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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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DTSTART;TZID=America/Los_Angeles:20260723T120000
DTEND;TZID=America/Los_Angeles:20260723T140000
DTSTAMP:20260708T162027Z
CREATED:20260708T162027Z
LAST-MODIFIED:20260708T162027Z
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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
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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:
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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@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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