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DTSTART;TZID=America/Los_Angeles:20260615T130000
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DTSTAMP:20260623T032526
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SUMMARY:Tang\, M. (STAT) - Bayesian Modeling and Scalable Inference for Count Time Series in Infectious Disease Surveillance
DESCRIPTION:Real-time monitoring of infectious disease outbreaks calls for statistical models that recover interpretable quantities such as the time-varying reproduction number from noisy count data\, track posterior uncertainty\, and run on time scales compatible with daily updates. Existing methods address these aims through separate model classes. Discretized Hawkes processes\, Poisson autoregressions\, and distributed lag models each capture self-exciting transmission through alternative parameterizations of the same conditional mean structure\, but they have been developed across separate software packages with model-specific inference routines\, which makes structural model comparison cumbersome in practice. This dissertation develops a unified Bayesian framework for count time series in disease surveillance\, organized around three threads. First\, a class of dynamic generalized transfer function models places the three modeling families inside a common modular state-space class built from six independent components. A hybrid variational algorithm combines sequential Monte Carlo on the latent trajectory with stochastic gradient ascent on the static parameters. Second\, a multivariate extension to spatially connected regions\, a Bayesian network Hawkes model\, jointly estimates time-varying source-specific reproduction numbers and a sparse transmission network learned from data through a regularized horseshoe prior. The observed reproduction number at each\nlocation is decomposed into a local component and an imported component. Posterior inference proceeds through a blocked Markov chain Monte Carlo sampler\, with a particle Laplace variational counterpart developed for routine refits at larger spatial scales. Third\, an R package implements the unified univariate framework through a compositional specification interface aligned with the six modular components\, with the two inference engines available behind a single entry point. The methods are illustrated through simulation studies and applications to daily COVID-19 case counts from Santa Cruz County and from ten California counties. \nEvent Host: Meini Tang\, Ph.D. Candidate\, Statistical Science  \nAdvisor: Raquel Prado \nZoom: https://ucsc.zoom.us/j/97990210796?pwd=e59WbsNrYgYSITmMw0OIT5f1SQThEN.1 \nPasscode:  479460
URL:https://events.ucsc.edu/event/tang-m-stat-bayesian-modeling-and-scalable-inference-for-count-time-series-in-infectious-disease-surveillance/
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:20260601T160000
DTEND;TZID=America/Los_Angeles:20260601T170000
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SUMMARY:Statistics Seminar: From Random Walks to Planning-Ready World Models: A Normative Model of Place Cells
DESCRIPTION:Presenter: Deqian Kong\, PhD student\, UCLA \nDescription: How does the hippocampus turn experience into a cognitive map that is not just a passive record of space but a representation ready for planning? In this talk\, I will present a normative model in which place cells emerge as a non-negative population embedding whose inner products approximate the multi-step random walk transition kernel across a discrete set of time scales. From this single construction\, a great deal follows. First\, the representation reproduces signature biological phenomena: multi-scale place fields that mirror the hippocampal dorsoventral gradient\, theta phase precession as an angular sweep in representational geometry (angle–phase duality)\, and contextual remapping. Second\, and more consequentially\, the resulting cognitive map is proximity-preserving — Euclidean distance in embedding space monotonically tracks graph distance in the environment — so path planning reduces to following the gradient of the learned embedding\, with no value iteration\, no explicit map reconstruction\, and no learned optimal policy. The underlying one-step transition is just random exploration; optimal trajectories arise from inference on the multi-scale kernel. I will argue that this turns place cells from a phenomenological model of space into a planning-centric world model: a single likelihood objective trains the kernel\, and planning\, goal-reaching\, and re-routing under detours or shortcuts all reduce to gradient queries against the learned geometry. I will close by briefly contrasting this proposal with prevailing world models in machine learning — Vision–Language–Action policies\, model-predictive control\, and latent-dynamics models— to highlight what is distinctive about a planning-ready cognitive map built from random exploration alone. \nBio: Deqian Kong is a PhD candidate in Statistics and Data Science at UCLA\, advised by Prof. Ying Nian Wu\, and a student researcher at Google DeepMind. His research develops generative models — latent-variable models\, energy-based models\, and language models — with applications in reasoning\, robotic planning\, drug discovery\, and representational models of spatial navigation. His work has appeared at NeurIPS\, ICML\, ICLR\, ICCV\, UAI\, and CoRL\, with spotlight presentations at NeurIPS 2024 and CoRL 2025. He has previously held research positions at Lambda\, Amazon\, and BioMap Research. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-from-random-walks-to-planning-ready-world-models-a-normative-model-of-place-cells/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
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DTSTART;TZID=America/Los_Angeles:20260521T090000
DTEND;TZID=America/Los_Angeles:20260521T143000
DTSTAMP:20260623T032526
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SUMMARY:Annual BE Student Project Showcase
DESCRIPTION:Join Baskin Engineering for our annual Student Project Showcase to celebrate the innovative work and accomplishments of undergraduate engineers in capstone courses and research pathways. The broader campus community\, parents\, and industry partners are invited to view the culmination of student work. \nThe day begins with oral presentations from nominated “best-in-class” teams and those working on industry-sponsored projects. Following this\, all students will participate in a comprehensive Poster Session featuring project outcomes with some teams including table-top demonstrations of functional hardware. \nEvent Details: \n\nDate: May 21\, 2026\nOral Presentations (Nominated/Industry Teams): 9:00 AM to 11:00 AM\, Engineering 2\, Room 180\nPoster Session (All Student Teams): 11:30 AM to 2:30 PM\, Engineering Courtyard
URL:https://events.ucsc.edu/event/be-student-project-showcase-2026/
CATEGORIES:Lectures & Presentations,Undergraduate
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260518T160000
DTEND;TZID=America/Los_Angeles:20260518T170000
DTSTAMP:20260623T032526
CREATED:20260408T220408Z
LAST-MODIFIED:20260408T220408Z
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SUMMARY:Statistics Seminar: Unifying Regression-Based and Design-Based Causal Inference in Time-Series Experiments and Crossover Experiments
DESCRIPTION:Presenter: Peng Ding\, Associate Professor\, UC Berkeley \nDescription: I will present some recent results on unifying regression-based and design-based causal inference in time-series experiments and crossover experiments. Part I: Time-series experiments\, also called switchback experiments or N-of-1 trials\, play increasingly important roles in modern applications in medical and industrial areas. Under the potential outcomes framework\, recent research has studied time-series experiments from the design-based perspective\, relying solely on the randomness in the design to drive the statistical inference. Focusing on simpler statistical methods\, we examine the design-based properties of regression- based methods for estimating treatment effects in time-series experiments. We demonstrate that the treatment effects of interest can be consistently estimated using ordinary least squares with an appropriately specified working model and transformed regressors. Additionally\, we show that asymptotically\, the heteroskedasticity and autocorrelation consistent variance estimators provide conservative estimates of the true\, design-based variances. This part is based on https://arxiv.org/pdf/2510.22864  \nPart II: Crossover designs randomly assign each unit to receive a sequence of treatments. By comparing outcomes within the same unit\, these designs can effectively eliminate between-unit variation and facilitate the identification of both instantaneous effects of current treatments and carryover effects from past treatments. They are widely used in traditional biomedical studies and are increasingly adopted in modern digital platforms. However\, standard analyses of crossover designs often rely on strong parametric models\, making inference vulnerable to model misspecification. We unify the analysis of crossover designs using least squares\, with restrictions on the coefficients and weights on the units. Based on the theory\, we recommend specifying the regression function\, weighting scheme\, and coefficient restrictions to assess identifiability\, construct efficient estimators\, and estimate variances in a unified manner. This part is based on https://arxiv.org/pdf/2511.09215 \nAbout the speaker: Peng Ding is an Associate Professor in the Department of Statistics at UC Berkeley. He obtained his Ph.D. from the Department of Statistics\, Harvard University in May 2015 and worked as a postdoctoral researcher in the Department of Epidemiology\, Harvard T. H. Chan School of Public Health until December 2015. Previously\, he received his B.S. in Mathematics\, B.A. in Economics\, and M.S. in Statistics from Peking University. \nThis seminar is hosted by Professor Allen Kei.
URL:https://events.ucsc.edu/event/statistics-seminar-unifying-regression-based-and-design-based-causal-inference/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260515T180000
DTEND;TZID=America/Los_Angeles:20260516T180000
DTSTAMP:20260623T032526
CREATED:20260428T221013Z
LAST-MODIFIED:20260508T194542Z
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SUMMARY:NemoClaw NVIDIA x ASUS Hackathon @ UC Santa Cruz
DESCRIPTION:Welcome to the premier physical AI hackathon on the West Coast. We are bringing together the top 200 AI\, infrastructure\, and hardware engineers to build autonomous\, agentic applications on the NVIDIA NemoClaw stack. \n​You aren’t just calling APIs\, you are building on enterprise-grade hardware. \n​The Tracks: \n\nThe Edge Track: 40 exclusive teams will be granted physical\, on-site access to an ASUS DGX Spark unit to build and deploy locally.\n​The Cloud Track: Teams will build the exact same stack utilizing fully sponsored cloud compute instances via Brev.dev.\n\n​The Arsenal & Prizes: Every team builds on a unified playing field. The top projects will take home heavy enterprise hardware\, including: \n\n​NVIDIA Jetson Orin Nanos\n​The ASUS Ascent (DGX Spark)\n​Jensen Huang signed NVIDIA hats & premium swag\n​High-value Brev.dev compute credits\n​Monitors\n​Internship Opportunities\n\n​The Details: \n\n​Who: Open to the top engineers at UC Santa Cruz and local feeder universities.\n​Food: Fully catered for 24 hours. Energy\, caffeine\, and meals are on us.\n​Special Guests*: Opening and closing ceremonies featuring VIP industry leaders (to be announced).\n​Title Sponsors: Nvidia\, ASUS\, Baskin School of Engineering\n\nRegister today!  \n​Space is strictly capped at 200 builders. Registration requires application approval. \n*May subject to change \n 
URL:https://events.ucsc.edu/event/nvidia-hackathon-2026/
LOCATION:Kresge College\, R-3 Suites\, Santa Cruz\, CA\, 95064
CATEGORIES:Competition,Meetings & Conferences
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END:VEVENT
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DTSTART;TZID=America/Los_Angeles:20260515T130000
DTEND;TZID=America/Los_Angeles:20260515T160000
DTSTAMP:20260623T032526
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LAST-MODIFIED:20260429T224549Z
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SUMMARY:STEM Culture Festival
DESCRIPTION:The STEM Culture Festival is returning to UC Santa Cruz on Friday\, May 15 from 1-4pm in the Baskin Engineering Courtyard. Join us! \nThis year\, we’re expanding with even more performances\, activities\, and creative ways to celebrate UCSC’s vibrant\, diverse\, and excellent STEM culture!  \nWhat to expect: \n\nCuban Dance Master Susana Arenas and her troupe of Orisha dancers led by Cuban Drum Master Toribio Garcia return for a rousing\, communal dance\n\nStudent performers: Los Mejicas and their traditional baile folklórico followed by an open dance lesson/performance by Slug N’ Boots\n\nSTEM-themed drag performances and spoken word poetry by student creatives \n\nAssociate Vice Chancellor for Student Success and Equity Dr. Ebonee Williams (Chemical Engineering\, University of Washington ‘04) will share an inspirational talk on “Bringing our whole selves to STEM!”\n\nEl Buen Taco and Falafel Santa Cruz will be serving delicious food\, completely FREE for all attendees who engage with the student orgs and their activities\n\nMore than just your standard student organization tabling: Games\, interactive demos\, culturally themed activities\, and opportunities to learn more about clubs from all over campus \n\nRaffle for gift cards to be awarded every hour from 1-4pm – must be present to win! \n\nThis event will take place in the Baskin Engineering Courtyard and will be open to all UCSC students\, staff\, and faculty. \nThe STEM Culture Festival celebrates and elevates the many backgrounds\, cultures\, and identities that intersect with our work as scientists\, engineers\, educators\, and members of the UCSC community. It is a rare opportunity when all of UCSC is invited to meet at the engineering school for a time of joy and togetherness. We enthusiastically invite you to attend and be in community with us – especially now in these tumultuous times of division and disunity.  \nThis event represents a collaboration between Baskin Engineering\, the Women’s Center\, the Lionel Cantú Queer Resource Center\, El Centro Latinx and Chicanx Resource Center\, the Asian American and Pacific Islander Resource Center\, the Physical and Biological Sciences Division\, and the Genomics Institute.
URL:https://events.ucsc.edu/event/stem-culture-festival-2026/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Concerts,Performances,Social Gathering,Undergraduate
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260511T160000
DTEND;TZID=America/Los_Angeles:20260511T170000
DTSTAMP:20260623T032526
CREATED:20260423T145740Z
LAST-MODIFIED:20260423T145740Z
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SUMMARY:Statistics Seminar: Learning under Constraints and Extremes: Methods and Applications in Energy Systems
DESCRIPTION:Presenter: Yu Zhang\, Associate Professor\, ECE Department of UC\, Santa Cruz \nDescription: Modern cyber-physical systems present statistical learning problems that deviate significantly from standard i.i.d. supervised settings. In particular\, two challenges frequently arise: (i) learning under hard structural constraints\, and (ii) learning under severe distributional imbalance and rare events. In this talk\, I present two case studies from energy systems that illustrate these challenges and motivate new learning paradigms. First\, I consider the problem of approximating the solution map of the AC optimal power flow (AC-OPF)\, a nonlinear and nonconvex optimization problem governing power grid operations. Rather than relying solely on labeled optimal solutions\, we develop both unsupervised and semi-supervised physics-informed learning frameworks that incorporate equality constraints directly into the training objective via augmented Lagrangian formulations and implicit gradient estimation. These approaches enable data-efficient learning while maintaining physical feasibility\, and can be interpreted as constrained function estimation where physical laws provide structural supervision. Second\, I discuss short-term power outage forecasting under extreme weather conditions\, where the data exhibit zero inflation\, heavy tails\, and strong temporal dependence. We propose a two-stage modeling framework that separates event occurrence and magnitude\, combining calibrated classification with Tweedie-based regression to better capture rare but high-impact events. Together\, these examples highlight a unifying theme: modern applications often require learning methods that effectively integrate domain structure while remaining robust to challenging data characteristics such as sparsity and extreme events. I conclude with a discussion of broader implications for scalable learning\, uncertainty handling\, and decision-making in complex systems. \nAbout the speaker: Dr. Yu Zhang is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California\, Santa Cruz. He received his Ph.D. in Electrical and Computer Engineering from the University of Minnesota\, followed by postdoctoral appointments at the University of California\, Berkeley and the Lawrence Berkeley National Laboratory. Dr. Zhang’s research advances the resilience\, efficiency\, and sustainability of modern electric power systems through innovations in AI-driven optimization\, machine learning\, and dynamic decision-making. His work develops physics-aware learning methods\, stochastic and robust optimization techniques\, and cyber-physical coordination frameworks to support reliable grid operations under uncertainty. Recent projects include learning-augmented outage forecasting\, planning for weather-driven grid hardening\, and integrating large flexible loads such as data centers into market and operational strategies. Dr. Zhang has been recognized with multiple awards\, including the 2025 Outstanding Young Investigator Award from the Energy Systems Division of the Institute of Industrial and Systems Engineers (IISE)\, the 2021 Early Career Best Paper Award from the INFORMS Energy\, Natural Resources\, and the Environment (ENRE) Section\, and the 2019 Hellman Fellowship. \nThis seminar is hosted by Professor Allen Kei. \n 
URL:https://events.ucsc.edu/event/statistics-seminar-learning-under-constraints-and-extremes/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260506T100000
DTEND;TZID=America/Los_Angeles:20260506T120000
DTSTAMP:20260623T032526
CREATED:20260422T165518Z
LAST-MODIFIED:20260422T165518Z
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SUMMARY:Wang\, Q. (STAT) - Modern Statistical Methods for Modeling Spatial and Temporal Processes
DESCRIPTION:Modern scientific studies increasingly rely on complex datasets exhibiting spatial and temporal dependence\, particularly in social\, environmental\, and climate applications. This dissertation develops statistical models and computational methods for analyzing such data\, with an emphasis on capturing dependence structures\, nonlinear dynamics\, and uncertainty quantification. \nA spatial deep learning framework is developed to extend classical geostatistical models by incorporating convolutional neural network architectures\, allowing for flexible modeling of complex and nonstationary spatial dependence The proposed approach preserves principled uncertainty quantification alongside improved predictive performance for large and heterogeneous spatial datasets. \nIn the temporal domain\, a Bayesian hierarchical echo state network model is introduced for count-valued time series\, providing a flexible alternative to traditional autoregressive approaches. By embedding reservoir computing within a hierarchical probabilistic framework\, the model accommodates nonlinear temporal dynamics while enabling coherent inference and uncertainty quantification\, which are typically absent in standard neural network approaches. \nAlongside these model-driven developments\, we conduct a data-driven analysis of Northern Hemisphere snow cover using weekly satellite-derived observations from 1972 to 2024. A spatio-temporal modeling framework is developed that combines a seasonal two-state Markov structure for temporal dynamics with a Besag–York–Mollié (BYM) formulation to capture spatial dependence\, allowing both trend and seasonal effects to vary across space. Covariates including temperature\, latitude\, and elevation are incorporated to explain observed patterns. The analysis reveals substantial spatial heterogeneity and pronounced seasonal structure\, including week-specific trends and a coherent wave-like pattern of snow cover changes across continents. \nTogether\, this thesis addresses key limitations of classical approaches to spatial and temporal data analysis\, which often rely on restrictive assumptions that limit their ability to capture complex dependence structures and nonlinear dynamics. By integrating modern machine learning techniques with statistical modeling and complementing these developments with data-driven scientific analysis\, this dissertation provides a flexible and principled framework for understanding complex spatio-temporal processes while maintaining uncertainty quantification. \n  \nEvent Host: Qi Wang\, Ph.D. Candidate\, Statistical Science  \nAdvisor: Paul Parker \nZoom: https://ucsc.zoom.us/j/97486222296?pwd=419R7C5I6gLbbB0eLqwMcSVQLTN7bA.1 \nPasscode: 766602
URL:https://events.ucsc.edu/event/wang-q-stat-modern-statistical-methods-for-modeling-spatial-and-temporal-processes/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260504T160000
DTEND;TZID=America/Los_Angeles:20260504T170000
DTSTAMP:20260623T032526
CREATED:20260312T222740Z
LAST-MODIFIED:20260429T174906Z
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SUMMARY:Statistics Seminar: Advancing Statistical Rigor in Single-Cell and Spatial Omics Using In Silico Control Data
DESCRIPTION:Presenter: Guan’ao Yan\, Assistant Professor\, Michigan State University \nDescription: Single-cell and spatial transcriptomics technologies now let us map cellular diversity and tissue organization at high resolution\, but the computational methods built to analyze these data are difficult to evaluate in a rigorous\, reproducible way. Two key barriers are the lack of realistic synthetic data with known ground truth and the ambiguity in how we define biologically meaningful spatial patterns. This talk will introduce two simulation frameworks—scReadSim for single-cell RNA-seq and ATAC-seq data\, and scIsoSim for isoform-level expression and splicing—that generate realistic sequencing reads while preserving user-specified truth. These tools enable fair\, controlled benchmarking of quantification and splicing methods across experimental protocols. The talk will also present a systematic review of 34 methods for detecting spatially variable genes (SVGs) in spatial transcriptomics data\, proposing a new categorization of SVGs and outlining how future benchmarks should be designed. Overall\, the goal is to improve statistical rigor\, interpretability\, and comparability in single-cell and spatial omics analysis. \nBio: Guan’ao Yan is an Assistant Professor of Computational Mathematics\, Science & Engineering at Michigan State University. He received his Ph.D. in Statistics from UCLA. His research focuses on statistical and computational methods for modern statistical genomics\, particularly single-cell and spatial omics\, with an emphasis on rigorous benchmarking\, interpretability\, and biomedical discovery. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-advancing-statistical-rigor-in-single-cell-and-spatial-omics-using-in-silico-control-data/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260427T160000
DTEND;TZID=America/Los_Angeles:20260427T170000
DTSTAMP:20260623T032526
CREATED:20260408T191555Z
LAST-MODIFIED:20260408T191555Z
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SUMMARY:Statistics Seminar: Active Learning for Fair and Stable Allocations
DESCRIPTION:Presenter: Riddhiman Bhattacharya\, Postdoc\, UCSC \nDescription: We propose an active learning approach for dynamic fair resource allocation problems. In contrast to prior work that assumes full feedback from all agents on their allocations\, we focus on scenarios where feedback is available only from a carefully select subset of agents at each epoch of the online resource allocation process. Despite this limitation\, our algorithms achieve sub-linear regret in the number of time-periods for multiple fairness metrics commonly used in resource allocation problems and stability constraints inherent to matching mechanisms. The core innovation of our approach lies in the adaptive identification of the most informative feedback through dueling upper and lower confidence bounds. This strategy enables efficient decision-making with limited feedback\, achieving favorable outcomes across various problem classes. \nAbout the speaker: I am Riddhiman Bhattacharya\, currently a postdoc at UCSC\, Statistics Department\, working with Justin (Sangwon Hyun). I have previously been a postdoc at Purdue and have obtained my PhD from the University of Minnesota in Statistics. I am interested in methodological development in statistics with varied applications including oceanography\, biology and economics. I am also interested in theoretical development of statistics particularly in the fields of Markov Chain Monte Carlo\, Optimization and Fast Sampling.
URL:https://events.ucsc.edu/event/statistics-seminar-active-learning-for-fair-and-stable-allocations/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260427T110000
DTEND;TZID=America/Los_Angeles:20260427T130000
DTSTAMP:20260623T032526
CREATED:20260420T225301Z
LAST-MODIFIED:20260423T210320Z
UID:10012119-1777287600-1777294800@events.ucsc.edu
SUMMARY:Quality First Coding Contest
DESCRIPTION:This is a programming contest\, but with a twist! Instead of scoring you based on your speed and solution accuracy\, we score you based on your programming quality and solution accuracy. This means that instead of looking at how fast you can program a solution\, we look at your number of compiles/runs instead.* The contestant that uses the least number of compiles/runs to produce passing code is the winner. Ties are broken by time. \nFood will be provided. QFCC 20260427 – Poster
URL:https://events.ucsc.edu/event/quality-first-coding-contest/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/Quality-First-Coding-Contest.png
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260424T130000
DTEND;TZID=America/Los_Angeles:20260424T150000
DTSTAMP:20260623T032526
CREATED:20260408T175733Z
LAST-MODIFIED:20260408T175733Z
UID:10012079-1777035600-1777042800@events.ucsc.edu
SUMMARY:Zheng\, Z. (STATS) - Semi-Supervised Statistical Learning for Oceanographic Data
DESCRIPTION:Oceanographic data\, generated by modern technologies that measure biological systems across time\, space\, and cell populations\, are often rich\, high-dimensional\, and highly heterogeneous. Such data provide valuable opportunities to study subcellular organization\, cellular heterogeneity\, and dynamic biological processes in marine environments. However\, because marine plankton systems remain relatively understudied and less well characterized than many model biological systems\, both data generation and labeling are particularly challenging. Limited domain knowledge and less mature laboratory protocols often produce noisy observations\, while reliable annotation requires substantial expert effort and is therefore difficult to obtain at scale.\nThis proposal develops statistical methodology for oceanographic data settings in which a small amount of expert-labeled data must be combined with a much larger collection of unlabeled or imperfectly processed data. A central goal is to incorporate limited scientific knowledge into statistical learning procedures to improve interpretability\, component identifiability\, and inferential reliability. In particular\, I develop semi-supervised statistical methods that explicitly quantify the information contributed by expert annotation.\nTo address this goal\, I study three related problems: semi-supervised functional clustering for subcellular spatial proteomics\, anchored semi-supervised mixture-of-experts models for flow cytometry\, and temporally structured latent-variable models that separate smooth trend and seasonal variation from scientific signals of interest. Together\, these projects aim to develop principled and interpretable methodology for partially labeled\, structured\, and high-dimensional oceanographic data\, with an emphasis on valid uncertainty quantification. \nEvent Host: Ziyue Zheng\, Ph.D. Student\, Statistical Science \nAdvisor: Sangwon Hyun \nZoom: https://ucsc.zoom.us/j/93229540289?pwd=8bsBOSBFmISlexmS4OWTmTZKp420u2.1
URL:https://events.ucsc.edu/event/zheng-z-stats-semi-supervised-statistical-learning-for-oceanographic-data/
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:20260423T180000
DTEND;TZID=America/Los_Angeles:20260423T193000
DTSTAMP:20260623T032526
CREATED:20260402T213440Z
LAST-MODIFIED:20260402T222539Z
UID:10012030-1776967200-1776972600@events.ucsc.edu
SUMMARY:Climate Week Tech Connect: Energy Solutions
DESCRIPTION:Join Baskin Engineering to explore the frontier of power engineering\, where the rapid rise of electrification and digital infrastructure is creating an unprecedented demand for next-generation talent and a critical opportunity for sustainability.  \nThis networking event bridges the gap between the classroom and the field\, offering students and faculty a front-row seat to the trends and high-impact career opportunities shaping our energy future. The event is part of Baskin Engineering Climate Week\, focused on raising awareness of climate issues and sustainability research and teaching. \nWhere: BE Courtyard\nWhen: Thursday\, April 23\, 6:00-7:30 p.m. \nWe hope to see you there!
URL:https://events.ucsc.edu/event/climate-week-tech-connect-energy-solutions/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Meetings & Conferences
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/01/BElogoWHITE.png
GEO:37.000369;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Jack Baskin Engineering Baskin Engineering 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Baskin Engineering 1156 High Street:geo:-122.0632371,37.000369
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260423T170000
DTEND;TZID=America/Los_Angeles:20260423T181500
DTSTAMP:20260623T032526
CREATED:20260402T211703Z
LAST-MODIFIED:20260402T212222Z
UID:10011935-1776963600-1776968100@events.ucsc.edu
SUMMARY:Careers in Climate Tech & Sustainability
DESCRIPTION:Ready to explore career pathways that matter? \nAttend our very special Careers in Climate Tech & Sustainability Panel—celebrating Baskin Engineering Climate Week—for an inside look at careers that will help build a sustainable future. Panelists representing different roles and organizations will share their career journeys and offer practical insights into working in climate tech. There will also be a catered networking reception that follows—don’t miss it! \nGet informed\, inspired\, and discover your path to a career in sustainability! \nThis event is part of Baskin Engineering’s Climate Tech Day featuring a community fair where students\, faculty\, climate and sustainability tech companies\, and community organizations will showcase their works through demonstrations\, poster presentations\, tabling\, and more.  \nWhere: E2-180\nWhen: Thursday\, April 23\, 5:00-6:15 p.m. \nRegister via Handshake. \nIf you have disability-related needs\, please contact the Career Success office at csuccess@ucsc.edu or (831) 459-4420 as soon as possible. \nYOU BELONG HERE\nPrograms and services are open to all\, consistent with state and federal law\, as well as the University of California’s nondiscrimination policies. Every initiative—whether a student service\, faculty program\, or community event—is designed to be accessible\, inclusive\, and respectful of all identities. To learn more\, please visit UC Nondiscrimination Statement or Nondiscrimination Policy for UC Publications.
URL:https://events.ucsc.edu/event/careers-in-climate-tech-sustainability/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/Javier-drone.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:20260423T153000
DTEND;TZID=America/Los_Angeles:20260423T173000
DTSTAMP:20260623T032526
CREATED:20260401T183254Z
LAST-MODIFIED:20260401T183254Z
UID:10011835-1776958200-1776965400@events.ucsc.edu
SUMMARY:Pawl\, E. (STAT) - Flexible and Scalable Mixtures of Experts for Oceanographic Flow Cytometry Data
DESCRIPTION:Flow cytometry is a valuable technique in microbial research used to measure the optical properties of single-celled organisms at high throughput. Oceanographers often deploy flow cytometers on research cruises in order to study the characteristics of phytosynthetic microbes—called phytoplankton—in regions and times with diverse environmental conditions. Because cytometers cannot distinguish between subpopulations\, researchers typically cluster observations into subpopulations and subsequently analyze cluster characteristics. This two-stage workflow is often manual\, difficult to reproduce\, and fails to account for uncertainty in cluster assignments when relating subpopulation behavior to environmental conditions. To address these shortcomings\, statistical mixture models are gradually being introduced as alternatives to manual flow cytometry data analysis. However\, existing models either cannot use covariates or make restrictive assumptions about the relationships between cluster characteristics and covariates. Additionally\, they are designed to analyze individual cruises and consequently characterize local\, rather than global\, patterns in phytoplankton behavior. We propose to develop computationally efficient mixtures of experts which account for the complex dependency structures in oceanographic flow cytometry data. In this framework\, cells are probabilistically assigned to latent subpopulations\, while cluster-specific regressions relate each subpopulation’s optical properties and relative abundance to environmental conditions. Our first project develops a mixture of random weight neural network experts which can estimate arbitrary nonlinear regressions at low computational cost\, without a priori specification of functional forms. In the second project\, we develop a variational Bayesian mixture of experts which automatically selects variables without requiring cross-validation for hyperparameter selection. The final project incorporates spatial and temporal dependence\, allowing joint inference on data collected from multiple research cruises conducted at different locations and times. \nEvent Host: Ethan Pawl\, Ph.D. Student\, Statistical Science \nAdvisors: Sangwon Hyun & Paul Parker \nZoom- https://ucsc.zoom.us/j/96353239941?pwd=a4PJ94EMSD6D0SJ75S3WYzrPbYsBtn.1 \nPasscode- 244463
URL:https://events.ucsc.edu/event/pawl-e-stat-flexible-and-scalable-mixtures-of-experts-for-oceanographic-flow-cytometry-data/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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GEO:37.0009723;-122.0632371
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260421T100000
DTEND;TZID=America/Los_Angeles:20260421T113000
DTSTAMP:20260623T032526
CREATED:20260401T234645Z
LAST-MODIFIED:20260401T234645Z
UID:10011845-1776765600-1776771000@events.ucsc.edu
SUMMARY:BE Climate & Cookies Student Pop-Up!
DESCRIPTION:Come get excited about Baskin Engineering Climate Week at our student pop-up! 🌎 \nClimate Week is a chance to explore how Baskin Engineering is addressing climate challenges through innovative research\, teaching\, and hands-on projects. \nDiscover the events happening throughout the week and find ways to get involved! \nSwing by for FREE BE swag\, coffee\, cookies\, Climate Week stickers\, and more—first come\, first served! \nWhere: BE Courtyard\nWhen: Tuesday\, April 21\, 10:00-11:30 a.m. \nWe hope to see you there!
URL:https://events.ucsc.edu/event/be-climate-week-pop-up-2026/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Social Gathering
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/BE-climate-week-pop-up.png
GEO:37.000369;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Jack Baskin Engineering Baskin Engineering 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Baskin Engineering 1156 High Street:geo:-122.0632371,37.000369
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260420T160000
DTEND;TZID=America/Los_Angeles:20260420T170000
DTSTAMP:20260623T032526
CREATED:20260331T181211Z
LAST-MODIFIED:20260331T181211Z
UID:10011822-1776700800-1776704400@events.ucsc.edu
SUMMARY:Statistics Seminar: Hierarchical Clustering with Confidence
DESCRIPTION:Presenter: Snigdha Panigrahi\, Associate Professor\, Department of Statistics\, University of Michigan \nDescription:Agglomerative hierarchical clustering is one of the most widely used approaches for exploring how observations in a dataset relate to each other. However\, its greedy nature makes it highly sensitive to small perturbations in the data\, often producing different clustering results and making it difficult to separate genuine structure from spurious patterns. In this talk\, I will show how randomizing hierarchical clustering can be useful not just for measuring stability but also for designing valid hypothesis testing procedures based on the clustering results. We propose a simple randomization scheme to construct valid p-values at each node of a hierarchical clustering dendrogram\, quantifying evidence against greedy merges while controlling the Type I error rate. Our method applies to any linkage without case-specific derivations\, is substantially more powerful than existing selective inference approaches\, and provides an estimate of the number of clusters with a probabilistic guarantee on overestimation. \nBio:Snigdha Panigrahi is an Associate Professor of Statistics at the University of Michigan\, where she also holds a courtesy appointment in the Department of Biostatistics. She received her PhD in Statistics from Stanford University in 2018 and has been a faculty member at Michigan since then. Her research focuses on converting purely predictive machine learning algorithms into principled inferential methods. She is an elected member of the International Statistical Institute\, and her work has been recognized with an NSF CAREER Award and the Bernoulli New Researcher’s Award. Her editorial service\, past and present\, includes Journal of Computational and Graphical Statistics\, Bernoulli\, and Journal of the Royal Statistical Society: Series B. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-hierarchical-clustering-with-confidence/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/03/ph.d.-presentation-graphic-option-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260415T173000
DTEND;TZID=America/Los_Angeles:20260415T203000
DTSTAMP:20260623T032526
CREATED:20260325T220453Z
LAST-MODIFIED:20260402T171331Z
UID:10011772-1776274200-1776285000@events.ucsc.edu
SUMMARY:Kraw Lecture: At the Forefront of AI: Innovation and Discovery
DESCRIPTION:Artificial intelligence is transforming how we understand and solve the world’s most complex challenges—while at the same time causing new challenges and concerns. We invite you to join us for a special UC Santa Cruz Kraw Lecture showcasing the faculty whose groundbreaking research in artificial intelligence is transforming science\, technology\, and society. From advances in autonomous systems and natural language processing to the development of sustainable and responsible AI\, this conversation will highlight the innovative work taking place across disciplines and the real-world impact it is poised to have. \nModerated by special guest Ahmad Thomas\, CEO of the Silicon Valley Leadership Group (SVLG)\, this dynamic discussion will bring together leading researchers to explore how these technologies are shaping the future—accelerating discovery\, addressing complex global challenges\, and opening new frontiers for collaboration. Gain insight into the ideas\, discoveries\, and collaborations shaping the next generation of artificial intelligence research and hear from the leaders advancing this work.\n \n\n\nIn-Person Reception: 5:30 p.m.\nLecture: 6:15 p.m.\n\nRegister Now
URL:https://events.ucsc.edu/event/kraw-lecture-at-the-forefront-of-ai-innovation-and-discovery/
LOCATION:The Quad Conference Center\, 2400 Sand Hill Rd\, Menlo Park\, CA\, 94025\, United States
CATEGORIES:Lectures & Presentations
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/03/2526-014E_Kraw_Lecture_banner-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260413T160000
DTEND;TZID=America/Los_Angeles:20260413T170000
DTSTAMP:20260623T032526
CREATED:20260312T223749Z
LAST-MODIFIED:20260312T223836Z
UID:10011318-1776096000-1776099600@events.ucsc.edu
SUMMARY:Statistics Seminar: Calibration Weighting-Style Diagnostics for Nonlinear Bayesian Hierarchical Models
DESCRIPTION:Presenter: Dr. Ryan Giordano\, UC Berkeley Statistics \nDescription: Multilevel Regression with Post-stratification (MrP) has become a workhorse method for estimating population quantities using non-probability surveys\, and is the primary alternative to traditional survey calibration weights\, e.g.~ as computed by raking. For simple linear regression models\, MrP methods admit “equivalent weights”\, allowing for direct comparisons between MrP and traditional calibration weights (Gelman 2006). In the present work\, we develop a more general framework for computing and interpreting “MrP local equivalent weights” (MrPlew)\, which admit direct comparison with calibration weights in terms of important diagnostic quantities such as covariate balance\, frequentist sampling variability\, and partial pooling. MrPlew is based on a local approximation\, which we show in theory and practice to be accurate and meaningful for the target diagnostics. Importantly\, MrPlew can be easily computed based on existing MCMC samples and conveniently wraps standard MrP software implementations. \nBio: Dr. Ryan Giordano is currently an assistant professor of statistics at UC Berkeley. Dr. Ryan Giordano earned a PhD in Statistics from UC Berkeley advised by Michael Jordan\, Tamara Broderick\, and Jon McAuliffe\, an MSc with distinction in econometrics and mathematical economics from the London School of Economics\, and undergraduate degrees in mathematics and engineering mechanics from the University of Illinois in Urbana-Champaign. Dr. Ryan Giordano has worked as a postdoctoral researcher at MIT under Tamara Broderick\, as an engineer for Google and HP\, and served for two years as an education volunteer in the US Peace Corps in Kazakhstan. Dr. Ryan Giordano’s research interests include machine learning\, variational inference\, Bayesian methods\, robustness quantification\, and what it even means to do statistics at all. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-calibration-weighting-style-diagnostics-for-nonlinear-bayesian-hierarchical-models/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/03/option-3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260409T100000
DTEND;TZID=America/Los_Angeles:20260409T120000
DTSTAMP:20260623T032526
CREATED:20260325T172250Z
LAST-MODIFIED:20260325T172250Z
UID:10011766-1775728800-1775736000@events.ucsc.edu
SUMMARY:Ticknor\, B. (STAT) - Clustering and Tractable Multivariate Inference for Extremes
DESCRIPTION:Modeling environmental extremes often involves large collections of spatial or temporal records where both clustering similar series and modeling dependence among extremes are challenging tasks. This Ph.D. proposal addresses several related problems in extreme value analysis. In particular\, we study how to cluster many time series based on their extremal behavior using strategies defined via univariate extremal models\, motivated by an application to 975 coastal wave-height records. We also investigate the development of scalable multivariate models for dependent extremes. A tractable construction based on a latent multivariate $t$ process with generalized extreme value margins is proposed\, together with a regularization strategy that encourages extremal dependence consistent with a max-stable limit while preserving likelihood-based inference. Together\, these efforts aim to provide practical tools for analyzing large collections of environmental extremes. \nEvent Host: Benjamin Ticknor\, Ph.D. Student\, Statistical Science \nAdvisor: Robert Lund \nZoom- https://ucsc.zoom.us/j/94347069554?pwd=21jbzUIlbopj2OFRySIHmBV11Ngoef.1 \nPasscode- 822764 \n 
URL:https://events.ucsc.edu/event/ticknor-b-stat-clustering-and-tractable-multivariate-inference-for-extremes/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/03/ph.d.-presentation-graphic-option-1.jpg
GEO:37.0009723;-122.0632371
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260406T160000
DTEND;TZID=America/Los_Angeles:20260406T170000
DTSTAMP:20260623T032526
CREATED:20260318T171956Z
LAST-MODIFIED:20260318T171956Z
UID:10011340-1775491200-1775494800@events.ucsc.edu
SUMMARY:Statistics Seminar: Some Recent Results on Transfer Learning
DESCRIPTION:Presenter: Oscar Hernan Madrid Padilla\, Assistant Professor\, University of California\, Los Angeles \nDescription: In the first part of the talk\, I will introduce TRansfer leArning via guideD horseshoE prioR (TRADER)\, a novel approach enabling multi-source transfer through pre-trained models in high-dimensional linear regression. TRADER shrinks target parameters towards a weighted average of source estimates\, accommodating sources with different scales. Theoretical investigation shows that TRADER achieves faster posterior contraction rates than standard continuous shrinkage priors when sources align well with the target while preventing negative transfer from heterogeneous sources. Extensive numerical studies and a real-data application demonstrate that TRADER improves estimation and inference accuracy over state-of-the-art transfer learning methods. In the second part of the talk\, I will discuss some ongoing work involving transfer learning in nonparametric regression with ReLU networks \nBio: Oscar Madrid Padilla is a tenure-track Assistant Professor in the Department of Statistics at the University of California\, Los Angeles. Previously\, from July 2017 to June 2019\, he was a Neyman Visiting Assistant Professor in the Department of Statistics at the University of California\, Berkeley. Before that\, he earned his Ph.D. in Statistics from The University of Texas at Austin in May 2017 under the supervision of Professor James Scott. He completed his undergraduate degree\, a B.S. in Mathematics\, at CIMAT in Mexico in April 2013. \nHosted by: Statistics Department 
URL:https://events.ucsc.edu/event/statistics-seminar-some-recent-results-on-transfer-learning/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/03/ph.d.-presentation-graphic-option-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T160000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260623T032526
CREATED:20260225T190019Z
LAST-MODIFIED:20260225T190019Z
UID:10009357-1773072000-1773075600@events.ucsc.edu
SUMMARY:Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data
DESCRIPTION:Presenter: Amanda Coston\, Assistant Professor\, University of California Berkeley \nDescription: Performance evaluation plays a central role in decisions about whether and how predictive algorithms should be deployed in high-stakes settings. Yet\, in many real-world domains\, evaluation is fundamentally difficult: the data available for assessment are often biased\, incomplete\, or noisy\, and the act of deploying a model can itself alter which outcomes are observed. As a result\, standard evaluation practices may substantially misrepresent both overall model performance and disparities across groups. In this talk\, we examine several common threats to valid evaluation—including measurement error\, selection bias\, and distribution shift—and present principled evaluation methods that enable valid performance assessment under these challenges when appropriate conditions are met. \nBio: From UC Berkeley website: Amanda Coston is an assistant professor of statistics at UC Berkeley. Her research addresses real-world data problems that challenge the validity\, reliability\, and equity of algorithmic decision support systems and data-driven policy-making. Her work draws on techniques from causal inference\, machine learning\, and nonparametric statistics. She earned her PhD in machine learning and public policy at Carnegie Mellon University and subsequently completed a postdoc at Microsoft Research on the Machine Learning and Statistics Team. She also holds a Bachelor of Science in Engineering from Princeton in computer science and a certificate in the Princeton School of Public and International Affairs. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-evaluating-predictive-algorithms-under-missing-data/2026-03-09/2/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/02/BElogoWHITE.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260309T080000
DTEND;TZID=America/Los_Angeles:20260309T170000
DTSTAMP:20260623T032526
CREATED:20260225T190019Z
LAST-MODIFIED:20260225T190019Z
UID:10009358-1773043200-1773075600@events.ucsc.edu
SUMMARY:Statistics Seminar: Evaluating Predictive Algorithms Under Missing Data
DESCRIPTION:Presenter: Amanda Coston\, Assistant Professor\, University of California Berkeley \nDescription: Performance evaluation plays a central role in decisions about whether and how predictive algorithms should be deployed in high-stakes settings. Yet\, in many real-world domains\, evaluation is fundamentally difficult: the data available for assessment are often biased\, incomplete\, or noisy\, and the act of deploying a model can itself alter which outcomes are observed. As a result\, standard evaluation practices may substantially misrepresent both overall model performance and disparities across groups. In this talk\, we examine several common threats to valid evaluation—including measurement error\, selection bias\, and distribution shift—and present principled evaluation methods that enable valid performance assessment under these challenges when appropriate conditions are met. \nBio: From UC Berkeley website: Amanda Coston is an assistant professor of statistics at UC Berkeley. Her research addresses real-world data problems that challenge the validity\, reliability\, and equity of algorithmic decision support systems and data-driven policy-making. Her work draws on techniques from causal inference\, machine learning\, and nonparametric statistics. She earned her PhD in machine learning and public policy at Carnegie Mellon University and subsequently completed a postdoc at Microsoft Research on the Machine Learning and Statistics Team. She also holds a Bachelor of Science in Engineering from Princeton in computer science and a certificate in the Princeton School of Public and International Affairs. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-evaluating-predictive-algorithms-under-missing-data/2026-03-09/1/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/02/BElogoWHITE.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260302T160000
DTEND;TZID=America/Los_Angeles:20260302T170000
DTSTAMP:20260623T032526
CREATED:20260202T195322Z
LAST-MODIFIED:20260202T195322Z
UID:10009146-1772467200-1772470800@events.ucsc.edu
SUMMARY:Statistics Seminar: Decoding Phytoplankton Responses to a Changing Ocean
DESCRIPTION:Presenter: Francois Ribalet\, Research Associate Professor\, School of Oceanography\, University of Washington \nDescription: François Ribalet will present new observational technologies and computational approaches for studying phytoplankton responses to ocean warming. Using SeaFlow\, a custom-built automated flow cytometer deployed on over 100 research cruises\, his team has collected nearly 850 billion cell measurements across global oceans. Matrix population models applied to these data reveal how temperature affects phytoplankton division rates and biomass. The research shows that Prochlorococcus\, the ocean’s most abundant photosynthetic organism\, experiences sharp declines in growth above 28°C. Climate projections incorporating these metabolic constraints predict a 40-60% decrease in Prochlorococcus production in tropical regions by 2100\, with Synechococcus partially compensating through a 20-40% increase. These shifts between dominant phytoplankton groups will likely disrupt ocean food webs and carbon cycling\, raising questions about whether tropical ecosystems can adapt to warming oceans. \n\n\n\n\n\n\n\n\n\nBio: François Ribalet is a research associate professor at the University of Washington studying phytoplankton and their role in ocean food webs and carbon cycling. He combines field observations with statistical models to understand how environmental changes affect the growth and community dynamics of these microscopic organisms. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-decoding-phytoplankton-responses-to-a-changing-ocean/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260226T120000
DTEND;TZID=America/Los_Angeles:20260226T140000
DTSTAMP:20260623T032526
CREATED:20260129T143555Z
LAST-MODIFIED:20260223T200828Z
UID:10009134-1772107200-1772114400@events.ucsc.edu
SUMMARY:BE Club Bash - Engineers Week
DESCRIPTION:Discover innovation at the Baskin Engineering Club Bash\, an event celebrating National Engineers Week! \nMark your calendars for Thursday\, February 26\, 12–2 PM in the BE Courtyard! The BE Club Bash brings together student organizations across all engineering disciplines to showcase their projects\, demos\, and interactive activities. \nStop by to: \n\nExplore hands-on booths and demonstrations from student organizations\nLearn about engineering opportunities on campus and how to get involved\nChat with student leaders and hear about their experiences\nEnter our 3D printer raffle (must be present to win!)\nGrab snacks and BE swag while you explore\n\nThis is a great way to connect with the engineering community\, discover new ideas\, and have fun. We hope to see you there! RSVP here.
URL:https://events.ucsc.edu/event/be-club-bash-engineers-week/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Social Gathering,Undergraduate
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260225T173000
DTEND;TZID=America/Los_Angeles:20260225T190000
DTSTAMP:20260623T032526
CREATED:20260130T054047Z
LAST-MODIFIED:20260209T232119Z
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SUMMARY:Exploring Research Pathways at Baskin Engineering
DESCRIPTION:Curious how being part of a research lab can supercharge your experience as a Baskin Engineer?   \nJoin us for this informative event to learn about opportunities to solve open-ended problems\, build deeper technical skills\, and learn how to think like an engineer. \nWe’ll kick things off with a quick overview of the kinds of research opportunities available to undergrads and how to get started\, then you’ll hear directly from students who’ve worked in research labs as undergraduates. They’ll share what they actually did day-to-day\, the skills they built (technical and professional)\, and how research shaped their confidence\, career goals\, and next steps. We’ll then have pizza and networking to end the evening. \nWhether you’re aiming for industry\, graduate school\, or just want hands-on experience that goes beyond coursework\, this panel will help you understand how undergraduate research can set you apart—academically\, professionally\, and personally! \n\nRegister via Handshake. \nYOU BELONG HERE\nPrograms and services are open to all\, consistent with state and federal law\, as well as the University of California’s nondiscrimination policies. Every initiative—whether a student service\, faculty program\, or community event—is designed to be accessible\, inclusive\, and respectful of all identities. To learn more\, please visit UC Nondiscrimination Statement or Nondiscrimination Policy for UC Publications.
URL:https://events.ucsc.edu/event/exploring-research-pathways-at-baskin-engineering/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260224T103000
DTEND;TZID=America/Los_Angeles:20260224T113000
DTSTAMP:20260623T032526
CREATED:20260129T145348Z
LAST-MODIFIED:20260209T232106Z
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SUMMARY:Transform Your Future Pop-Up (Cookies Included!)
DESCRIPTION:Join Baskin Engineering to celebrate National Engineers Week with a sweet stop at the Transform Your Future Pop-Up (Cookies Included!) 🍪☕ \nThis year’s Engineers Week theme\, Transform Your Future\, is a powerful reminder that engineering doesn’t just shape our world—it shapes our opportunities\, our communities\, and the futures we can imagine for ourselves. \nSwing by the BE Courtyard to grab cookies\, coffee\, and BE swag (first come\, first served!) and take a moment to celebrate how you are transforming your future. \n📅 Date: Tuesday\, February 24⏰ Time: 10:30 a.m.📍 Location: BE Courtyard \nWe hope to see you there!
URL:https://events.ucsc.edu/event/transform-your-future-pop-up-cookies-included/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Social Gathering,Undergraduate
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260223T160000
DTEND;TZID=America/Los_Angeles:20260223T170000
DTSTAMP:20260623T032526
CREATED:20260126T202042Z
LAST-MODIFIED:20260126T202042Z
UID:10009108-1771862400-1771866000@events.ucsc.edu
SUMMARY:Statistics Seminar: Rotated Mean-Field Variational Inference and Iterative Gaussianization
DESCRIPTION:Presenter: Sifan Liu\, Assistant Professor\, Department of Statistical Science\, Duke University \nDescription:Mean-field variational inference (MFVI) approximates a target distribution with a product distribution in the standard coordinate system\, offering a scalable approach to Bayesian inference but often severely underestimating uncertainty due to neglected dependence. We show that MFVI can be greatly improved when performed along carefully chosen principal component axes rather than the standard coordinates. The principal components are obtained from a cross-covariance matrix of the target’s score function and identify orthogonal directions that capture the dominant discrepancies between the target distribution and a Gaussian reference. Performing MFVI in a rotated system defines a rotation followed by a coordinatewise transformation that moves the target closer to Gaussian. Iterating this procedure yields a sequence of transformations that progressively Gaussianize the target. The resulting algorithm provides a computationally efficient construction of normalizing flows\, requiring only MFVI sub-problems and avoiding large-scale optimization. In posterior sampling tasks\, we demonstrate that the proposed method greatly outperforms standard MFVI while achieving accuracy comparable to normalizing flows at a much lower computational cost. \nBio: Sifan Liu is an Assistant Professor in the Department of Statistical Science at Duke University. She was previously a research scientist at the Flatiron Institute and received her Ph.D. in Statistics from Stanford University. Her research interests include sampling\, generative modeling\, and selective inference. \nHosted by: Statistics Department
URL:https://events.ucsc.edu/event/statistics-seminar-rotated-mean-field-variational-inference-and-iterative-gaussianization/
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260204T120000
DTEND;TZID=America/Los_Angeles:20260204T130000
DTSTAMP:20260623T032526
CREATED:20260128T170858Z
LAST-MODIFIED:20260128T170858Z
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SUMMARY:Statistics Seminar: Statistical Inference for Multi-Modality Data in the AI Era
DESCRIPTION:Presenter: Qi Xu\, Postdoctoral Researcher\, Department of Statistics & Data Science\, Carnegie Mellon University \nDescription: Multi-modality data are increasingly common across science medicine and technology\, such as imaging\, text\, sensors\, and genomics. These modalities are often high dimensional or unstructured and naturally exhibit blockwise (nonmonotone) missingness where different samples observe different subsets of modalities. Such missingness creates a major obstacle for statistical analyses since classical methods either discard large portions of data or rely on strong modeling assumptions. Recent advances in AI make it possible to generate or predict unobserved modalities from observed ones\, opening new opportunities for data integration. In this talk\, I will focus on statistical inference for blockwise-missing multi-modality data\, while rigorously incorporating modern AI tools. Rooted in semiparametric theory\, there is a long-term open problem that theoretically optimal estimating function under non-monotone missingness is computationally intractable\, even under the missing completely at random mechanism. I introduce a tractable approximation to the optimal estimating equation through a novel Restricted ANOVA hierarchY or RAY decomposition and its almost-eigen-operator property. This leads to a new class of estimators that leverage predictive or generative AI models to borrow information across datasets while remaining unbiased and asymptotically normal. Motivated by the property of the RAY estimator\, we extend the RAY estimator to a class of unbiased\, consistent\, and computationally tractable estimators. The most efficient estimator in this class is then derived\, named as Adaptive RAY estimator\, which optimally integrating all available data and prediction from AI. Simulation studies and a single cell multi-omics application demonstrate that the proposed framework enables stable and efficient inference for complex multi modality data in the AI era. This is a joint work with Lorenzo Testa\, Jing Lei and Kathryn Roeder\, and the paper is available on arXiv: https://arxiv.org/abs/2509.24158 \nBio: Qi Xu is a postdoctoral researcher in the Department of Statistics & Data Science at Carnegie Mellon University. His research interests lie broadly in statistics and machine learning\, especially in data integration and AI for statistics\, with their applications in genomics and mobile health. He received his Ph.D. from the Department of Statistics at University of California\, Irvine\, and the Master degree from University of Illinois Urbana Champaign\, and the Bachelor degree (with honors) from Tongji University. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/91740050783?pwd=joK9hfwvM7FZ48acaiow8OY4ZlBDXA.1
URL:https://events.ucsc.edu/event/statistics-seminar-statistical-inference-for-multi-modality-data-in-the-ai-era/
CATEGORIES:Lectures & Presentations,Seminars
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LOCATION:https://ucsc.zoom.us/j/91740050783?pwd=joK9hfwvM7FZ48acaiow8OY4ZlBDXA.1
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260202T120000
DTEND;TZID=America/Los_Angeles:20260202T130000
DTSTAMP:20260623T032526
CREATED:20260122T191932Z
LAST-MODIFIED:20260128T171007Z
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SUMMARY:Statistics Seminar: Mathematical Foundations for Machine Learning from a Nonlinear Time Series Perspective
DESCRIPTION:Presenter: Jiaqi Li\, William H. Kruskal Instructor\, University of Chicago \nDescription:Modern machine learning (ML) algorithms achieve remarkable empirical success\, yet providing rigorous statistical guarantees remains a major challenge\, particularly in distributional theory and online inference methods. In this talk\, we will introduce a novel framework to provide mathematical foundations for ML by bringing powerful tools in nonlinear time series. First\, we focus on the stochastic gradient descent (SGD) with constant learning rates. By interpreting the SGD sequence as a nonlinear AR(1) process\, we can establish the geometric moment contraction (GMC) for SGD regardless of initializations. By this GMC property\, we can derive refined asymptotic theory of SGD and its averaging variant\, including general moment convergence\, quenched central limit theorems\, quenched invariance principles\, and sharp Berry- Esseen bounds. Then\, we extend this theoretical framework to SGD with dropout regularization\, a widely used but theoretically underexplored technique in deep learning. By establishing GMC under explicit learning-rate and dimensional scaling regimes\, we obtain asymptotic normality and invariance principles for dropout SGD and its averaged version. These results enable online inference\, for which we introduce a fully recursive estimator of the long-run covariance matrix appearing in the limiting distributions. The proposed online confidence intervals with asymptotically correct coverage can be generalized to many other ML algorithms. Overall\, viewing online learning algorithms as nonlinear time series provides a powerful toolkit for deriving statistical guarantees in modern ML\, with implications for high-dimensional stochastic optimization and real-time uncertainty quantification. \nBio:Jiaqi Li is a William H. Kruskal Instructor in the Department of Statistics at the University of Chicago. She obtained her PhD in Statistics from Washington University in St. Louis in 2024. Her research focuses on developing theoretical guarantees and statistical inference methods for machine learning algorithms. She also works on time series data\, especially in the high- dimensional settings with complex temporal and cross-sectional dependency structures. She also\ncollaborates with neuroscientists on applications in fMRI and EEG data. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/96647674332?pwd=rCHfeGpKslaGS5iIPP5Jh29mQiMJID.1
URL:https://events.ucsc.edu/event/statistics-seminar-mathematical-foundations-for-machine-learning-from-a-nonlinear-time-series-perspective/
CATEGORIES:Lectures & Presentations,Seminars
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LOCATION:https://ucsc.zoom.us/j/96647674332?pwd=rCHfeGpKslaGS5iIPP5Jh29mQiMJID.1
END:VEVENT
END:VCALENDAR