIED:20260813T164521Z
UID:10015336-1787911200-1787918400@events.ucsc.edu
SUMMARY:Nag\, S. (BMEB) - Personalized Diploid Genome Graphs for Accurate Somatic Variant Discovery
DESCRIPTION: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 misclassified as somatic. I propose replacing the generic reference with a personalized diploid genome graph constructed from a donor-specific assembly (DSA)\, which represents both inherited haplotypes of the individual. I will develop this framework by (1) generating haplotype-resolved\, telomere-to-telomere DSAs for cancer reference cell lines\, (2) developing haplotype-aware graph alignment and adapting DeepSomatic to call variants against personalized diploid genomes\, and (3) applying this approach across tissues from SMaHT donors to improve somatic mosaicism detection and characterize shared and tissue-specific mutations. \nEvent Host: Sagorika Nag\, Ph.D. Student\, Biomolecular Engineering & Bioinformatics  \nAdvisor: Benedict Paten \nZoom: https://ucsc.zoom.us/j/99844148597?pwd=amp5Nhmj2UeodTADUdaJwZsKtscRMG.1 \nPasscode: 685655
URL:https://events.ucsc.edu/event/nag-s-bmeb-personalized-diploid-genome-graphs-for-accurate-somatic-variant-discovery/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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