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

• 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

Publications

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 Process 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.








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