Tengyang Xie

Hi! I'm a final-year Ph.D. candidate in Computer Science at University of Illinois at Urbana-Champaign (UIUC), where I am fortunate to work with Nan Jiang. Prior to UIUC, I received master degree in Computer Science from University of Massachusetts Amherst. Before that, I obtained bachelor's degree in Physics from University of Science and Technology of China (USTC). I have also spent time at Simons Institute, Amazon AI, Microsoft Research, and Google Research.

Research Interests: I work on Machine Learning. The primary goal of my research is to design provably efficient and practical algorithms relevant to modern machine learning paradigms, especially Reinforcement Learning and Representation Learning. My current interests include:
(1) Reinforcement learning from offline data, e.g., offline RL with general function approximation and arbitrary offline data, and best of both worlds from offline RL and imitation learning, provably and scalably  (MSR blog).
(2) Reinforcement learning from (rich) human feedback, e.g., how to align AI through human interactions, and beyond.
(3) Theoretical foundations of machine learning, e.g., connecting the learability conditions between online and offline RL.
(4) Foundation models for decision making.
Recently, I am also working on their applications to networked systems and human-computer/AI interaction.

I am currently on the job market!

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