Zhengyuan Zhou

  I'm currently an assistant professor in the Department of Technology, Operations, and Statistics at Stern School of Business, New York University. Before joining NYU Stern, I obtained my Ph.D. in the Information System Laboratory in Department of Electrical Engineering at Stanford University in summer 2019, advised by Professor Nick Bambos and Professor Peter Glynn . During the year 2019-2020, I was gratefully supported by the IBM Goldstine fellowship and also a visiting assistant professor at NYU Stern.

My research interests lie at the intersection of machine learning, stochastic control and optimization. I'm broadly interested in developing sample-efficient and computationally efficient policy learning algorithms for data-driven decision making problems. Some of my recent research projects include distributionally robust policy learning, learning to adaptively bid in first-price auctions, efficient policy learning with limited adaptation, offline policy learning using adaptively collected data and next-generation RL.

Email : zzhou@stern.nyu.edu

Recent Work

    Below are some of my recent work (unpublished preprints):
  1. Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits
    Zhimei Ren and Zhengyuan Zhou

  2. Optimal No-regret Learning in Repeated First-price Auctions
    Yanjun Han, Zhengyuan Zhou and Tsachy Weissman

  3. Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions
    Yanjun Han, Zhengyuan Zhou, Aaron Flores, Erik Ordentlich, Tsachy Weissman

  4. Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent State
    Shi Dong, Ben Van Roy and Zhengyuan Zhou

  5. Distributional Robust Batch Contextual Bandits
    Nian Si, Fan Zhang, Zhengyuan Zhou and Jose Blanchet

Research Interests

  • Data-driven decision making

  • Online learning and online sequential decision making

  • Contextual bandits and reinforcement learning

  • Stochastic control and optimization

  • Multi-agent learning


  • IBM Goldstine Fellowship, 2019-2020

  • 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 JMLR, OR, MS, MOR, MSOM, TAC, SIOPT, SICON, Automatica, TIT, JASA,