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

1. Linyun He, Luke Rhodes-Leader, Eunhye Song (2024) Digital Twin Validation with Multi-Epoch, Multi-Variate Output Data. Accepted to present at the 2024 Winter Simulation Conference.
2. Linyun He, Eunhye Song (2024) Introductory Tutorial: Simulation Optimization under Input Uncertainty. Accepted to present at the 2024 Winter Simulation Conference.
3. 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.
4. Linyun He, Eunhye Song, Mingbin Ben Feng (2023) Efficient Input Uncertainty Quantification for Regenerative Simulation. In Proceedings of the 2023 Winter Simulation Conference, Best Theoretical Contributed Paper - Finalist (5/209).
5. 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.
6. Linyun He, Eunhye Song (2021) Nonparametric Kullback-Liebler Divergence Estimation Using M-Spacing. In Proceedings of the 2021 Winter Simulation Conference.
7. 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

1. Linyun He, Luke Rhodes-Leader, Eunhye Song (2024) Nonparametric Digital Twin Validation with Multi-Epoch, Multi-Variate and Dependent Output Data. In Preparation.
2. Linyun He, Mingbin Ben Feng, Eunhye Song (2024) Efficient Input Uncertainty Quantification for Ratio Estimator. https://arxiv.org/abs/2410.04696. Submitted.