ISyE Seminar: Scalable Bayesian Optimization for High Dimensional Expensive Functions

Presentation by Dr. Matthias Poloczek
Senior Manager, Research Scientist
Bayesian Optimization Lead
Uber AI

Wednesday, Sept. 16, 2020

3:30-4:30 p.m. - Graduate seminar
4:30 p.m. - Reception

Zoom meeting link
Password: ISyE2020

About the Seminar

Bayesian optimization has recently emerged as a powerful method for the sample-efficient optimization of expensive black-box functions. These functions do not have a closed-form and are evaluated, for example, by running a complex simulation, a lab experiment, or solving a partial differential equation. Use cases arise in machine learning when optimizing a reinforcement learning policy; examples in engineering include the design of aerodynamic structures or searching for better materials. 

About the Speaker

Matthias Poloczek leads the Bayesian optimization team at Uber AI. His research interests lie at the intersection of machine learning and optimization. Recently, he has focused on enabling Bayesian optimization for "exotic" black-box problems that arise in aerospace engineering and materials science.