Zeyu Zheng

Assistant Professor

University of California, Berkeley

Office: 4125 Etcheverry Hall

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

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.

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.

Spring 2021, INDENG 173, Introduction to Stochastic Processes.

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

Spring 2022, INDENG 173, Introduction to Stochastic Processes.












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