Byung-Jun Yoon

Brookhaven National Laboratory

Computational Science Initiative
Bldg. 725
P.O. Box 5000
Upton, NY 11973-5000

byoon@bnl.gov

Expertise | Education | Appointments | Publications | Awards

Expertise

• Optimal experimental design (OED)
• Objective-based uncertainty quantification
• Scientific AI/ML
• Computational network biology & bioinformatics

Education

• Ph.D., California Institute of Technology, Electrical Engineering, 2007.
• M.S., California Institute of Technology, Electrical Engineering, 2002.
• B.S., Seoul National University, Electrical Engineering, 1998.

Professional Appointments

• 2018 - present: Scientist, Computational Science Initiative, Brookhaven National Laboratory (by Joint Appointment)
• 2023 - present: Professor, Department of Electrical and Computer Engineering, Texas A&M University
• 2014 - 2023: Associate Professor, Department of Electrical and Computer Engineering, Texas A&M University
• 2008 - 2014: Assistant Professor, Department of Electrical and Computer Engineering, Texas A&M University

Selected Publications

• Woo H-M, Qian X, Tan L, et al (2023) Optimal decision-making in high-throughput virtual screening pipelines. Patterns 4:100875. https://doi.org/10.1016/j.patter.2023.100875
• Qian X, Yoon B-J, Arróyave R, et al (2023) Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery. Patterns 4:100863. https://doi.org/10.1016/j.patter.2023.100863
• Alexander FJ, Lin M, Qian X, Yoon B-J (2023) Accelerating scientific discoveries through data-driven innovations. Patterns 4:100876. https://doi.org/10.1016/j.patter.2023.100876
• Pouchard L, Reyes KG, Alexander FJ, Yoon B-J (2023) A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows. Digital Discovery 2:1251–1258. https://doi.org/10.1039/d3dd00094j
• Chen Q, Chen X, Woo H-M, Yoon B-J (2023) Neural message-passing for objective-based uncertainty quantification and optimal experimental design. Engineering Applications of Artificial Intelligence 123:106171. https://doi.org/10.1016/j.engappai.2023.106171
• Woo H-M, Allam O, Chen J, et al (2023) Optimal high-throughput virtual screening pipeline for efficient selection of redox-active organic materials. iScience 26:105735. https://doi.org/10.1016/j.isci.2022.105735
• Maddouri O, Qian X, Alexander FJ, et al (2022) Robust importance sampling for error estimation in the context of optimal Bayesian transfer learning. Patterns 3:100428. https://doi.org/10.1016/j.patter.2021.100428
• Maddouri O, Qian X, Yoon B-J (2021) Deep graph representations embed network information for robust disease marker identification. Bioinformatics 38:1075–1086. https://doi.org/10.1093/bioinformatics/btab772
• Woo H-M, Hong Y, Kwon B, Yoon B-J (2021) Accelerating Optimal Experimental Design for Robust Synchronization of Uncertain Kuramoto Oscillator Model Using Machine Learning. IEEE Transactions on Signal Processing 69:6473–6487. https://doi.org/10.1109/tsp.2021.3130967
• Yoon B-J, Qian X, Dougherty ER (2021) Quantifying the Multi-Objective Cost of Uncertainty. IEEE Access 9:80351–80359. doi: 10.1109/access.2021.3085486
• Hong Y, Kwon B, Yoon B-J (2021) Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations. IEEE Access 9:53804–53810. doi: 10.1109/access.2021.3071038
• Niu P, Soto MJ, Yoon B-J, et al (2021) TRIMER: Transcription Regulation Integrated with Metabolic Regulation. iScience 24:103218. https://doi.org/10.1016/j.isci.2021.103218
• Zhao G, Qian X, Yoon B-J, Alexander FJ, Dougherty ER (2020) Model-Based Robust Filtering and Experimental Design for Stochastic Differential Equation Systems. IEEE Transactions on Signal Processing 68:3849–3859. doi: 10.1109/tsp.2020.3001384
• Dehghannasiri R, Yoon B-J, Dougherty ER (2015) Efficient experimental design for uncertainty reduction in gene regulatory networks. BMC Bioinformatics. doi: 10.1186/1471-2105-16-s13-s2
• Dehghannasiri R, Byung-Jun Yoon, Dougherty ER (2015) Optimal Experimental Design for Gene Regulatory Networks in the Presence of Uncertainty. IEEE/ACM Transactions on Computational Biology and Bioinformatics 12:938–950. doi: 10.1109/tcbb.2014.2377733
• Yoon B-J, Qian X, Dougherty ER (2013) Quantifying the Objective Cost of Uncertainty in Complex Dynamical Systems. IEEE Transactions on Signal Processing 61:2256–2266. doi: 10.1109/tsp.2013.2251336
• Yoon B-J, Qian X, Sahraeian SME (2012) Comparative Analysis of Biological Networks: Hidden Markov model and Markov chain-based approach. IEEE Signal Processing Magazine 29:22–34. doi: 10.1109/msp.2011.942819

Awards & Recognition

• 2015: Best Paper Award, 12th Annual MCBIOS Conference.
• 2012: Faculty Early Career Development (CAREER) Award. National Science Foundation (NSF), USA.
• 2011: Best Paper Award, 9th Asia Pacific Bioinformatics Conference (APBC).
• 2009: Student Led Award for Teaching Excellence (SLATE), Texas A&M University System, College Station, TX, USA.
• 2004: Microsoft Research Ph.D. Fellowship, Microsoft Research, Redmond, WA, USA.
• 2001: Killgore Fellowship, California Institute of Technology, Pasadena, CA, USA.
• 2001: Doctoral Scholarship, Korea Foundation for Advanced Studies (KFAS), Seoul, Korea