Byung-Jun Yoon

Associate Professor, Texas A&M University / Scientist, Computational Science Initiative (CSI), Brookhaven National Laboratory

Lecture Information:
  • October 28, 2022
  • 2:00 PM
  • Zoom (Must register in advance)

Speaker 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 an Associate 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.


Accelerating Scientific Discoveries Through Optimal Experimental Design and Machine Learning Real-world scientific problems involve complex systems with immense uncertainties. In this talk, we discuss how we can accelerate scientific discoveries involving such complex systems by effectively navigating through the challenges that arise from the complexities of the systems, the uncertainties therein, and the scarcity of the data available for accurate system identification/modeling. Especially, we will present a Bayesian framework for modeling and quantifying uncertainties in complex systems, based on which we will introduce techniques for optimal learning and decision making in the presence of uncertainty. Furthermore, we will show how the proposed framework enables and takes advantage of the latest advances in machine learning to accelerate scientific discoveries. We will demonstrate the advantages and potentials of these approaches in systems biology, drug discovery, and material design.

This event requires advance registration to receive Zoom link. Please register here.