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DTSTART;TZID=America/Los_Angeles:20260724T140000
DTEND;TZID=America/Los_Angeles:20260724T160000
DTSTAMP:20260716T222234Z
CREATED:20260716T222234Z
LAST-MODIFIED:20260716T222234Z
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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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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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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260803T160000
DTEND;TZID=America/Los_Angeles:20260803T170000
DTSTAMP:20260724T212920Z
CREATED: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://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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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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