BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Events - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Events
X-ORIGINAL-URL:https://events.ucsc.edu
X-WR-CALDESC:Events for Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20250309T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20251102T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20260308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20261101T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20270314T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20271107T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260413T160000
DTEND;TZID=America/Los_Angeles:20260413T170000
DTSTAMP:20260413T145134
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:20260415T173000
DTEND;TZID=America/Los_Angeles:20260415T203000
DTSTAMP:20260413T145134
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:20260420T160000
DTEND;TZID=America/Los_Angeles:20260420T170000
DTSTAMP:20260413T145134
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/
LOCATION:Jack Baskin Engineering Building\, 156
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:20260421T100000
DTEND;TZID=America/Los_Angeles:20260421T113000
DTSTAMP:20260413T145134
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:20260423T153000
DTEND;TZID=America/Los_Angeles:20260423T173000
DTSTAMP:20260413T145134
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
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/03/option-3.png
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260423T170000
DTEND;TZID=America/Los_Angeles:20260423T181500
DTSTAMP:20260413T145134
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:20260423T180000
DTEND;TZID=America/Los_Angeles:20260423T193000
DTSTAMP:20260413T145134
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:20260424T130000
DTEND;TZID=America/Los_Angeles:20260424T150000
DTSTAMP:20260413T145134
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
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/ph.d.-presentation-graphic-option-3.png
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260427T160000
DTEND;TZID=America/Los_Angeles:20260427T170000
DTSTAMP:20260413T145134
CREATED:20260408T191555Z
LAST-MODIFIED:20260408T191555Z
UID:10012080-1777305600-1777309200@events.ucsc.edu
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
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/04/BE-logomark_localist.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:20260504T160000
DTEND;TZID=America/Los_Angeles:20260504T170000
DTSTAMP:20260413T145134
CREATED:20260312T222740Z
LAST-MODIFIED:20260312T222740Z
UID:10011317-1777910400-1777914000@events.ucsc.edu
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/
CATEGORIES:Lectures & Presentations,Seminars
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/02/ph.d.-presentation-graphic-option-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260515T130000
DTEND;TZID=America/Los_Angeles:20260515T160000
DTSTAMP:20260413T145134
CREATED:20260306T005653Z
LAST-MODIFIED:20260310T233744Z
UID:10009405-1778850000-1778860800@events.ucsc.edu
SUMMARY:STEM Culture Festival
DESCRIPTION:Join us for a celebration of UCSC’s vibrant\, innovative\, and diverse STEM community featuring: \n\nMusical performances\nDance lessons\nSTEM-themed drag shows\nInspirational talks\nStudent organization tabling\nStudent-led games & activities\nFREE tacos\n\nThis is a rare opportunity for UCSC to come together at Baskin Engineering for an exuberant and colorful celebration – don’t miss out! \nThis event will be sponsored by Baskin Engineering\, the Women’s Center\, the Cantu Queer Center\, and the UC Santa Cruz 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
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/03/BE-STEM-Culture-Festival_Events-Calendar-scaled.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:20260518T160000
DTEND;TZID=America/Los_Angeles:20260518T170000
DTSTAMP:20260413T145134
CREATED:20260408T220408Z
LAST-MODIFIED:20260408T220408Z
UID:10012085-1779120000-1779123600@events.ucsc.edu
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
ATTACH;FMTTYPE=image/jpeg:https://events.ucsc.edu/wp-content/uploads/2026/04/unnamed-scaled.jpg
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:20260521T090000
DTEND;TZID=America/Los_Angeles:20260521T143000
DTSTAMP:20260413T145134
CREATED:20260326T204610Z
LAST-MODIFIED:20260326T204610Z
UID:10011802-1779354000-1779373800@events.ucsc.edu
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
ATTACH;FMTTYPE=image/png:https://events.ucsc.edu/wp-content/uploads/2026/03/BE-ug-project-showcase.png
END:VEVENT
END:VCALENDAR