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DTSTART;TZID=America/Los_Angeles:20260928T160000
DTEND;TZID=America/Los_Angeles:20260928T170000
DTSTAMP:20260915T205202Z
CREATED:20260915T205202Z
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SUMMARY:Statistics Seminar: Vecchia Approximated Bayesian Heteroskedastic Gaussian Processes
DESCRIPTION:Presenter: Parul Patil\, Postdoctoral Researcher\, UC Santa Cruz \nDescription: Many computer simulations are stochastic and exhibit input dependent noise. In such situations\, heteroskedastic Gaussian processes (hetGPs) make ideal surrogates as they estimate a latent\, non-constant variance. However\, existing hetGP implementations are unable to deal with large simulation campaigns and use point-estimates for all unknown quantities\, including latent variances. This limits applicability to small experiments and undercuts uncertainty. We propose a Bayesian hetGP using elliptical slice sampling (ESS) for posterior variance integration\, and the Vecchia approximation to circumvent computational bottlenecks. We show good performance for our upgraded hetGP capability\, compared to alternatives\, on a benchmark example and a motivating corpus of more than 9-million lake temperature simulations. An open source implementation is provided as bhetGP on CRAN. \nAbout the speaker: Parul Patil is currently working as a post-doctoral researcher with Dr Paul Parker and Dr Sangwon Hyun at University of California\, Santa Cruz. She completed her PhD in May 2026 from Virginia Tech. She had previously completed B.Sc. and M.Sc. in Statistics in India. She works with large-scale data\, using statistical tools to model complex processes\, quantify uncertainty\, and uncover patterns. Her work lies at the intersection of statistics and machine learning with ecological applications\, focusing on scalable methods for analyzing high-dimensional and spatially structured data. \nThis seminar is hosted by Professor Yunyi Shen.
URL:https://events.ucsc.edu/event/statistics-seminar-vecchia-approximated-bayesian-heteroskedastic-gaussian-processes/
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:20261019T160000
DTEND;TZID=America/Los_Angeles:20261019T170000
DTSTAMP:20260921T164141Z
CREATED:20260921T164141Z
LAST-MODIFIED:20260921T164141Z
UID:10017107-1792425600-1792429200@events.ucsc.edu
SUMMARY:Statistics Seminar: Advancing Science on the Sea and Stars with Modern Statistical Techniques
DESCRIPTION:Presenter: J. Xavier Prochaska\, Professor\, UC Santa Cruz \nDescription: I will present two current and challenging research problems from the domains of astrophysics and oceanography which are leveraging standard Bayesian methods. After describing the datasets\, scientific goals\, and currently methodology\, I will welcome a discussion on ways to improve\, advance\, and/or replace the current approaches. Ideally within the realm of tradiational/modern statistics\, but I will welcome soluation with machine learning/AI approaches. I will then conclude by describing forthcoming\, large datasets that could leverage expertise from members of the Department of Statistics. For those who wish a preview of the data and current techniques\, I will circulate a README a day or two in advance of the seminar. \nAbout the speaker: PhD Physics — UC San Diego; Professor Astronomy & Astrophysics and Ocean Sciences — UC Santa Cruz \n\n\n\n\n\nThis seminar is hosted by Professor Yunyi Shen.
URL:https://events.ucsc.edu/event/statistics-seminar-advancing-science-on-the-sea-and-stars-with-modern-statistical-techniques/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
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DTSTART;TZID=America/Los_Angeles:20261102T160000
DTEND;TZID=America/Los_Angeles:20261102T170000
DTSTAMP:20260921T170456Z
CREATED:20260921T170456Z
LAST-MODIFIED:20260921T170456Z
UID:10017108-1793635200-1793638800@events.ucsc.edu
SUMMARY:AM Seminar: Sliced Optimal Transport: From Acceleration to Bayesian Modeling
DESCRIPTION:Presenter: Enter the speaker’s name\, title\, and institution at the top of the event \nDescription: Optimal transport (OT) provides a principled way to match\, compare\, and interpolate distributions\, but its computational cost limits its use at realistic scales. I will present our recent work on reducing that cost and on using OT for statistical modeling and inference. In the first part of the talk\, I will briefly introduce optimal transport and sliced optimal transport (SOT). I will then present sliced-regularized OT\, which uses a cheap sliced plan as an informative prior for approximating the full plan\, yielding an efficient approximate OT solver. In the second part\, I will show how SOT enables new applications in statistical inference. In particular\, I will introduce distributional determinantal point processes\, which use SOT geometry to place repulsive priors on distributions\, giving a Bayesian model for clustering distributional data. Applications include image processing and the clustering of single-cell and epilepsy data. \nAbout the speaker: Dr. Khai Nguyen is an Assistant Professor in the Department of Statistics and Data Science at Texas A&M University. He received his Ph.D. in Statistics from the University of Texas at Austin and his B.Sc. in Computer Science from Hanoi University of Science and Technology. His research develops computational optimal transport\, particularly sliced methods that make large-scale transport tractable. He brings these tools to problems in machine learning and geometric data processing\, as well as to Bayesian approximate inference and nonparametric models for distributional data. \nThis seminar is hosted by Professor Yunyi Shen.
URL:https://events.ucsc.edu/event/am-seminar-sliced-optimal-transport-from-acceleration-to-bayesian-modeling/
LOCATION:Jack Baskin Engineering\, Baskin Engineering 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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