Eunhye Song

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

AWARDS 

• 2024 INFORMS Simulation Society Peter Welch Early Career Award

• 2022/23 Lancaster University Management School Visiting Scholar Award 

• Coca-Cola Foundation Early Career Professorship, H. Milton Stewart School of Industrial and Systems Engineering, 2021 - 2024.

• 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

Yujia Xie, Gian-Gabriel Garcia, Eunhye Song, and Nicoleta Serban (2025), Evaluating Access to Pediatric Psychosocial Services: A Discrete Event Simulation Approach under Uncertainty, Accepted, Journal of Simulation.

Taeho Kim*, Kyoung-Kuk Kim, Eunhye Song (2024) Selection of the Most Probable Best, Accepted, Operations Research.

Ben Feng and Eunhye Song (2024) Efficient 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, ACM TOMACS 34 (3), No. 14.

Eunhye Song, Henry Lam, Russell R. Barton (2024) A Shrinkage Approach to Improve Direct Bootstrap Resampling under Input Uncertainty, INFORMS Journal on Computing, 36(4):1023-1039.

Linyun He*, Uday V. Shanbhag, Eunhye Song (2024) Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data, ACM TOMACS 34 (2), No. 6.

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.

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

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). 

SERVICE

• INFORMS-Simulation Society PhD Colloquium Committee, 2023 - 2027

• 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.