LANS Seminar

Seminar Title: Objective-Driven Optimal Experimental Design: Quantifying the Uncertainties that Matter and Reducing Them Efficiently & Effectively

Speaker: Dr. Byung-Jun Yoon, Professor, Department of Electrical and Computer Engineering, Texas A&M University, Joint appointment at Brookhaven National Laboratory, Scientist in Computational Science Initiative, Applied Mathematics Group

Date/Time: March 13, 2024/ 10:30 AM-1:30 AM
Location: See Meeting URL on the cels-seminars website which will require an Argonne login.

Description: Modeling complex real-world systems involve immense uncertainties due to a wide variety of factors, including inherent stochasticity of the system, limited availability of data for modeling the system, presence of unobservable hidden variables, and measurement noise. In practice, these uncertainties may not be completely eliminated and difficult to reduce, requiring one to focus on the uncertainties that actually matter for achieving the modeling goal(s). In this talk, we present how one may quantify the model uncertainty in an objective-driven manner and develop optimal experimental design (OED) techniques that can reduce the uncertainty that critically affects the operational goal(s) of the model. Furthermore, we discuss how one may leverage machine learning (ML) approaches to accelerate uncertainty quantification (UQ), and ultimately, OED. 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 B.S. degree from the Seoul National University and the M.S. and Ph.D. 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, Applied Mathematics Group. 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 University. 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.