Eunhye Song
Coca-Cola Foundation Early Career Professor and Assistant Professor
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology
CONTACT
Email: eunhye.song at isye.gatech.edu
Office: 345 Groseclose
Full CV (last update: 9/29/2023) [Download]
WORK EXPERIENCE
2022-current Coca-Cola Foundation Early Career Professor and Assistant Professor, Industrial and Systems Engineering, Georgia Institute of Technology
2017-2022 Harold and Inge Marcus Early Career Assistant Professor, Industrial and Manufacturing Engineering, Penn State University
EDUCATION
Ph.D. in Industrial Engineering and Management Sciences, Northwestern University, 2017
M.S. in Industrial and Systems Engineering, KAIST, 2012
B.S. in Industrial and Systems Engineering, KAIST, 2010
RESEARCH INTEREST
My research interests lie in simulation analysis and design, in particular
Simulation optimization under model risk
Uncertainty quantification and sensitivity analysis of a simulation model
Gaussian Markov random fields-based large-scale discrete simulation optimization
GRANTS AND AWARDS
Southern Company Services/Georgia Power Simulation Modeling of Ash Pond Closure Programs at Georgia Power, 2024 - 2025.
National Science Foundation (NSF) CMMI-2417616 CMMI-EPSRC: Tackling New Simulation and Optimization Challenges Towards Self-Organizing Manufacturing Digital Twins, 2024 - 2027.
2022/23 Lancaster University Management School Visiting Scholar Award
National Science Foundation (NSF) CMMI-2045400 CAREER: Advancing Theory and Practice of Robust Simulation Analysis Under Input Model Risk, 2021 - 2026.
National Science Foundation (NSF) DMS-1854659 Collaborative Research: Adaptive Gaussian Markov Random Fields for Large-scale Discrete Optimization via Simulation, Role: PI (PI: Barry L. Nelson, co-PI: Andreas Waechter), 2019 - 2023.
2020 INFORMS Junior Faculty Interest Group Paper Competition, Honorable Mention
Harold and Inge Marcus Early Career Assistant Professorship, Industrial and Manufacturing Engineering @ Penn State, 2017 - 2022.
PUBLICATIONS
Students or postdoctoral advisees are marked with *
Journal articles
Ben Feng and Eunhye Song (2024) Optimal Nested Simulation Experiment Design via Likelihood Ratio Method, Accepted, INFORMS Journal on Computing, won Honorable Mention of the 2020 INFORMS Junior Faculty Interest Group Paper Competition.
Xinru Li*, Eunhye Song (2024) Projected Gaussian Markov Improvement Algorithm for High-dimensional Discrete Optimization via Simulation, Accepted, ACM TOMACS.
Eunhye Song, Henry Lam, Russell R. Barton (2024) A Shrinkage Approach to Improve Direct Bootstrap Resampling under Input Uncertainty, Accepted, INFORMS Journal on Computing.
Linyun He*, Uday V. Shanbhag, Eunhye Song (2024) Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data, Accepted, ACM TOMACS.
Taeho Kim*, Kyoung-Kuk Kim, Eunhye Song (2022) Selection of the Most Probable Best, Submitted.
Harun Avci*, Barry Nelson, Eunhye Song and Andreas Waechter (2023) Using Cache or Credit for Parallel Ranking and Selection, ACM TOMACS 33 (4), No. 12.
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).
Michael Hoffman*, Eunhye Song, Michael Brundage, 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.
Mark Semelhago*, Barry L. Nelson, Eunhye Song, Andreas Waechter (2021) Rapid Optimization via Simulation with Gaussian Markov Random Fields, INFORMS Journal on Computing 33 (3) 915-930.
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.
Eunhye Song and Barry L. Nelson (2019) Input-Output Uncertainty Comparisons for Optimization via Simulation, Operations Research, 67(2), 562-576.
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, 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.
Yujing Lin, Eunhye Song, and Barry L. Nelson (2015) Single-Experiment Input Uncertainty, Journal of Simulation 9, 249-259.
Eunhye Song and Barry L. Nelson (2015) Quickly Assessing Contributions to Input Uncertainty, IIE Transactions 47(9), 893-909.
Book chapters
Russell R. Barton, H. Lam, and E. Song (2022) Input Uncertainty in Stochastic Simulation. edited by Salhi, S., Boylan, J., The Palgrave Handbook of Operations Research. Palgrave Macmillan, Cham.
Eunhye Song and Barry L. Nelson (2017) Input Model Risk. edited by Tolk, Fowler, Shao and Yücesan, Advances in Modeling and Simulation: Seminal Research from 50 Years of Winter Simulation Conferences (pp. 63-80), Springer, NY.
Refereed conference proceedings
Linyun He*, Ben Feng and Eunhye Song (2023) Efficient Input Uncertainty Quantification for Regenerative Simulation, In Proceedings of the 2023 Winter Simulation Conference, Best Contributed Paper Finalist.
Taeho Kim* and Eunhye Song (2022) Optimizing Input Data Acquisition for Ranking and Selection: A View through the Most Probable Best, In Proceedings of the 2022 Winter Simulation Conference, 2258-2269.
Mark Semelhago*, Barry L. Nelson, Eunhye Song and Andreas Waechter (2022) Object-oriented Implementation and Parallelization of the Rapid Gaussian Markov Improvement Algorithm, In Proceedings of the 2022 Winter Simulation Conference.
Linyun He* and Eunhye Song (2021) Nonparametric Kullback-Liebler Divergence Estimation using m-Spacing, In Proceedings of the 2021 Winter Simulation Conference, 1-12.
Kyoung-Kuk Kim, Taeho Kim*, Eunhye Song (2021) Selection of the Most Probable Best under Input Uncertainty, In Proceedings of the 2021 Winter Simulation Conference, 1-12.
Xinru Li* and Eunhye Song (2020) Smart Linear Algebraic Operations for Efficient Gaussian Markov Improvement Algorithm, In Proceedings of the 2020 Winter Simulation Conference, 2887-2898.
Ben Feng and Eunhye Song (2019) Efficient Input Uncertainty Quantification via Green Simulation using Sample-Path Likelihood Ratios, In Proceedings of the 2019 Winter Simulation Conference, National Harbor, MA, 3693-3704.
Eunhye Song and Uday Shanbhag (2019) Stochastic Approximation for Simulation Optimization under Input Uncertainty with Streaming Data, In Proceedings of the 2019 Winter Simulation Conference, National Harbor, MA, 3597-3608.
Michael Hoffman*, Eunhye Song, Michael Brundage, and Soundar Kumara (2018) Condition-based maintenance policy optimization using genetic algorithms and Gaussian Markov improvement algorithm, In Proceedings of the Annual Conference of the PHM Society 2018, Philadelphia, PA.
Russell R. Barton, Henry Lam, and Eunhye Song (2018) Revisiting Direct Bootstrap Resampling for Input Model Uncertainty, In Proceedings of the 2018 Winter Simulation Conference, Gothenberg, Sweden.
Eunhye Song and Yi Dong* (2018) Generalized Method of Moments Approach to Hyperparameter Estimation for Gaussian Markov Random Fields, In Proceedings of the 2018 Winter Simulation Conference, Gothenberg, Sweden.
Mark Semelhago*, Barry L. Nelson, Andreas Waechter, and Eunhye Song (2017) Computation Methods for Simulation Optimization Using Gaussian Markov Random Fields, In Proceedings of the 2017 Winter Simulation Conference, Las Vegas, NV.
Eunhye Song (2016) Input-output Uncertainty Comparisons for Optimization via Simulation, Doctoral Colloquium, In Proceedings of the 2016 Winter Simulation Conference, Arlington, VA.
Eunhye Song, Barry L. Nelson, and L. Jeff Hong (2015) Input Uncertainty and Indifference Zone Ranking and Selection, In Proceedings of the 2015 Winter Simulation Conference, 414-424.
Eunhye Song, Barry L. Nelson, and C. D. Pegden (2014) Input Uncertainty Quantification: Advanced Tutorial, In Proceedings of the 2014 Winter Simulation Conference, 162-176.
Eunhye Song and Barry L. Nelson (2013) A Quicker Assessment of Input Uncertainty, In Proceedings of the 2013 Winter Simulation Conference, 474-485.
Eunhye Song, S. Gu, T. Choi and B. K. Choi (2011) A Framework for Integrated Simulation of Production and Material Handling Systems of TFT-LCD Fab, In Proceedings of the 2011 Summer Computer Simulation Conference, IEEE, Hague, 48-54.
PRESENTATIONS
Eunhye Song (2021) Simulation optimization with parameter uncertainty - beyond what-if analysis, Google Workshop on Urban Mobility Simulation and Optimization.
Eunhye Song (2021) Selection of the most probable best, INFORMS Simulation Society Summer Research Workshop, Penn State.
Eunhye Song (2021) S3 Tutorial: Gaussian process metamodeling for simulation, INFORMS Simulation Society Summer School, Penn State.
Eunhye Song (2019) Sequential risk set inference for simulation optimization under input uncertainty, The 20th INFORMS Applied Probability Society Conference, Brisbane, Australia.
Eunhye Song, Mark Semelhago, Barry L. Nelson, and Andreas Waechter (2019) Rapid Search with Gaussian Markov Improvement Algorithm, The 20th INFORMS Applied Probability Society Conference, Brisbane, Australia.
Eunhye Song and Ben Feng (2019) Efficient Input Uncertainty Quantification via Green Simulation using Sample-Path Likelihood Ratios, The Fifth International Conference on the Interface between Statistics and Engineering, Seoul, Korea.
Eunhye Song (2018) Sequential Inferential Optimization via Simulation Under Input Model Risk, INFORMS Annual Meeting 2018, Phoenix, AZ.
Eunhye Song and Barry L. Nelson (2018) Input-Output Uncertainty Comparisons for Optimization via Simulation, SIAM Conference on Uncertainty Quantification 2018, Annaheim, CA.
Eunhye Song, Mark Semelhago, Barry L. Nelson, and Andreas Waechter (2017) Computation Methods for Simulation Optimization Using Gaussian Markov Random Fields, INFORMS Annual Meeting 2017, Houston, TX.
Eunhye Song and Barry L. Nelson (2016) Leveraging the Common Input Data in Comparisons of Systems under Input Uncertainty, INFORMS Annual Meeting 2016, Nashville, TN.
Eunhye Song, Barry L. Nelson, and Jeremy Staum (2016) Multi-resolution Gaussian Markov Random Fields for Discrete Optimization via Simulation, INFORMS Annual Meeting 2016, Nashville, TN.
Eunhye Song, Barry L. Nelson, and Jeremy Staum (2014) A New Measure in Global Sensitivity Analysis: Shapley Values of Input Parameters, INFORMS Annual Meeting 2014, San Francisco, CA.
PHD/POSTDOCTORAL ADVISEES
Current
* Starting years in parentheses.
Jaime Guillermo Gonzalez Hodar (2022), PhD student
Linyun He (2019), PhD student
Past
* Graduation (completion) years in parentheses
Xinru Li (2023), First position: General Motors
Taeho Kim (postdoctoral researcher, 2021-2023), First position: postdoctoral researcher at Texas A & M
Michael Hoffman (2021, coadvised by Soundar Kumara), First position: Department of Defense
Mark Semelhago (2020, coadvised by Barry L. Nelson and Andreas Waechter), First position: Amazon
UNDERGRADUATE RESEARCH
For Penn State undergraduate students who are interested in working on a research project with me, the following is a sample project.
Yidan Wang, Eunhye Song, Huanan Zhang (2019) Simulation study on emergency medical service operations at the Centre LifeLink, Technical Report, Penn State University
TEACHING & ADVISING
Georgia Institute of Technology
ISYE 3044, Simulation Analysis and Design, Spring 2023
Penn State University
IE 322, Probabilistic Models for Industrial Engineers, Fall 2017, 2018, 2019, 2020
IE 425, Stochastic Modeling in Operations Research, Spring 2021
IE 522, Discrete Event Systems Simulation, Spring 2018, 2019, 2020, 2021
INFORMS Student Chapter advisor, 2017 - 2022
Northwestern University
IEMS 317, Discrete-Event Systems Simulation, Spring 2015
SOFTWARE
R 'Sensitivity' package: https://cran.r-project.org/web/packages/sensitivity/index.html
'shapleyPermEx' and 'shapleyPermRand' implement the Shapley effect estimation algorithm in Song, Nelson, and Staum (2016).
Fire spread model implemented in R for global sensitivity analysis: FireFunction.R
Used as a test function for Shapley effect analysis in Song, Nelson, and Staum (2016).
INDUSTRY COLLABORATIONS
General Motors, Operations Research group at the R&D center
Uncertainty quantification in Content Optimization simulation at GM
Simio
Developing sample size error & sensitivity analysis module in Simio simulation software package
SERVICE
INFORMS Journal on Computing Associate Editor - Simulation area
ACM TOMACS Guest Editor - Special issue for 2021 I-SIM Workshop
2021 I-SIM Workshop organizing committee, 2018 - 2021.
2019 Winter Simulation Conference track co-chair for Robust Simulation and Uncertainty Quantification track.
INFORMS-Simulation Society Underrepresented Minorities & Women Committee, 2018 - 2020.
LINKS
