Hyeong Suk Na (2019)

Assistant Professor

Industrial Engineering (IE)


B.S., M.E., Inha University
M.S., Texas A&M University
Ph.D., Pennsylvania State University



LIB 144 (campus map)
Research Expertise
Dr. Hyeong Suk Na's research interests include the large-scale stochastic optimization, stochastic process modeling, artificial intelligence and machine learning, geospatial intelligence, emergency management, traffic operations and control, network theory, social influence modeling, and agent-based simulation modeling. His research outcomes have been published in many leading journals and supported by various organizations including the National Science Foundation (NSF), the National Aeronautics and Space Administration (NASA), the South Dakota Board of Regents (SDBOR), the South Dakota Mines Foundation, and the National Research Foundation of Korea.
Brief Bio

Dr. Hyeong Suk Na is an Ervin Pietz Assistant Professor of Industrial Engineering and leads the OPTImization and MAchine Learning (OPTIMAL) Laboratory at South Dakota School of Mines & Technology. He is also affiliated with the NSF I/UCRC Center for Solid-State Electric Power Storage (CEPS) and the South Dakota Governor's Center for Electrochemical Energy Storage (CEES). He earned a Ph.D. in Industrial Engineering and Operations Research from the Pennsylvania State University in University Park, PA, an M.S. in Industrial Engineering from Texas A&M University in College Station, TX. He also received a B.S. and an M.Eng. in Industrial and Systems Engineering from Inha University. He is always looking for opportunities for research collaborations with academic and industry partners. Please email him at HyeongSuk.Na@sdsmt.edu if you have ideas or problems you would like to discuss with his research group. 


Dr. Hyeong Suk Na has a strong record of teaching various courses in Industrial Engineering and Operations Research such as Deterministic Models in Operations Research, Stochastic Models in Operations Research, Data Analytics for Engineering and Technology, Simulation Modeling for Decision Support, Probability Theory and Statistics, Forecasting in Business and Technology, Introduction to Engineering Analytics, Regression Analysis and Design of Experiments, Manufacturing System Design and Analysis, Production and Operations Management, Statistical Quality Control, and Six Sigma Methodology, to name a few.

Course Listing