Junzi Zhang 张峻梓

About me

Junzi Zhang photo 

Welcome to my homepage!

Currently I am working at Amazon Advertising as an Applied Scientist II.

I got my Ph.D. degree in Computational Mathematics at Stanford University, advised by Prof. Stephen P. Boyd from Stanford Department of Electrical Engineering. I have also been working closely with Prof. Xin Guo and Prof. Mykel J. Kochenderfer. Before coming to Stanford, I obtained a B.S. degree in applied mathematics from School of Mathematical Sciences, Peking University, where I conducted my undergraduate research under the supervision of Prof. Zaiwen Wen and Prof. Pingwen Zhang. My research has been focused on the design and analysis of optimization algorithms and software, and extends broadly into the fields of machine learning, causal inference and decision-making systems (especially reinforcement learning). I'm also recently extending my research to federated optimization, predictive modeling and digital advertising. My research had been partly supported by Stanford Graduate Fellowship.

Please find my CV here.


Email: junziz [at] stanford (dot) edu
Personal Email: saslas (dot) c (dot) royale [at] gmail (dot) com
[ResearchGate] [Google Scholar] [github] [LinkedIn]


Research Interests

  • Optimization

  • Reinforcement learning, optimal control & game theory

  • Machine learning, statistics & applied probability


  • I'm organizing a session on Recent Advances in Data Efficient Reinforcement Learning with Policy Gradient Methods at the 2021 INFORMS Annual Meeting.

  • Two new papers posted on arXiv. In these papers, we derive the first set of global convergence results for stochastic policy gradient methods with momentum and entropy. (October 19, 2021)

  • New paper posted on arXiv. In this paper, we quantify the widely-used “fictitious discount” empirical trick in finite-horizon episodic reinforcement learning from a rigorous theoretical perspective for the first time. (September 13, 2021)

  • I gave a talk at Seminars in Applied Mathematics, Yau Mathematical Sciences Center, Tsinghua University. (August 19, 2021)

  • I'm recognized as an outstanding reviewer at ICLR 2021. (March 19, 2021)

  • I recently joined Amazon Advertising (Palo Alto) as an Applied Scientist II in March 2021.

  • I gave a talk on “New Discoveries of Old Wisdoms for Faster Optimization” as an invited speaker at the NeurIPS 2020 Nairobi Meetup. [Video] (December 10, 2020)

  • Our paper Sample Efficient Reinforcement Learning with REINFORCE is accepted at AAAI 2021. (December 2, 2020)