Eunhye Song

RESEARCH INTEREST

My research interests lie in simulation analysis theory, 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

PUBLICATIONS

Journal articles

Eunhye Song (2019) Sequential Bayesian Risk Set Inference for Robust Discrete Optimization via Simulation, Submitted.

Mark Semelhago, Barry L. Nelson, Eunhye Song, Andreas Waechter (2019) Rapid Optimization via Simulation with Gaussian Markov Random Fields, major revision at INFORMS Journal on Computing.

Eunhye Song, Peiling Wu-Smith, and Barry L. Nelson (2019) Uncertainty Quantification in Vehicle Content Optimization for General Motors, major revision at INFORMS Journal on Applied Analytics.

Eunhye Song and Barry L. Nelson (2019) Input-Output Uncertainty Comparisons for Optimization via Simulation, Operations Research, 67(2), 562-576.

Peter Salmi, 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

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 proceedings

Ben Feng and Eunhye Song (2019) Efficient Input Uncertainty Quantification via Green Simulation using Sample-Path Likelihood Ratios, Accepted, In Proceedings of the 2019 Winter Simulation Conference, National Harbor, MA.

Eunhye Song and Uday Shanbhag (2019) Stochastic Approximation for Simulation Optimization under Input Uncertainty with Streaming Data, Accepted, In Proceedings of the 2019 Winter Simulation Conference, National Harbor, MA.

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.

CURRENT PHD STUDENTS

* Starting years in parentheses.

• Linyun He (2019), Penn State
• Xinru Li (2018), Penn State
• Michael Hoffman (2016, coadvised by Soundar Kumara), Penn State
• Mark Semelhago (2014, coadvised by Barry L. Nelson and Andreas Waechter), Northwestern 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

• 2019 Winter Simulation Conference track co-chair for Robust Simulation and Uncertainty Quantification track.
• 2020 I-SIM Workshop organizing committee, 2018 - 2020.
• INFORMS-Simulation Society Diversity Committee, 2018 - 2020.