Research
The focus of my research is on creating and analyzing simulation-based decision-making algorithms and experiment designs, particularly under model risks. I explore both the theoretical foundations and practical applications of operations research and machine learning.
Below is a list of my articles. You can also find them on my Google Scholar profile.
Publications
Linyun He, Mingbin Ben Feng, Eunhye Song (2026) Efficient Input Uncertainty Quantification for Ratio Estimator. INFORMS Journal on Computing. Linyun He, Luke Rhodes-Leader, Eunhye Song (2024) Digital Twin Validation with Multi-Epoch, Multi-Variate Output Data. In Proceedings of the 2024 Winter Simulation Conference, 347-358. Linyun He, Eunhye Song (2024) Introductory Tutorial: Simulation Optimization under Input Uncertainty. In Proceedings of the 2024 Winter Simulation Conference, 1338-1352. Linyun He, Uday V. Shanbhag, Eunhye Song (2024) Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data. ACM Transactions on Modeling and Computer Simulation. 34 (2), 1-27. Linyun He, Eunhye Song, Mingbin Ben Feng (2023) Efficient Input Uncertainty Quantification for Regenerative Simulation. In Proceedings of the 2023 Winter Simulation Conference, 385-396. Best Theoretical Contributed Paper - Finalist (5/209). Zhunxuan Wang, Linyun He, Chunchuan Lyu, Shay B Cohen (2022) Nonparametric Learning of Two-Layer ReLU Residual Units. Transactions on Machine Learning Research. 1-41. Linyun He, Eunhye Song (2021) Nonparametric Kullback-Liebler Divergence Estimation Using M-Spacing. In Proceedings of the 2021 Winter Simulation Conference, 1-12. Zihao Wang, Linyun He, Zhenyun Qin, Roger Grimshaw, Gui Mu (2019) High-Order Rogue Waves and Their Dynamics of the Fokas–Lenells Equation Revisited: a Variable Separation Technique. Nonlinear Dynamics. 98 (3), 2067-2077.
Preprints and Working Papers
Linyun He, Luke Rhodes-Leader, Eunhye Song (2025) Validating a Stochastic Digital Twin with Multi-Epoch, Multi-Variate Data. Submitted.