Joint DSC/CIS seminar series (Talk by Dr. Lulu Kang of UMass Amherst)
CCB242
Donghui Yan, Yuchou Chang, Joshua Carberry
508-999-8746
dyan@umassd.edu
Title: Building GP Surrogate Model with High-Dimensional Input
Abstract:
Gaussian process (GP) regression is a popular surrogate modeling tool for computer simulations in engineering and scientific domains. However, it often struggles with high computational costs and low prediction accuracy when the simulation involves too many input variables. In this talk, I will present two different approaches to build Gaussian process surrogate model for experiments with high dimensional input. I first introduce an optimal kernel learning approach to identify the active variables, thereby overcoming GP model limitations and enhancing system understanding. This method approximates the original GP model's covariance function through a convex combination of kernel functions, each utilizing low-dimensional subsets of input variables. The second approach is Bayesian bridge GP regression approach, in which we impose shrinkage penalty on the linear regression coefficients of the mean and correlation coefficients in the covariance function. This is equivalent to using certain proper informative priors on these parameters under Bayesian framework. Using Spherical Hamiltonian Monte Carlo, we can directly sample from the constrained posterior distribution without the restrictions on prior distribution as in Bayesian bridge regression.
Bio:
Lulu Kang is a Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. She earned her Ph.D. in Industrial Engineering and M.S. in Operations Research from the Georgia Institute of Technology. Her research interests lie at the intersection of statistics and computational science, focusing on the design of experiments, uncertainty quantification, and statistical machine learning. Kang became an elected Fellow of the American Statistical Association in 2026. She has received the ASA's SPAIG Award and the SPES Outstanding Service Award. She also serves as an Associate Editor for Technometrics and the SIAM/ASA Journal on Uncertainty Quantification.