Biography

Shuze Daniel Liu is a Postdoctoral Researcher at the MIT Data Science Lab and the Data Science Center for Decision Making at Purdue University’s Mitch Daniels School of Business, advised by Professor David Simchi-Levi. Previously, he was a Research Scientist at Meta, working on LLM fine-tuning with Reinforcement Learning. He received his Ph.D. in Computer Science from the University of Virginia, advised by Professor Shangtong Zhang. He regularly serves on the Program Committee for major AI conferences including ICML, NeurIPS, ICLR, and AAAI.

Journal Articles

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

• Optimal Pricing of Information.
  Shuze Daniel Liu, Weiran Shen, Haifeng Xu.
  Major Revision in Operations Research (OR), 2025.

Conference Publications

• Offline Two-Player Zero-Sum Markov Games with KL Regularization.
  Claire Chen*, Yuheng Zhang*, Xinyu Liu, Zixuan Xie, Shuze Daniel Liu, Nan Jiang.
  International Conference on Machine Learning (ICML), 2026.

• Convergence of Two-Timescale Stochastic Approximation with Markovian Samples and Applications in Reinforcement Learning.
  Vagul Mahadevan, Claire Chen, Shuze Daniel Liu, Shangtong Zhang.
  International Conference on Machine Learning (ICML), 2026.

• MathlibLemma: Folklore Lemma Generation and Benchmark for Formal Mathematics.
  Xinyu Liu, Zixuan Xie, Amir Moeini, Claire Chen, Shuze Daniel Liu, Yu Meng, Aidong Zhang, Shangtong Zhang.
  International Conference on Machine Learning (ICML), 2026.

• The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise.
  Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang.
  Conference on Neural Information Processing Systems (NeurIPS), 2025.

• Doubly Optimal Policy Evaluation for Reinforcement Learning.
  Shuze Daniel Liu, Claire Chen, Shangtong Zhang.
  International Conference on Learning Representations (ICLR), 2025

• Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning.
  Claire Chen*, Shuze Daniel Liu*, Shangtong Zhang.
  International Conference on Learning Representations (ICLR), 2025

• Efficient Multi-Policy Evaluation for Reinforcement Learning.
  Shuze Daniel 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 Daniel Liu, Shangtong Zhang.
  International Conference on Machine Learning (ICML), 2024.

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

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

* indicates equal contribution

Working Papers

• Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards.
  Shuze Daniel Liu*, Claire Chen*, Jiabao Sean Xiao, Lei Lei, Yuheng Zhang, Yisong Yue, David Simchi-Levi.

• AstroAlertBench: Evaluating the Accuracy, Reasoning, and Honesty of Multimodal LLMs in Astronomical Classification.
  Claire Chen*, Jiabao Sean Xiao*, Shuze Daniel Liu*, Facundo Perez Paolino, Luke Handley, Theophile Jegou du Laz, Ricky Nilsson, Alice Zou, Matthew Graham, Ashish Mahabal.

• Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective.
  Jiuqi Wang, Jayanth Srinivasa, Claire Chen, Shuze Daniel Liu, Ali Payani, Shangtong Zhang.

• Optimal Policy Evaluation for Reinforcement Learning.
  Shuze Daniel Liu, Claire Chen, Will Ma, Shangtong Zhang.
  

• Robust Data-Collection Policy Learning for Low-Variance Online Policy Evaluation.
  Claire Chen, Shuze Daniel Liu, Licheng Luo, Rohan Chandra, Nan Jiang, Shangtong Zhang.
  

• Transformers Implement Nonlinear In-Context Reinforcement Learning: Convergence and Emergence.
  Zixuan Xie, Xinyu Liu, Claire Chen, Shuze Daniel Liu, Rohan Chandra, Shangtong Zhang.
  

* indicates equal contribution

Program Committee

ICML, NeurIPS, ICLR, AAAI, AISTATS.

Guest Lecture

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

Teaching Assistant

Reinforcement Learning, Machine Learning, Artificial Intelligence.