Journal publication:

 J13. Non-stationary A/B Tests: Optimal Variance Reduction, Bias Correction, and Valid Inference (2024), Management Science, Accepted, with Yuhang Wu, Guangyu Zhang, Zuohua Zhang and Chu Wang.

 J12.  Stochastic Localization Methods for Convex Discrete Optimization via Simulation (2023), Operations Research, Accepted, with Haixiang Zhang and Javad Lavaei.

 J11.  Offline Planning and Online Learning under Recovering Rewards (2023), Management Science, Accepted, with David Simchi-Levi and Feng Zhu.

 J10.  A Doubly Stochastic Simulator with Applications in Arrivals Modeling and Simulation (2023), Operations Research, Accepted, with Yufeng Zheng and Tingyu Zhu.

 J9.   Dynamic Pricing with External Information and Inventory Constraints (2023), Management Science, Accepted, with Xiaocheng Li. 

 J8.   Inference on the Best Policies with Many Covariates (2023), Journal of Econometrics, Accepted, with Waverly Wei, Yuqing Zhou, and Jingshen Wang.

 J7.   Gradient-based Simulation Optimization Algorithms via Multi-Resolution System Approximations (2023), INFORMS Journal on Computing, Volume 35, Issue 3, pp. 633-651, with Jingxu Xu. 

 J6.   Learning to Simulate Sequentially Generated Data via Neural Networks and Wasserstein Training (2023), ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 33, Issue 3, pp. 1-33, with Tingyu Zhu and Haoyu Liu.  

 J5.   Measuring Policy Performance in Online Pricing with Offline Data: Worst-case Perspective and Bayesian Perspective (2023), Journal of Systems Science and Systems Engineering, Volume 32, pp. 352-371, with Yue Wang. 

 J4.   Gradient-based Methods for Convex Discrete Optimization via Simulation (2022), Operations Research, Volume 71, Issue 5, pp. 1815-1834, with Haixiang Zhang and Javad Lavaei. 

 J3.   Immediacy Provision and Matchmaking (2022), Management Science, Volume 69, Issue 2, pp. 1245-1263, with Yu An. 

 J2.   Technical Note-Approximating Systems Fed by Poisson Processes with Rapidly Changing Arrival Rates (2021), Operations Research, Volume 69, Issue 5, 2021, pp. 1566-1574, with H. Honnappa and Peter W. Glynn.

 J1.   Method of Moments Estimation for Levy-driven Ornstein-Uhlenbeck Stochastic Volatility Models (2020), Probability in the Engineering and Informational Sciences, 35(4), 975-1004, with Xiangyu Yang, Yanfeng Wu and Jian-Qiang Hu.  

Conference proceedings publication:

C27. Combining Numerical Linear Algebra with Simulation (COSIMLA) with General Regeneration Set to Compute Markov Chain Stationary Expectations (2023), Proceedings of 56th Winter Simulation Conference (WSC), with Peter W. Glynn. 

C26. Best Arm Identification with Fairness Constraints on Subpopulations (2023), Proceedings of 56th Winter Simulation Conference (WSC), with Yuhang Wu and Tingyu Zhu. 

C25. A Preliminary Study of Regularization Framework for Constructing Task-Specific Simulators (2023), Proceedings of 56th Winter Simulation Conference (WSC), with Dilara Aykanat and Nian Si. 

C24. Contextual Gaussian Process Bandits with Neural Networks (2023), Advances in Neural Information Processing Systems (NeurIPS), with Haoting Zhang, Jinghai He, Rhonda Righter and Max Zuo-Jun Shen.

C23. Non-stationary Experimental Design under Linear Trends (2023), Advances in Neural Information Processing Systems (NeurIPS), with David Simchi-Levi and Chonghuan Wang. 

C22. Stochastic Multi-armed Bandits: Optimal Trade-off among Optimality, Consistency, and Tail Risk (2023), Advances in Neural Information Processing Systems (NeurIPS), with David Simchi-Levi and Feng Zhu. 

C21. Combining Numerical Linear Algebra with Simulation to Compute Stationary Distributions (2022), Proceedings of 55th Winter Simulation Conference (WSC), pp. 2342-2353, with Alex Infanger and Peter W. Glynn. 

C20. Non-stationary A/B Tests (2022), ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 2079–2089, with Yuhang Wu, Guangyu Zhang, Zuohua Zhang, and Chu Wang.

C19. A Simple and Optimal Policy Design for Online Learning with Safety against Heavy-tailed Risk (2022), Advances in Neural Information Processing Systems (NeurIPS), pp. 33795-33805, with David Simchi-Levi and Feng Zhu. 

C18. Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization (2022), Advances in Neural Information Processing Systems (NeurIPS), pp. 26160-26175, with Tianyi Lin and Michael I. Jordan. 

C17. Stochastic L-convex Function Minimization (2021), Advances in Neural Information Processing Systems (NeurIPS), pp. 13004-13018, with Haixiang Zhang and Javad Lavaei.  

C16. Dynamic Planning and Learning under Recovering Rewards (2021), International Conference on Machine Learning (ICML), pp. 9702-9711, with David Simchi-Levi and Feng Zhu. 

C15. On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification (2021), International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 262-270, with Tianyi Lin, Elynn Y. Chen, Marco Cuturi and Michael I. Jordan. 

C14. Learning to Simulate Sequentially Generated Data via Neural Networks and Wasserstein Training (2021), Proceedings of 54th Winter Simulation Conference (WSC), with Tingyu Zhu. 

C13. Explainable Modeling in Digital Twin (2021), Proceedings of 54th Winter Simulation Conference (WSC), with Lu Wang, Tianhu Deng and Max Zuo-Jun Shen.

C12. Neural Network-Assisted Simulation Optimization with Covariates (2021), Proceedings of 54th Winter Simulation Conference (WSC), with Haoting Zhang, Jinghai He and Donglin Zhan.

C11. Efficient Computation for Stratified Splitting (2021), Proceedings of 54th Winter Simulation Conference (WSC), with Peter W. Glynn. 

C10. When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment (2020), International Conference on Machine Learning (ICML), pp. 11629-11638, with Feng Zhu. 

C9.   Joint Resource Allocation for Input Data Collection and Simulation (2020), Proceedings of 53rd Winter Simulation Conference (WSC), pp. 2126-2137, with Jingxu Xu and Peter W. Glynn. 

C8.   Simulating Nonstationary Spatio-Temporal Poisson Processes using the Inversion Method (2020), Proceedings of 53rd Winter Simulation Conference (WSC), pp. 492-503, with Haoting Zhang. 

C7.   Estimation and Inference for Non-Stationary Arrival Models with a Linear Trend (2019), Proceedings of 52nd Winter Simulation Conference (WSC), pp. 3764-3773, with Peter W. Glynn. 

C6.   Data-Driven Ranking and Selection: High-Dimensional Covariates and General Dependence (2018), Proceedings of 51st Winter Simulation Conference (WSC), pp. 1933-1944, with Xiaocheng Li and Xiaowei Zhang.

C5.   A Scalable Approach to Enhancing Stochastic Kriging with Gradients (2018), Proceedings of 51st Winter Simulation Conference (WSC), pp. 2213-2224, with Haojun Hao and Xiaowei Zhang. 

C4.   A CLT for Infinitely Stratified Estimators with Applications to Debiased MLMC (2017), ESAIM: Proceedings and Surveys 59, pp. 104-114, with Peter W. Glynn. 

C3.   Fitting Continuous Piecewise Linear Poisson Intensities via Maximum Likelihood and Least Squares (2017), Proceedings of 50th Winter Simulation Conference (WSC), pp. 1740-1749, with Peter W. Glynn. 

C2.   Rates of Convergence and CLTs for Subcanonical Debiased MLMC (2016), International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, pp. 289-301, with Jose Blanchet and Peter W. Glynn. 

C1.   Extensions of the Regenerative Method to New Functionals (2016), Proceedings of 49th Winter Simulation Conference (WSC), pp. 289-301, with Peter W. Glynn.

Working paper:

W1.  Performance Evaluation and Stochastic Optimization with Gradually Changing Non-Stationary Data (with Y. Wu).

W2.  A Dynamic Factor Model of Price Impacts (with Y. An).

W3.  An Axiomatic Approach to Informed Order Flow  (with Y. An). 

W4.  Adaptive A/B Tests and Simultaneous Treatment Parameter Optimization (with Y. Wu, G. Zhang, Z. Zhang and C. Wang).

W5.  A Simple and Optimal Policy Design with Safety against Heavy-tailed Risk for Stochastic Bandits (with D. Simchi-Levi and F. Zhu).

W6.  Selecting the Best Optimizing System (with N. Si).

W7.  On Greedy-like Policies in Online Matching with Reusable Network Resources and Decaying Rewards (with D. Simchi-Levi and F. Zhu).

W8.  Approximating Performance Measures for Slowly Changing Non-stationary Markov Chains (with H. Honnappa and P. W. Glynn).

Application of simulation and machine learning in mechanical materials:

 M3.  Designing Mechanically Tough Graphene Oxide Materials using Deep Reinforcement Learning (2022), npj Computational Materials, with B. Zheng and G.X. Gu.  

 M2.  Uncertainty Quantification and Prediction for Mechanical Properties of Graphene Aerogels via Gaussian Process Metamodels (2021), Nano Futures, with B. Zheng and G.X. Gu.  

 M1.  Scalable Graphene Defect Prediction Using Transferable Learning (2021), Nanomaterials, with B. Zheng and G.X. Gu.