Zhengyuan Zhou

I obtained my Ph.D. in the Information System Laboratory in Department of Electrical Engineering at Stanford University in summer 2019. I am advised by Professor Nick Bambos and Professor Peter Glynn . My research interests lie in applying methodological tools from machine learning, applied probability, game theory and stochastic optimization to solve data-driven decision making problems at large. I am funded by a Stanford Graduate Fellowship (SGF) in Science and Engineering. I completed my undergraduate degrees (EECS and Math) at UC Berkeley. At Berkeley, I worked with Professor Claire Tomlin on control and optimization. Over the years, I have also been very fortunate to collaborate with many amazing people. This is my Google Scholar page. I am also an organizer of the department seminar ISL colloqium.

During the year 2019-2020, I am an IBM Goldstine Fellow (and gratefully acknowledge IBM Research's Goldstine fellowship support) and a visiting assistant professor at NYU Stern School of Business. I will officially start teaching as an assistant professor at NYU Stern in Fall 2020 (this website will move soon).


Email : zyzhou@stanford.edu

Packard Bldg.
350 Serra Mall
Stanford, CA 94305




  • Ph.D. in Electrical Engineering, Stanford University, 09/2013 - 09/2019

  • M.S. in Computer Science, Stanford University

  • M.S. in Statistics, Stanford University

  • M.S. in Economics, Stanford University

  • PhD minor in Mathematics, Stanford University

  • PhD minor in Management Science and Engineering, Stanford University

  • (Visiting Researcher at Microsoft Research Asia, 06/2013 - 09/2013)

  • (Junior research scientist at UC Berkeley Hybrid Systems Research Lab, 01/2013- 06/2013)

  • B.E. in Electrical Engineering and Computer Sciences, UC Berkeley, 08/2009 - 12/2012

  • B.A. in Mathematics, UC Berkeley, 08/2009 - 12/2012

Research Interests

  • Data-driven decision making

  • Online learning and online sequential decision making

  • Contextual bandits and reinforcement learning

  • Machine learning and stochastic optimization

  • Stochastic systems and applied probability

  • Control, optimization and game theory


  • INFORMS George Nicholson Award, Finalist, 2018

  • INFORMS George Nicholson Award, Finalist, 2017

  • INFORMS Applied Probability Society Best Student Paper Prize, Finalist, 2017

  • Schlumberger Innovation Fellowship, 2016-2017

  • Stanford Graduate Fellowship in Science and Engineering (Rambus Corporation Fellow) 2013-2016

  • Qualcomm Innovation Fellowship Finalist, 2015-2016, 2016-2017

  • The CRA (Computing Research Association) Outstanding Undergraduate Researchers Award, 2013

  • Berkeley EECS Department Arthur M.Hopkin Award, 2013

  • Microsoft College Scholarship and Scholar, 2012-2013

  • Berkeley Leadership Award and Scholar, 2010-2011, 2011-2012, 2012-2013

Professional Services

  • Reviewer for Journal of Machine Learning Research, Operations Research, IEEE Transactions on Automatic Control, Automatica, IEEE Transactions on Information Theory, IEEE Transactions on Wireless Communications, Journal of Optimization Theory and Applications, Discrete Event Dynamic Systems

  • Reviewer for NIPS, ICML, AAAI, COLT, IEEE Conference on Decision and Control, American Control Conference, IEEE International Conference on Robotics and Automation