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Zhengyuan Zhou
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| 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).
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Contact
Email : zyzhou@stanford.edu
Packard Bldg.
350 Serra Mall
Stanford, CA 94305
News
Education
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
Awards
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
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