Geunyeong Byeon

Geunyeong Byeon is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). She previously held postdoctoral positions at Argonne National Laboratory and Los Alamos National Laboratory. Her research focuses on large-scale optimization methodologies for decision-making in energy systems and interdependent infrastructure networks. Byeon teaches courses in operations research, optimization, and data-driven methods at both undergraduate and graduate levels.

Education

• Ph.D. in Industrial & Operations Engineering (2020), University of Michigan, Ann Arbor
• M.S. in Industrial Engineering (2016), Seoul National University, South Korea
• B.S. in Industrial Management Engineering (2014), Korea University, South Korea

Research Interests

Byeon specializes in developing optimization frameworks for complex systems, including energy grids and interdependent infrastructures. Her work emphasizes distributionally robust optimization, Benders decomposition, and federated algorithms, with applications to power systems, resilient distribution networks, and multi-agent decision-making under uncertainty. She bridges theoretical advancements with practical computational implementations.

Teaching Highlights

• Optimization I (2022-2024)
• Computing for Data-Driven Optimization (2021-2025)
• Probability and Statistics for Engineering (2021)

Awards

• MICDE Fellowship (University of Michigan, 2018)
• Rising Stars in Computational & Data Sciences Nominee (2020)
• Richard & Eleanor Towner Prize for Outstanding Ph.D. Research (Honorable Mention, 2019)

Professional Service

Byeon has reviewed for prestigious journals including INFORMS Journal on Computing, Management Science, and IEEE Transactions on Power Systems, and conferences like the Power Systems Computation Conference.