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DTSTART;TZID=America/Los_Angeles:20260717T113000
DTEND;TZID=America/Los_Angeles:20260717T133000
DTSTAMP:20260715T163613Z
CREATED:20260715T163517Z
LAST-MODIFIED:20260715T163613Z
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SUMMARY:Calicchio\, A. (BMEB) - Comparison of long-read sequencing and analysis methods for transcriptome analysis
DESCRIPTION: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 ability to characterize isoforms\, since single reads can span full-length transcripts\, but limitations still prevent our identification of all the isoforms in the human transcriptome. Our research proposes to improve both the library preparation and computational analysis steps of the isoform identification process.\nTo do so\, we are updating the isoform identification and quantification tool IG28 (previously called Mandalorion) so that it can analyse both bulk and single-cell long-read sequencing data and. By pairing our analysis with single-cell clustering in Seurat\, we can generate transcriptomes for hundreds of thousands of single cells\, for individual cell types\, and for bulk datasets containing hundreds of millions of reads\, providing a scalable approach to identify isoforms in the largest and most recent datasets.\nFurthermore\, since long reads can carry both the variants defining an allele of origin and the full isoform structure\, we plan to extend IG28 to perform allele-specific transcript usage analysis. We plan to include accurate statistical tests in this module by using beta-binomial and Dirichlet-multinomial models that account for overdispersion\, to provide a tested and integrated pipeline for isoform allelic assignment.\nFinally\, recognizing that isoform detection depends on the quality\, length\, and throughput of the input data\, we are improving library preparation and benchmarking sequencing technologies. We are refining the R2C2 protocol coupled with size selection to overcome the current circularization limit for fragments beyond 6 kb\, and we are generating matched datasets to compare R2C2 to the Kinnex library preparation method\, and ONT against PacBio HiFi sequencing\, to determine which approaches produce the most accurate and longest reads for isoform identification.\nTogether\, these advances will provide a competitive pipeline\, from cDNA preparation to isoform identification and annotation\, enabling accurate isoform annotations that can lead to a deeper understanding of cell differentiation and disease etiology. \nEvent Host: Alessandro Calicchio\, Ph.D. Student\, Biomolecular Engineering & Bioinformatics \nAdvisor: Christopher Vollmers \nZoom: https://ucsc.zoom.us/j/92704819548?pwd=PUqQpq0Soandz8E5DIPCXFdvnFaf00.1 \nPasscode: 760165
URL:https://live-events-ucsc.pantheonsite.io/event/calicchio-a-bmeb-comparison-of-long-read-sequencing-and-analysis-methods-for-transcriptome-analysis/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260722T110000
DTEND;TZID=America/Los_Angeles:20260722T130000
DTSTAMP:20260708T160702Z
CREATED:20260708T160702Z
LAST-MODIFIED:20260708T160702Z
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SUMMARY:Holmes\, J. (CM) - Towards a Multi-dimensional Model of User Load
DESCRIPTION: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 NASA-TLX\, rely on player reflections of mental effort\, primarily at the end of the playtest session\, to distinguish different cognitive load types. This makes it difficult to: (1) understand where specifically players are struggling and experiencing high load\, especially at the non-reflective subconscious level\, (2) identify where that load is primarily coming from (e.g.\, perceptual clutter or difficulty/skill imbalance)\, and (3) examine user overload at scale\, a crucial component of designing a game with large player bases. Telemetry is the behavioral record of what players are doing from moment to moment\, in varying degrees of granularity. Telemetry has served as a powerful tool to understand player behaviors at scale\, yet is rarely used to measure user load\, especially through a validated multidimensional framework. This dissertation proposes that behavioral signatures of specific load constructs are observable in game telemetry\, and a model built and validated on such telemetry can measure the distinct components of each load at the moment-to-moment granularity of individual play\, as opposed to aggregated magnitude. This dissertation consists of three parts: (1) Validation through construct manipulation and reference measurement. Specifically\, manipulating theoretically grounded load constructs and confirming that the proposed telemetry features respond as predicted\, relative to the established measurements collected alongside them (e.g.\, NASA-TLX\, pupillometry\, secondary-task). (2) Individual-level validation through rigorous longitudinal examination of the same players repeatedly across many sessions such that load constructs can be tracked at the within-person granularity. This is necessary to establish that the measure works for an individual player and not just for population averages. (3) Test the user load model by applying it to naturalistic game telemetry. Additionally\, this phase will entail the development of an insight-oriented measurement tool for GURs based on our validated user load model. The overarching contribution is a behavioral\, telemetry-based method for measuring multidimensional user load in games\, validated to measure load within each person (individual-level). This gives GURs a scalable tool and replicable process for detecting user load in commercial game telemetry. \nEvent Host: Jonattan Holmes\, Ph.D. Student\, Computational Media \nAdvisor: Magy Seif El-Nasr \nZoom: https://ucsc.zoom.us/j/98245962806?pwd=HnkwPMFSamQJFrE5aihbZbKDBbt4s9.1 \nPasscode: 347521
URL:https://live-events-ucsc.pantheonsite.io/event/holmes-j-cm-towards-a-multi-dimensional-model-of-user-load/
CATEGORIES:Ph.D. Presentations
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LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260723T100000
DTEND;TZID=America/Los_Angeles:20260723T120000
DTSTAMP:20260715T162923Z
CREATED:20260715T162923Z
LAST-MODIFIED:20260715T162923Z
UID:10015089-1784800800-1784808000@live-events-ucsc.pantheonsite.io
SUMMARY:Chen\, X. (STAT) - Changepoint Detection and Clustering Methods for Multivariate Time Series and Attributed Networks
DESCRIPTION: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 in the mean of a multivariate stationary time series. The proposed framework accommodates short-range dependence\, encompasses classical CUSUM and Wilcoxon tests as special cases\, and admits a tractable limiting distribution after a pre-whitening transformation. Second\, we study nodal clustering in graphs with dynamic attributes through a decoder-only latent space framework that integrates temporal dynamics and structural information via a graph-fused LASSO regularization. An extension of this framework\, in which the neural network decoder is replaced by an autoregressive structure at each node\, is also introduced. Lastly\, a future research project is proposed on modeling and changepoint inference for Arctic sea ice coverage data\, whose marginal distribution is doubly inflated with point masses at zero and one. A latent Gaussian process transformation approach is outlined that accommodates this exotic marginal structure while permitting temporal and spatial autocorrelation\, trends\, and seasonal dynamics. In tandem\, these efforts aim to provide flexible and theoretically grounded tools for analyzing complex dependent data. \nEvent Host: Xi Chen\, Ph.D. Student\, Statistical Science \nAdvisor: Robert Lund \nZoom: https://ucsc.zoom.us/j/97760514185?pwd=ImfeI5uEdBvq9eoiFXnF5pecmwfVHd.1 \nPasscode: 333103
URL:https://live-events-ucsc.pantheonsite.io/event/chen-x-stat-changepoint-detection-and-clustering-methods-for-multivariate-time-series-and-attributed-networks/
CATEGORIES:Ph.D. Presentations
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LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260723T120000
DTEND;TZID=America/Los_Angeles:20260723T140000
DTSTAMP:20260708T162027Z
CREATED:20260708T162027Z
LAST-MODIFIED:20260708T162027Z
UID:10015013-1784808000-1784815200@live-events-ucsc.pantheonsite.io
SUMMARY:Li\, J. (CM) - Detecting Failure to Adapt: Reading Self-Regulated Learning Breakdowns from Game Telemetry through Plan Recognition
DESCRIPTION: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 one breakdown as its object: failure-to-adapt\, the case where the game has repeatedly surfaced evidence that a learner’s current approach is failing and the learner’s approach shows no responsive change. The construct is grounded in Winne and Hadwin’s monitor-and-control model of self-regulated learning and defined at the level of the learner’s plan. To detect it\, a plan-recognition engine maintains a continuously updated probability estimate of which strategy the learner is executing across the whole trace; an episode is flagged when that estimate shows no evidence-responsive revision. Because behavior alone cannot settle what broke down\, flagged episodes are validated against learners’ own verbal reports\, coded blind\, and decomposed into monitoring failure\, control failure\, or control the trace cannot show. Three studies carry the work: detection and diagnosis on real telemetry from an educational game\, including a comparison against the analytics the field already runs; a formative study of what a facilitator (an instructor or teaching assistant running a class play session) must see to judge correctly which learners need attention; and a documented authoring case carrying the detection to a second game. The contribution is knowledge for game-based-learning researchers: a theory-grounded construct\, a validated way to detect it from play\, and the authoring knowledge to embed that detection in new games. \nEvent Host: Jiahong Li\, Ph.D. Student\, Computational Media \nAdvisor: Magy Seif El-Nasr \nZoom: https://ucsc.zoom.us/j/93238603235?pwd=zENRsu82HRj4JYKcMEn9MZibU8kC7F.1 \nPasscode: 835328
URL:https://live-events-ucsc.pantheonsite.io/event/li-j-cm-detecting-failure-to-adapt-reading-self-regulated-learning-breakdowns-from-game-telemetry-through-plan-recognition/
CATEGORIES:Ph.D. Presentations
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LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260724T140000
DTEND;TZID=America/Los_Angeles:20260724T160000
DTSTAMP:20260716T222234Z
CREATED:20260716T222234Z
LAST-MODIFIED:20260716T222234Z
UID:10015099-1784901600-1784908800@live-events-ucsc.pantheonsite.io
SUMMARY:Gholami\, K. (ECE) - Efficient Language Model Construction and Inference via Sparsity
DESCRIPTION: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 behavioral fingerprint. Then\, we introduce a semi-structured correlation-aware weight sparsity (CWS) method that uses the full activation covariance to identify and prune correlated weights whose combined removal cost is lower than any individual score predicts. CWS\, improves perplexity over existing criteria up to 70% sparsity. To extend this gain to extreme sparsity\, we propose a hierarchical ADMM framework that optimizes pruning directly against cross-entropy and distillation loss\, first layer-wise for efficiency and then globally for cross-layer coordination. This research establishes brain-inspired principles as a foundation for efficient language models that remain accurate even under extreme compression. \nEvent Host: Kimia Gholami\, Ph.D. Student\, Electrical & Computer Engineering \nAdvisor: Jason Eshraghian \nZoom: https://ucsc.zoom.us/j/9827512398?pwd=SGpDWGtVVG81dkgyTHhjbG81dEVUZz09&omn=98349793611 \nPasscode: 8398
URL:https://live-events-ucsc.pantheonsite.io/event/gholami-k-ece-efficient-language-model-construction-and-inference-via-sparsity/
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:20260724T140000
DTEND;TZID=America/Los_Angeles:20260724T170000
DTSTAMP:20260720T162132Z
CREATED:20260720T162049Z
LAST-MODIFIED:20260720T162132Z
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SUMMARY:Fontana\, J. (STAT) - When We're Always Wrong: Scalable Variable Selection in M-Open Settings
DESCRIPTION: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 in the M-open setting\, where the data generating process is outside the model space. We focus on the novel problem of “model superinduction”\, which refers to the tendency of model selection procedures to select larger models at an exponential rate as the sample size grows\, resulting in overparametrized models which collapse model interpretability and induce severe computational difficulties. For a set of popular frequentist information criteria and the Bayesian case of mixtures of g-priors\, we prove that when comparing nested models\, the larger model will always be asymptotically selected. We seek to minimize this effect for large n while preserving variable selection consistency.We propose utilizing a mixture of g-priors\, where the hyper-prior on g has hyper-parameters chosen to result in a slowly diminishing rate of prior influence on the posterior\, which favors simpler models while preserving consistency. We also propose a model space prior which induces stronger model complexity penalization for large sample sizes. The posterior model probabilities under our prior choices further provide an alternative information criterion that is resistant to the effects of model superinduction. \nNext\, we extend our results to other classes of popular variable selection priors\, the family of spike and slab priors\, the non-local priors\, and selection procedures that correspond to posterior modes such as the LASSO. We show that these procedures are all afflicted with model superinduction\, except for the continuous spike and slab priors when a Student-t distribution is used for both the spike and the slab. \nFinally\, we address existing bottlenecks in the computation efficacy of spike and slab based variable selection. We demonstrate that Gibbs samplers scale poorly to large sample sizes\, and the proposed alternatives in the literature result in posterior surrogates that are afflicted with superinduction. Instead\, we propose a search strategy based off easy to compute approximations of the posterior model probabilities. We show this procedure\, Fast Approximate Stochastic Search (FASS)\, coupled with post-hoc inference on parameters via either a Block-Variational Bayes approach or an Expectation Propagation approach\, results in competitive performance. We demonstrate the aforementioned phenomena\, and the efficacy of our proposed solutions\, via synthetic data examples and case studies using albedo data from GOES satellites and LCA application data. \nEvent Host: Jacob Fontana\, Ph.D. Candidate\, Statistical Science \nAdvisor: Bruno Sansó \nZoom: https://ucsc.zoom.us/j/99965109575?pwd=uGkOwWM3Rl3zP66aL1RecoOfB8Yat0.1 \nPasscode: 879019
URL:https://live-events-ucsc.pantheonsite.io/event/fontana-j-stat-when-were-always-wrong-scalable-variable-selection-in-m-open-settings/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260803T160000
DTEND;TZID=America/Los_Angeles:20260803T170000
DTSTAMP:20260724T212920Z
CREATED:20260724T212920Z
LAST-MODIFIED:20260724T212920Z
UID:10015115-1785772800-1785776400@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/event/le-a-stat-bayesian-nonparametric-analysis-of-densities-for-replicated-point-patterns/
CATEGORIES:Ph.D. Presentations
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LOCATION:
END:VEVENT
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@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260814T083000
DTEND;TZID=America/Los_Angeles:20260814T103000
DTSTAMP:20260811T162729Z
CREATED:20260811T162729Z
LAST-MODIFIED:20260811T162729Z
UID:10015331-1786696200-1786703400@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260814T141500
DTEND;TZID=America/Los_Angeles:20260814T161500
DTSTAMP:20260810T163804Z
CREATED:20260810T163804Z
LAST-MODIFIED:20260810T163804Z
UID:10015329-1786716900-1786724100@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/event/aliamooei-lakeh-s-ece-optimization-and-decision-support-frameworks-for-resilient-power-systems-under-large-scale-electrification/
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg
LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260817T100000
DTEND;TZID=America/Los_Angeles:20260817T120000
DTSTAMP:20260810T162940Z
CREATED:20260810T162940Z
LAST-MODIFIED:20260810T162940Z
UID:10015328-1786960800-1786968000@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png
GEO:37.0009723;-122.0632371
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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
UID:10015338-1786971600-1786978800@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/event/condon-c-bmeb-genomic-conflict-across-scales/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg
GEO:46.1226939;-64.7891251
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Biomedical Sciences Building 575 McLaughlin Drive;X-APPLE-RADIUS=500;X-TITLE=575 McLaughlin Drive:geo:-64.7891251,46.1226939
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260818T100000
DTEND;TZID=America/Los_Angeles:20260818T110000
DTSTAMP:20260810T162245Z
CREATED:20260810T162245Z
LAST-MODIFIED:20260810T162245Z
UID:10015327-1787047200-1787050800@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg
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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
UID:10015340-1787230800-1787238000@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-1.jpg
GEO:46.1226939;-64.7891251
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Biomedical Sciences Building 575 McLaughlin Drive;X-APPLE-RADIUS=500;X-TITLE=575 McLaughlin Drive:geo:-64.7891251,46.1226939
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260821T150000
DTEND;TZID=America/Los_Angeles:20260821T160000
DTSTAMP:20260817T160248Z
CREATED:20260817T160020Z
LAST-MODIFIED:20260817T160248Z
UID:10015342-1787324400-1787328000@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/event/huang-x-cse-scalable-and-verifiable-reasoning-for-medical-foundation-models/
CATEGORIES:Ph.D. Presentations
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LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260824T140000
DTEND;TZID=America/Los_Angeles:20260824T160000
DTSTAMP:20260818T161442Z
CREATED:20260818T161442Z
LAST-MODIFIED:20260818T161442Z
UID:10015343-1787580000-1787587200@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option2.jpg
GEO:37.0009723;-122.0632371
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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
UID:10015339-1787842800-1787850000@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/event/kramer-a-bmeb-scalable-phylo-pangenomics/
LOCATION:Biomedical Sciences Building\, 575 McLaughlin Drive
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png
GEO:46.1226939;-64.7891251
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Biomedical Sciences Building 575 McLaughlin Drive;X-APPLE-RADIUS=500;X-TITLE=575 McLaughlin Drive:geo:-64.7891251,46.1226939
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@live-events-ucsc.pantheonsite.io
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://live-events-ucsc.pantheonsite.io/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
ATTACH;FMTTYPE=image/png:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260901T090000
DTEND;TZID=America/Los_Angeles:20260901T110000
DTSTAMP:20260812T161813Z
CREATED:20260812T161813Z
LAST-MODIFIED:20260812T161813Z
UID:10015334-1788253200-1788260400@live-events-ucsc.pantheonsite.io
SUMMARY:Shen\, J. (STAT) - Bayesian Modeling and Uncertainty Quantification for Verbal Autopsy Data
DESCRIPTION: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 level cause-of-death probabilities and population level cause-specific mortality fractions. In this document\, we present three projects dealing with challenges current VA modeling faces. In the first project\, we build a shared latent class model for verbal autopsy\, which allows causes to share common symptom patterns. A truncated Bayesian nonparametric prior allows the number of latent classes to adapt to data\, while retaining a computationally tractable representation. We provide a general framework for few-shot learning of the VA data\, where limited labels can be combined in analysis with a potentially much larger collection of unlabeled symptom profile. Two complementary factorizations are considered to account for different types of distribution shift between source and target. In the second project\, we develop a conformal prediction procedure for verbal autposy. For each death\, we generate a conformal prediction set from existing VA model outputs\, which guarantees a marginal coverage of true cause from a fequentist perspective. We also investigate conformal Bayesian procedures that more directly incorporate posterior uncertainty from Bayesian VA models. In the third project\, we consider situations where source and target do not share a common cause list. We propose methods using repulsive priors to identify deaths in the target domain whose symptom profiles are not adequately represented by the known causes in the source domain. \nEvent Host: Jibo Shen\, Ph.D. Student\, Statistical Science \nAdvisor: Zehang Richard Li \nZoom: https://ucsc.zoom.us/j/94158273558?pwd=VZORHL8P5O9bfb5JpSMZAL1DOM44uC.1&jst=2 \nPasscode:  294401
URL:https://live-events-ucsc.pantheonsite.io/event/shen-j-stat-bayesian-modeling-and-uncertainty-quantification-for-verbal-autopsy-data/
CATEGORIES:Ph.D. Presentations
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