SEMINAR – April 12, 2024

Abstract

This talk begins with an overview of Gaussian process (GP) surrogate modeling, and my favorite application: active learning for the (Bayesian) optimization of a blackbox function.  I shall then survey some important, recent methodological developments targeting specific situations that increasingly arise in practice: large simulation campaigns, noisy observations/stochastic simulation, nonstationary modeling, and the calibration of computer models to field data.  The presentation concludes with an in-depth description of a recent application: contour location for reliability in an airfoil simulation experiment using deep GPs.  Throughout, there will be reproducible visuals and demos supported by code, both run live and embedded in the slides.  These are biased toward my own work, in part because I understand that code best.  But along the way I shall also endeavor to provide an otherwise balanced discussion of myriad alternatives that can be found elsewhere in this fast-moving literature.

About the Speaker

Bobby is a Professor of Statistics at Virginia Tech, and a Fellow of the American Statistical Association (ASA).  He currently serves as the Editor-in-Chief at Technometrics, an ASA journal.  Recently he completed tours as Chair of the ASA's Uncertainty Quantification Interest Group, as President of the ASA's Section on Physical and Engineering Sciences, and as Treasurer for the International Society of Bayesian Analysis.   He works mainly on surrogate modeling, uncertainty quantification (UQ), Bayesian inference and statistical computing.

Event Organizer

David Kepplinger
Assistant Professor, Department of Statistics
College of Engineering and Computing
George Mason University

Nicholas Rios
Assistant Professor, Department of Statistics
College of Engineering and Computing
George Mason University