Ph.D. Candidate
Department of Computer Science
University of Virginia
Office: 224 Rice Hall
Mail: 85 Engineer's Way, Charlottesville, VA, 22903
Email: shuzeliu AT virginia DOT edu

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Biography

Shuze Liu is a Ph.D. candidate in the Department of Computer Science at the University of Virginia advised by Professor Shangtong Zhang. His research interest is in Reinforcement Learning. He regularly serves on the Program Committee in major AI venues, e.g., ICML, NeurIPS, ICLR, and AAAI. He spent some time at Alibaba Group as a Machine Learning Engineer. He received his M.S. from Yale University and his B.S. from Rensselaer Polytechnic Institute.

Publications

• Doubly Optimal Policy Evaluation for Reinforcement Learning.
  Shuze Liu, Claire Chen, Shangtong Zhang.
  arXiv:2410.02226, 2024.

• Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning.
  Claire Chen*, Shuze Liu*, Shangtong Zhang.
  arXiv:2410.05655, 2024.

• The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise.
  Shuze Liu, Shuhang Chen, Shangtong Zhang.
  Journal of Machine Learning Research (JMLR), 2025.

• Efficient Multi-Policy Evaluation for Reinforcement Learning.
  Shuze Liu, Claire Chen, Shangtong Zhang.
  AAAI Conference on Artificial Intelligence (AAAI), 2025.
  Oral Presentation, 4.7%

• Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design.
  Shuze Liu, Shangtong Zhang.
  International Conference on Machine Learning (ICML), 2024.

• Optimal Pricing of Information.
  Shuze Liu, Weiran Shen, Haifeng Xu.
  ACM Conference on Economics and Computation (EC), 2021.

• Strengthening Smart Contracts to Handle Unexpected Situations.
  Shuze Liu, Farhad Mohsin, Oshani Seneviratne, Lirong Xia.
  IEEE International Conference on Decentralized Applications and Infrastructures, 2019.

Work Experience

Machine Learning Engineer, Alibaba Group, June 2019 - Aug. 2019.

Program Committee

ICML, NeurIPS, ICLR, AAAI.

Guest Lecture

Reinforcement Learning from Human Feedback (Fall 2024),
Reinforcement Learning (Spring 2024).

Teaching Assistant

Reinforcement Learning, Machine Learning, Artificial Intelligence.