Dear Colleagues,
The Section on Statistics in Defense and National Security is pleased to announce the next SDNS webinar on March 19th presented by Dr. Annie Sauer Booth, Assistant Professor in the Department of Statistics at NC State University.
For more information about the SDNS webinar series, please visit the ASA SDNS website or reach out to Elise Roberts (SDNS.AmStat@gmail.com).
Speaker: Dr. Annie Sauer Booth, Department of Statistics, NC State University
Title: Deep Gaussian Process Surrogates for Computer Experiments
Date: Tuesday, March 19th
Time: 2:00 – 3:30 PM Eastern / 11:00 AM – 12:30 PM Pacific
Registration (Zoom, free): SDNS Webinar Registration
Abstract: This talk provides an overview of Bayesian deep Gaussian processes (DGPs) as surrogate models for computer experiments. Computer experiments are invaluable tools for replacing and/or supplementing direct experimentation, particularly in settings where physical experimentation is restricted by ethical, time, financial, or practicality constraints. Such simulations are necessarily complex and require statistical "surrogate" models, trained on a limited budget of simulator evaluations, which can provide predictions and uncertainty quantification at untried input configurations. Gaussian process (GP) surrogates are the canonical choice, but they are limited by stationarity constraints. DGPs upgrade ordinary GPs through functional composition, in which intermediate GP layers warp the original inputs, providing flexibility to model non-stationary dynamics. In large data settings, we integrate Vecchia approximation for faster computation. In small data settings, we utilize strategic active learning/sequential designs with a variety of objectives including variance reduction, Bayesian optimization, and reliability analysis. We showcase implementation in the "deepgp" package for R on CRAN.
About the Speaker: Annie Booth (previously Annie Sauer) is an Assistant Professor in the Department of Statistics at NC State University. Her research focuses on surrogate modeling of computer experiments including uncertainty quantification, active learning, Bayesian optimization, and reliability analysis. She completed her Ph.D. in statistics at Virginia Tech last year, where she worked with advisors Bobby Gramacy and Dave Higdon on developing deep Gaussian processes as surrogate models.
This webinar is sponsored by the Statistics in Defense and National Security Section of the American Statistical Association. Interested in viewing past webinars or learning more about the SDNS webinar series? Please visit the SDNS website for more information: https://community.amstat.org/sdns/events/webinar-series
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Elise Roberts
Statistics in Defense and National Security
https://community.amstat.org/sdns/homeSDNS.AmStat@gmail.com------------------------------