Education Background

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

• Committee: Peter W. Glynn (advisor), Nicholas Bambos, Jose Blanchet, Darrell Duffie, J. Michael Harrison, Yinyu Ye

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 Interests

• Simulation

• Non-stationary stochastic modeling and decision making

• Data analytics, financial technologies

Preprints

• Measuring Policy Performance in Online Pricing with Offline Data, with Yue Wang.

• Gradient-based Simulation Optimization using Approximated Systems, with Jingxu Xu.

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

• Doubly Stochastic Generative Arrivals Modeling, with Yufeng Zheng.

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

• Discrete Convex Simulation Optimization, with Haixiang Zhang and Javad Lavaei.

• Learning Operational Decisions with Intertemporal Dependence and Moderate Non-stationarities, with Meng Qi and Zuo-Jun Max Shen.

• Conflicted Immediacy Provision, with Yu An.

• COVID-19 Symptom Web Search Surges Precede Local Hospitalization Surges, with Erol A. Pekoz, Aaron Smith, and Anita Tucker.

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

• Demand Prediction, Predictive Shipping, and Product Allocation for Large-scale E-commerce, with Xiaocheng Li, Yufeng Zheng, and Zhenpeng Zhou.

Publications

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

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

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

• 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.

• 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, Yanfeng Wu, and Jian-Qiang Hu, 2020, accepted by 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.

• Heterogeneous Assets Market Design, with Yu An, 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.

• A scalable approach to enhancing stochastic kriging with Gradients, with Haojun Huo 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

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.

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