Center for Advanced Intelligence Project

Title

Practical Issues in Bayesian Optimization

Abstract

Bayesian optimization is a valuable tool to efficiently search for the
optimum of a noisy, black-box function. This talk will introduce the
topic, using Gaussian processes to power the statistical modeling powering
the sequential decision making process of optimization. Then, variations
on the standard formulation will be discussed to handle complications
arising in various applications. Examples from machine learning and
materials sciences will be presented.

Bio

Michael McCourt is a member of the research engineering team at SigOpt, with
interests in kernel-based approximation theory, Bayesian optimization, spatial statistics,
and matrix computations. At SigOpt, he applies his expertise in leading the development
of an enterprise-grade Bayesian optimization platform and facilitating collaborations with
academic partners. Prior to joining SigOpt, he spent time in the math and computer
science division at Argonne National Laboratory and was a visiting assistant professor at
the University of Colorado-Denver and Illinois Institute of Technology. Michael holds a
Ph.D. in applied math from Cornell.