MATH Seminar

Title: Scalable Bayesian optimal experimental design for efficient data acquisition
Seminar: CODES@Emory
Speaker: Peng Chen of Georgia Tech
Contact: Matthias Chung,
Date: 2022-11-10 at 10:00AM
Venue: MSC W301
Download Flyer
Bayesian optimal experimental design (OED) is a principled framework for maximizing information gained from limited data in Bayesian inverse problems. Unfortunately, conventional methods for OED are prohibitive when applied to expensive models with high-dimensional parameters. In this talk, I will present fast and scalable computational methods for large-scale Bayesian OED with infinite-dimensional parameters, including data-informed low-rank approximation, efficient offline-online decomposition, projected neural network approximation, and a new swapping greedy algorithm for combinatorial optimization.

See All Seminars