Education Background

Ph.D. in Management Science and Engineering, Stanford University, Stanford, CA, 2018

Ph.D. Minor in Statistics, Stanford University, Stanford, CA, 2018

M.A. in Economics, Stanford University, Stanford, CA, 2016

B.S. in Mathematics, Peking University, Beijing, China, 2012

Research

• Simulation theory

• Non-stationary stochastic modeling and decision making

Working Paper

• Performance Evaluation and Stochastic Optimization with Gradually Changing Non-Stationary Data, with Yuhang Wu.

• Non-stationary A/B Tests: Optimal Variance Reduction, Bias Correction, and Valid Inference, with Yuhang Wu, Guangyu Zhang, Zuohua Zhang and Chu Wang; short version appeared in KDD 2022.

• Selecting the Best Optimizing System, with Nian Si.

• Online Matching with Reusable Network Resources and Decaying Rewards: New Framework and Analysis, with Feng Zhu and David Simchi-Levi.

• Stochastic Localization Methods Algorithms for Convex Discrete Optimization via Simulation, with Haixiang Zhang and Javad Lavaei.

• Offline Planning and Online Learning under Recovering Rewards, with Feng Zhu and David Simchi-Levi; short version appeared in ICML 2021.

• Gradient-based Simulation-Optimization Algorithms via Approximations for Complex Stochastic Systems, with Jingxu Xu.

• Dynamic Pricing with External Information and Inventory Constraint, with Xiaocheng Li.

• Approximating Performance Measures for Slowly Changing Non-stationary Markov Chains, with Harsha Honnappa and Peter W. Glynn.

Publication

• Non-stationary A/B Tests, with Yuhang Wu, Guangyu Zhang, Zuohua Zhang and Chu Wang, accepted by ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), research track, 2022.

• Inference on the Best Policies with Many Covariates, with Waverly Wei, Yuqing Zhou and Jingshen Wang, accepted by Journal of Econometrics, 2022.

• Gradient-based Algorithms for Convex Discrete Optimization via Simulation, with Haixiang Zhang and Javad Lavaei, accepted by Operations Research, 2022.

• Immediacy Provision and Matchmaking, with Yu An, accepted by Management Science, 2022.

• Uncertainty Quantification and Prediction for Mechanical Properties of Graphene Aerogels via Gaussian Process Metamodels, with Bowen Zheng and Grace X. Gu, accepted by Nano Futures, 2021.

• Stochastic L-Convex Function Minimization, with Haixiang Zhang and Javad Lavaei, accepted by Conference on Neural Information Processing Systems (NeurIPS), 2021.

• Scalable Graphene Defect Prediction using Transferable Learning, with Bowen Zheng and Grace X. Gu, accepted by Nanomaterials, 2021.

• Dynamic Planning and Learning under Recovering Rewards, with Feng Zhu and David Simchi-Levi, accepted by International Conference on Machine Learning (ICML) 2021.

• Learning to Simulate Sequentially Generated Data via Neural Networks and Wasserstein Training, with Tingyu Zhu, accepted by Proceedings of the Winter Simulation Conference (WSC) 2021.

• Efficient Computation for Stratified Splitting, with Peter W. Glynn, accepted by Proceedings of the Winter Simulation Conference (WSC) 2021.

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

• When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment, with Feng Zhu, accepted by International Conference on Machine Learning (ICML) 2020.

• Joint Resource Allocation for Input Data Collection and Simulation, with Jingxu Xu and Peter W. Glynn, accepted by Proceedings of the Winter Simulation Conference (WSC) 2020.

• Simulating Nonstationary Spatio-Temporal Poisson Processes using Inversion Method, with Haoting Zhang, accepted by Proceedings of the Winter Simulation Conference (WSC) 2020.

• Approximating Systems Fed by Poisson Processes with Rapidly Changing Arrival Rates, with Harsha Honnappa and Peter W. Glynn, 2020, accepted by Operations Research.

• Method of Moments Estimation for Lévy-driven Ornstein-Uhlenbeck Stochastic Volatility Models, with Xiangyu Yang, Yanfang Wu, Jian-Qiang Hu, 2020, Probability in the Engineering and Informational Sciences.

• Estimation and Inference for Non-stationary Arrival Models, with Peter W. Glynn, 2019, Proceedings of the Winter Simulation Conference.

• Rates of Convergence and CLTs for Subcanonical Debiased MLMC, with Jose Blanchet and Peter W. Glynn, 2018, Monte Carlo and Quasi-Monte Carlo Methods, Springer Proceedings in Mathematics & Statistics, vol 241, pp 465-479.

• Data-driven Ranking and Selection with High Dimensional Covariates and General Dependence, with Xiaocheng Li and Xiaowei Zhang, 2018, Proceedings of the Winter Simulation Conference.

• Fitting Continuous Piecewise Linear Poisson Intensities via Maximum Likelihood and Least Squares, with Peter W. Glynn, 2017, Proceedings of the Winter Simulation Conference.

• A CLT for Infinitely Stratified Estimators, with Applications to Debiased MLMC, with Peter W. Glynn, ESAIM: Proceedings and Surveys (B. Bouchard, E. Gobet and B. Jourdain, Editors), vol 59, pp 104-114.

• Extensions of the Regenerative Method to New Functionals, with Peter W. Glynn, 2016, Proceedings of the Winter Simulation Conference.

Teaching/Courses

Spring 2019, INDENG 173, Introduction to Stochastic Processes.

Fall 2019, INDENG 263A, Applied Stochastic Processes I.

Spring 2020, INDENG 173, Introduction to Stochastic Processes.

Spring 2020, INDENG 174, Simulation for Enterprise-scale Systems.

Fall 2020, INDENG 174, Simulation for Enterprise-scale Systems.

Spring 2021, INDENG 173, Introduction to Stochastic Processes.

Spring 2021, INDENG 290, Special Topics - Simulation, Stochastics, and Learning.

Fall 2021, INDENG 174, Simulation for Enterprise-scale Systems.

Fall 2021, INDENG 263A, Applied Stochastic Processes I.

Spring 2022, INDENG 173, Introduction to Stochastic Processes.

Fall 2022, INDENG 174, Simulation for Enterprise-scale Systems.

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