Hi, my name is Xutong Liu. I am now a postdoctoral researcher in LIONS research group, in the Department of Electrical and Computer Engineering at Carnegie Mellon University, advised by Prof. Carlee Joe-Wong. 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 supervised by Prof. John C.S. Lui. Prior to that, I received my bachelor’s degree with an honored rank (top 5%) from University of Science and Technology of China (USTC) in 2017.

I am actively seeking faculty or research-focused positions starting in Fall 2025. Here are my CV and my research statement.

My Research

My research strives to develop scalable, generalizable, and robust machine learning algorithms for AI-powered networked systems. In light of this vision, I focus on theoretical foundations of combinatorial decision-making under uncertainty, federated online learning, reinforcement learning with large action spaces, and their applications in edge/cloud computing systems, multimedia networks, smart energy systems, and LLM serving systems.

For my research, I am fortunate to collaborate with many outstanding researchers, including Dr. Wei Chen (IEEE Fellow, Chair of MSR Asia Theory Center), Dr. Siwei Wang from Microsoft Research, Prof. Jinhang Zuo from City University of Hong Kong, Prof. Shuai Li from Shanghai Jiao Tong University, Prof. Enhong Chen (IEEE Fellow), Prof. Defu Lian, Prof. Hong Xie from University of Science and Technology of China, Prof. Don Towsley (IEEE/ACM Fellow), Dr. Xuchuang Wang from University of Massachusetts Amherst, and Prof. Adam Wierman from California Institute of Technology.

I am always open to new research collaborations with both industry and academia. Please contact me if you are interested!

Selected Publications

• [SIGMETRICS] Combinatorial Logistic Bandits
  Xutong Liu, Xiangxiang Dai, Xuchuang Wang, Mohammad Hajiesmaili, John C.S. Lui.
  Accepted in the ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2025 (20/110 = 18.2%).
  [arXiv]

• [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.
  Forty-first International Conference on Machine Learning (ICML), 2024 (2609/9473=27.5%).
  [arXiv] [poster]

• [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.
  Thirty-sixth 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

• May. 2024: Our works on combinatorial bandits view to solve episodic RL and quantum algorithm for online exp-concave optimization are accepted to ICML 2024.
• Dec. 2023: Our work on federated contextual cascading bandits is accepted to AAAI 2024.
• Dec. 2023: Our work on learning context-aware probabilistic maximum coverage bandits is accepted to INFOCOM 2024.
• Oct. 2023: I am visiting 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 to NeurIPS 2023.
• April. 2023: Our work on contextual combinatorial bandits with probabilistically triggered arms is accepted to ICML 2023.
• April. 2023: I was awarded RGC Postdoctoral Fellowship (one of 50 awardees globally)!
• Dec. 2022: Our work on variance-adaptive algorithm for probabilistic maximum coverage problem is accepted to INFOCOM 2023.
• Nov. 2022: Our work on explorative key-term selection strategies for conversational contextual bandits is accepted to AAAI 2023.
• Sept. 2022: Our work on batch-size independent regret bounds for combinatorial bandits is accepted to NeurIPS 2022.
• July. 2022: I successfully pass my Ph.D. thesis defence! I will join CUHK as a postdoc this fall.