Coca-Cola Foundation Early Career Professor and
Associate Professor
Education
- Ph.D. Industrial Engineering and Management Sciences (2017), Northwestern University
- M.S. Industrial & Systems Engineering (2012), Korea Advanced Institute of Science and Technology (KAIST)
- B.S. Industrial & Systems Engineering (2010), Korea Advanced Institute of Science and Technology (KAIST)
Expertise
- Simulation
- Simulation Optimization
- Applied Probability
- Applied Statistics
About
Dr. Eunhye Song is a Coca-Cola Foundation Early Career Professor and Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Before joining ISyE she was a Harold and Inge Marcus Early Career Assistant Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at Penn State University. Dr. Song is the recipient of the NSF CAREER Award and Peter Welch INFORMS Simulation Society Early Career Award.
Research
Dr. Song's research focuses on building and analyzing stochastic simulators for decision making. Her research has three main threads: (1) uncertainty quantification of a data-driven simulation model; (2) simulation optimization under model risk; and (3) simulation validation and calibration. The first two threads focus on quantifying the discrepancy between a data-driven simulation model and the target system, and finding the robust optimum for the system despite the discrepancy. Furthermore, Dr. Song studies how to optimally collect data from multiple sources so that the optimum for the system can be identified as quickly as possible. In the last thread of research, she tackles new challenges that emerge in the simulation Digital Twin technologies where the target system generates high-dimensional, nonstationary data and a fast and frequent synchronization is required. Dr. Song has several industry collaboration experiences with companies including Georgia Power, General Motors, Simio LLC. and more.
Teaching
Dr. Song is passionate about teaching simulation to the next-generation industrial engineers and operations researchers. She teaches how to build and make decisions with a simulation model at both undergraduate and graduate levels. Her classes combine both statistical theory of simulation and practical implementation of it. Especially at the undergraduate level, her class offers hands-on experience in simulation applications in industry through the extensive course projects that are built based on her industry projects.
Dr. Song enjoys advising her PhD students and postdoctoral researchers on research and career development as well. Her recent graduates found positions in academia as well as at Uber, Department of Defense, General Motors, and more.
Awards and Honors
- INFORMS Simulation Society Peter Welch Early Career Award
- National Science Foundation CAREER Award
Representative Publications
- Eunhye Song and Barry L. Nelson (2015) “Quickly Assessing Contributions to Input Uncertainty”, IIE Transactions 47(9) 893–909.
- Yujing Lin, Eunhye Song, and Barry L. Nelson (2015) “Single-Experiment Input Uncertainty”, Journal of Simulation 9, 249–259.
- Eunhye Song, Barry L. Nelson and Jeremy Staum (2016) “Shapley Effects for Global Sensitivity Analysis: Theory and Computation”, SIAM/ASA Journal on Uncertainty Quantification 4(1) 1060–1083.
- Eunhye Song and Barry L. Nelson (2017) “Input Model Risk”, edited by Tolk, A., Fowler, J., Shao, G., and Yücesan, E. Advances in Modeling and Simulation: Seminal Research from 50 Years of Winter Simulation Conferences (pp. 63-80), Springer, NY.
- Peter Salemi, Eunhye Song, Barry L. Nelson, and Jeremy Staum (2019) “Gaussian Markov Random Fields for Discrete Optimization via Simulation: Framework and Algorithms”, Operations Research 67(1) 250–266.
- Eunhye Song† and Barry L. Nelson (2019) “Input-Output Uncertainty Comparisons for Optimization via Simulation”, Operations Research 67(2) 562–576.
- Eunhye Song, Peiling Wu-Smith, and Barry L. Nelson. (2020) “Uncertainty Quantification in Vehicle Content Optimization for General Motors”, INFORMS Journal on Applied Analytics 50(4) 225–238.
- Mark Semelhago, Barry L. Nelson, Eunhye Song and Andreas W¨achter (2021) “Rapid Discrete Optimization via Simulation with Gaussian Markov Random Fields”, INFORMS Journal on Computing 33(3) 915–930.
- Michael Hoffman, Eunhye Song, Michael Brundage, and Soundar Kumara (2022) “Online Improvement of Condition-based Maintenance Policy via Monte Carlo Tree Search”, IEEE Transactions on Automation Science and Engineering, 19(3) 2540–2551.
- Michael Hoffman, Eunhye Song, Michael Brundage, and Soundar Kumara (2022) “Online Maintenance Prioritization via Monte Carlo Tree Search and Case-based Reasoning”, ASME Journal of Computing and Information Science in Engineering, 22(4) 041005.
- Russell R. Barton, Henry Lam, and Eunhye Song (2022) “Input Uncertainty in Stochastic Simulation”, edited by Salhi, S., Boylan, J. The Palgrave Handbook of Operations Research (pp. 573-620). Palgrave Macmillan, Cham.
- Harun Avci, Barry Nelson, Eunhye Song and Andreas W¨achter (2023), “Using Cache or Credit for Parallel Ranking and Selection”, in ACM Transactions on Modeling and Computer Simulation, 33(4) 12.
- Eunhye Song, Henry Lam, and Russell R. Barton (2024), “A Shrinkage Approach to Improve Direct Bootstrap Resampling under Input Uncertainty”, in INFORMS Journal on Computing, 36(4) 1023–1039.
- Linyun He, Uday V. Shanbhag and Eunhye Song† (2024) “Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data”, in ACM Transactions on Modeling and Computer Simulation, 34(2) 6.
- Xinru Li and Eunhye Song† (2024), “Projected Gaussian Markov Improvement Algorithm for High-dimensional Discrete Optimization via Simulation”, in ACM Transactions on Modeling and Computer Simulation, 34(3) 14.
- Ben M. Feng and Eunhye Song† (2025), “Efficient Nested Simulation Experiment Design via Likelihood Ratio Method” (won Honorable Mention at 2020 INFORMS Junior Faculty Interest Group Paper Competition), in INFORMS Journal on Computing, 37(3) 503–783.
- Taeho Kim, Kyoung-Kuk Kim, and Eunhye Song† (2025), “Selection of the Most Probable Best”, in Operations Research, 73 (6) 3199–3218.
- Yujia Xie, Gian-Gabriel Garcia, Eunhye Song, and Nicoleta Serban (2025), “Evaluating Access to Pediatric Psychosocial Services: A Discrete Event Simulation Approach under Uncertainty”, in Journal of Simulation, In print.
- Linyun He, Ben M. Feng, and Eunhye Song (2025), “Efficient Experiment Design for Input Uncertainty Quantification of a Ratio Estimator”, in INFORMS Journal on Computing, Accepted.
- Harun Avci, Barry Nelson, Eunhye Song and Andreas Wächter (2026), “Dice and Slice Simulation Optimization for High-Dimensional Discrete Problems,” in European Journal of Operational Research, Accepted.