JisunLee
- Analytics and Probabilistic Modeling
Convexification of mixed-integer convex optimization, tractable distributed MIP algorithms, combined frameworks of optimization and machine learning, and optimization under uncertainty
About
Jisun Lee is an Assistant Professor in the Operations Research & Industrial Engineering program at the University of Texas at Austin. Prior to joining UT Austin, she was a postdoctoral fellow at the Georgia Tech H. Milton Stewart School of Industrial & Systems Engineering. She received her PhD in Operations Research from the Department of Industrial Engineering & Operations Research at the University of California, Berkeley, and her MS and BS from the Department of Industrial Engineering at Seoul National University.
Her research goals center on developing strong formulations and efficient solution approaches for mixed-integer optimization problems, particularly by exploiting special problem structures. She seeks to address real-world challenges from an optimization perspective, with a focus on exact methods for mixed-integer programs in complicated systems, large-scale settings, and problems under uncertainty. Additionally, she is dedicated to exploring mixed-integer programming approaches across diverse areas, including convexification, distributed IP algorithms, mitigating computational burden and uncertainty, and building integrated frameworks for optimization and machine learning.
Educational Qualifications
- Ph.D., Industrial Engineering & Operations Research, University of California, Berkeley, 2025
- M.S., Industrial Engineering, Seoul National University, 2019
- B.S., Industrial Engineering, Seoul National University, 2017