Short Bio

I'm a Ph.D. student in Computer Science at Northeastern University, advised by Christopher Amato. My research interests lie in game theory and reinforcement learning, with focuses on (1) designing theory and methods in decentralized partially observable environments and (2) modeling societal-scale multi-agent system dynamics through game theory to enable scalable control.

Keywords: Multi-Agent Reinforcement Learning; Decentralized Partially-Observable Markov Decision Process; Mean-Field Game.

Education

• Northeastern University, Boston, Massachusetts
  Ph.D. in Computer Science, 2024 - Present
  
• Columbia University, New York, New York
  M.S. in Computer Science, Data Science, 2021 - 2024
  
• Sun Yat-sen University, Guangzhou, Guangdong, China
  B.E. in Software Engineering, 2016 - 2020
  

Featured Papers

• [Mathematics]  Scalable Learning for Spatiotemporal Mean-Field Games using Physics-Informed Neural Operator.
  Shuo Liu, Xu Chen, Sharon Di,
  in Mathematics, 2024.
     

• [AAMAS]  A Hybrid Framework of Reinforcement Learning and Physics-Informed Deep Learning for Spatiotemporal Mean-Field Games.
  Xu Chen, Shuo Liu, Sharon Di,
  in 22nd International Conference on Autonomous Agents and Multiagent Systems.
       

Teaching Experience

• Course Assistant, CSOR 4246 Algorithm for Data Science, Fall 2021.
• Course Assistant, COMS 4231 Analysis of Algorithms, Spring 2022.
• Course Assistant, COMS 4995 Empirical Methods for Data Science, Spring 2022, Fall 2022.