Hi, my name is Xutong (James) Liu. I am now a postdoctoral researcher in LIONS research group at Carnegie Mellon University, advised by Prof. Carlee Joe-Wong. I will be joining the University of Washington in Fall 2025 as a Tenure-Track Assistant Professor of Computer Science & Systems at Tacoma School of Engineering & Technology.

At UW, I plan to build the Learning, Evaluation, and Advanced Decision-making (LEAD) research lab and actively recruit Ph.D. students in Fall 2026, check group information for details if you are interested in joining us.

Previously, I was a visiting postdoc in SOLAR Lab at University of Massachusetts Amherst, advised by Prof. Mohammad Hajiesmaili, and a postdoctoral fellow in ANSR Lab at the Chinese University of Hong Kong (CUHK), advised by Prof. John C.S. Lui (IEEE/ACM Fellow). I received my Ph.D. degree from the Computer Science and Engineering Department at CUHK in 2022, proudly advised by Prof. John C.S. Lui. Prior to that, I received my bachelor’s degree with an honored rank (top 5%) from the University of Science and Technology of China (USTC) in 2017.

My Research

My research focuses on building structure-aware online learning and reinforcement learning (RL) algorithms that exploit the underlying action, feedback, and agent structures in networked systems (such as smoothness, sparsity, clustering) for optimal decision-making. I place a special emphasis on theory-grounded data-efficiency, scalability, and robustness guarantees of these algorithms, while also ensuring they can be readily applicable to real-world decision-making problems in complex network environments. My recent focuses are:

🚀 Structure-aware Online Learning/Reinforcement Learning (RL):

• Reinforcement Learning with Large Action Spaces:
  [ICML ‘25a], [ICML ‘25b], [ICML ‘24]
• Scalable Combinatorial Decision-making under Uncertainty:
  [NeurIPS ‘22], [ICML ‘21, 🎤️Long Oral]
• Generalizable Combinatorial Online Learning with Function Approximation:
  [SIGMETRICS ‘25, 🏆Best Paper Finalists], [INFOCOM ‘24], [ICML ‘23]
• Robust Multi-agent Online Learning in Heterogeneous and Unreliable Environments:
  [ICLR ‘25], [SIGMETRICS ‘25], [INFOCOM ‘25], [AAAI ‘24], [NeurIPS ‘23], [UAI ‘23], [AISTATS ‘23], [ICLR ‘23], [UAI ‘23]

🛜 Decision-making for Network Applications:

• Edge/Cloud Computing, Multimedia Networking, and IoT Systems:
  [IEEE/ACM TON], [ACM MM ‘24], [INFOCOM ‘23], [INFOCOM ‘18], [IEEE TMC]
• Conversational Recommendation Systems and Social Network Marketing:
  [KDD ‘25], [AAAI ‘23], [AISTATS ‘22], [IEEE TKDE]
• Cost-effective Large Language Model (LLM) Training/Serving Systems:
  [ArXiv ‘24]

Selected Publications

• [ICML ‘25] Offline Learning for Combinatorial Multi-armed Bandits
  Xutong Liu, Xiangxiang Dai, Jinhang Zuo, Siwei Wang, Xuchuang Wang, Carlee Joe-Wong, John C.S. Lui, and Wei Chen.
  Accepted by the Forty-second International Conference on Machine Learning (ICML), 2024.
  [arXiv] [slides]

• [SIGMETRICS ‘25, 🏆Best Paper Finalists] Combinatorial Logistic Bandits
  Xutong Liu, Xiangxiang Dai, Xuchuang Wang, Mohammad Hajiesmaili, John C.S. Lui.
  The ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2025.
  Best Paper Finalists (Top 5) at SIGMETRICS 2025.
  [arXiv] [code] [slides]

• [ICML ‘24] Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond
  Xutong Liu, Siwei Wang, Jinhang Zuo, Han Zhong, Xuchuang Wang, Zhiyong Wang, Shuai Li, Mohammad Hajiesmaili, John C.S. Lui, and Wei Chen.
  The 41st International Conference on Machine Learning (ICML), 2024.
  [arXiv] [poster] [slides] (By Prof. Shuai Li)

• [NeurIPS ‘22] Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
  Xutong Liu, Jinhang Zuo, Siwei Wang, Carlee Joe-Wong, John C.S. Lui, Wei Chen.
  The 36th Conference on Neural Information Processing Systems (NeurIPS), 2022.
  [arXiv] [paper] [slides] [poster]

• [UAI ‘22] Federated Online Clustering of Bandits
  Xutong Liu, Haoru Zhao, Tong Yu, Shuai Li, John C.S. Lui.
  The 38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022.
  [paper] [arXiv] [slides] [poster] [code]

• [ICML ‘21, 🎤️Long Oral] Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning
  Xutong Liu, Jinhang Zuo, Xiaowei Chen, Wei Chen, John C.S. Lui.
  The 38th International Conference on Machine Learning (ICML), Long Oral, 2021.
  [paper] [arXiv] [slides] [poster] [video]

News

• June. 2025: I am invited as a TPC for ACM SIGMETRICS 2026!
• May. 2025: Our paper “Combinatorial Logistic Bandits” has been selected as one of the five Best Paper Finalists at SIGMETRICS 2025!
• Dec. 2024: We are excited to co-organize the 3rd Annual Workshop on Learning-Augmented Algorithms: Theory and Applications at ACM SIGMETRICS 2025. The workshop will take place at Stony Brook University, New York, USA. For more details, visit the official workshop website.
• Dec. 2024: Our works on (1) robust combinatorial contextual bandits and (2) online learning algorithms to learn the best quantum path have been accepted by INFOCOM 2025.
• Oct. 2024: Our work on combinatorial bandits with logistic function approximation has been accepted by ACM SIGMETRICS 2025.
• Sept. 2024: I am joining Carnegie Mellon University as a postdoctoral researcher advised by Prof. Carlee Joe-Wong.
• May. 2024: Our works on (1) combinatorial bandits view to solve episodic RL and (2) quantum algorithm for online exp-concave optimization are accepted by ICML 2024.
• Oct. 2023: I am visiting the University of Massachusetts Amherst as a visiting scholar advised by Prof. Mohammad Hajiesmaili.
• Sept. 2023: Our work on online clustering of bandits with misspecified user model is accepted by NeurIPS 2023.
• April. 2023: Our work on contextual combinatorial bandits with probabilistically triggered arms is accepted by ICML 2023.
• April. 2023: I was awarded RGC Postdoctoral Fellowship (one of 50 awardees globally)!