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

• Stochastic modeling

• Data analytics

• Financial technologies

Publications and Preprints

• Demand Prediction, Predictive Shipping, and Product Allocation for Large-scale E-commerce, with Xiaocheng Li, Yufeng Zheng, and Zhenpeng Zhou, 2019, (Finalist, 2018 MSOM Data Driven Research Challenge).

• Approximating Systems Fed by Poisson Processes with Rapidly Changing Arrival Rates, with Harsha Honnappa and Peter W. Glynn, 2019, under revision at Operations Research.

• Approximating Performance Measures for Slowly Changing Non-stationary Markov Chains, with Harsha Honnappa and Peter W. Glynn, 2019, under revision at Operations Research.

• Conflicted Immediacy Provision, with Yu An, 2019, submitted for publication.

• 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.

• 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.

• 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.

Working Papers

Poisson Autoregressive Models for Arrival Data (with Peter W. Glynn and Xiaowei Zhang)

Efficient Real-time Arrivals Prediction and Model Selection (with Peter W. Glynn)

Modeling and Testing Blocks Arrival Patterns

Testing the Poisson Assumption for Stationary and Non-stationary Data (with Harsha Honnappa and Peter W. Glynn)

Teaching

Spring 2019, INDENG 173, Introduction To Stochastic Processes, Office hours: MW 1-2pm

Fall 2020, INDENG 263A, Applied Stochastic Process I

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