This is a past event.

ME-EM Graduate Seminar Speaker Series

proudly presents

Byung-Jun Yoon, PhD

Associate Professor, Electrical & Computer Engineering
Texas A&M University

Abstract

Various real-world problems in science and engineering often involve complex systems with immense uncertainties. In this talk, we show how recent advances in AI/ML can help us effectively navigate through the challenges that naturally arise when aiming at achieving operational goals based on complex uncertain systems, especially in data-poor domains. Specifically, we focus on techniques that enable optimal design under uncertainty and can accelerate novel scientific discoveries, including Bayesian optimal experimental design, optimal computational campaigns, and generative molecular design. To demonstrate the advantages and potentials of these approaches, we will consider examples in systems biology, drug discovery, and material design.

Bio

Dr. Byung-Jun Yoon received the BS degree from the Seoul National University and the MS and PhD degrees from the California Institute of Technology, all in Electrical Engineering. Since 2008, he has been with the Department of Electrical and Computer Engineering, Texas A&M University, where he is currently a Professor. Dr. Yoon holds a joint appointment at Brookhaven National Laboratory, where he is a Scientist in Computational Science Initiative. He received the NSF CAREER Award, the Best Paper Award at the 9th Asia Pacific Bioinformatics Conference and the 12th Annual MCBIOS Conference, and the SLATE Teaching Excellence Award from the Texas A&M. Dr. Yoon’s main research interests lie in Scientific AI/ML, optimal experimental design, and objective-based uncertainty quantification. He is actively working on the development of these methods and their application to various scientific domains, including computational biology and materials science.

Invited by: Jungyun Bae