Chengshuai Shi


Fourth-year Ph.D. student
Department of Electrical and Computer Engineering
University of Virginia

Email: cs7ync at virginia dot edu

Phone: 434-218-9860

Advisor: Prof. Cong Shen

About this picture: My mother drew this Dr. Cat wearing a graduation cap when I was in high school. Now I am working towards a Ph.D. and hopefully, my family can see me wearing it one day:)

Since Fall 2021, I have been supported by the generous Bloomberg Data Science Ph.D. fellowship.

Research Interest

My current research focuses on multi-armed bandits (MAB) problems and more general reinforcement learning problems, especially their multi-agent versions. I also have broad interests in distributed learning and federated learning.


  • 04/2023: Selected to receive the 2023 Charles L. Brown Department of Electrical and Computer Engineering’s Louis T Rader Graduate Research Award!

  • 04/2023: One paper accepted to ICML 2023!

    • Our paper “Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources” (with Wei Xiong, Prof. Cong Shen and Prof. Jing Yang) is accepted to be presented at ICML 2022.

  • 04/2023: Two papers accepted to IEEE ISIT 2023!

    • The first one titled “On High-Dimensional and Low-Rank Tensor Bandits” (with Prof. Cong Shen and Prof. Nikolaos Sidiropoulos) is on extending the one-dimensional system in linear bandits to a high-dimensional one characterized by low-rank tensors;

    • The second one titled “Reward Teaching for Federated Multi-Armed Bandits” (with Wei Xiong, Prof. Cong Shen and Prof. Jing Yang) is on using implicit reward adjustments to guide autonomous bandit agents in a federated multi-armed bandit system.

  • 09/2022: Finished a three-month internship at Bloomberg, NY!