Seunghan Lee

Seunghan Lee

Assistant Professor

Office: 260Q McCain Hall
P 662.325.9220
Google Scholar

Seunghan Lee received his B.S. and M.S. degrees in Industrial Engineering from Seoul National University in South Korea. After four years as a software & systems engineer in industry, he pursued and received his second M.S. degree in Operations Research from the Georgia Institute of Technology and a Ph.D. degree in Systems and Industrial Engineering from the University of Arizona. Prior to joining MSU, he served as an assistant professor of teaching in the Industrial and Systems Engineering department at the State University of New York at Buffalo.

His research interests are simulation methodologies and their intersections of optimization and stochastics. He focuses on developing socio-economic decision support systems and validating them under the uncertainties induced by dynamic environmental changes, interactions among agents, and/or adversary behaviors. Major applications include disaster management, homeland security, smart cities, and healthcare operations.

His publications have appeared in peer-reviewed journals such as Information Sciences, ACM Transactions on Modeling and Computer Simulation (TOMACS), IISE Transactions, Expert Systems with Applications, and Simulation Modeling Practice and Theory. He also won the best paper awards twice at the IISE Annual Conference in 2016 and 2018 and received the Outstanding Graduate Teaching Assistant Award from the University of Arizona in 2019.


  • Ph.D., Systems & Industrial Engineering, University of Arizona, Tucson, Arizona, 2019.
  • M.S., Operations Research, Georgia Institute of Technology, Atlanta, Georgia, 2015.
  • M.S., Industrial Engineering, Seoul National University, Seoul, South Korea, 2008.
  • B.S., Industrial Engineering, Seoul National University, Seoul, South Korea, 2006.

Research Interests

  • Simulation Modeling
    • Discrete-event Simulation (DES)
    • Agent-based Simulation (ABS)
    • System Dynamics (SD)
  • Simulation Optimization
    • Discrete Optimization via Simulation (DOvS)
    • Bayesian Optimization (BayesOpt)
  • Applied Probability and Statistics
    • Stochastic Processes
    • Statistical Computing


  • Disaster Mitigation and Relief Efforts
  • Homeland Security and Smart Cities
  • Surgical Training in Healthcare Operations
  • Social Networks