Hi, my name is Xutong Liu. I am now a postdoctoral researcher in LIONS research group 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 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.

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

I am also very excited to co-organize the 3rd Annual Workshop on Learning-Augmented Algorithms: Theory and Applications at ACM SIGMETRICS 2025, taking place in June at Stony Brook University, New York, USA. We warmly welcome posters/talks if you’re interested. For more details, please visit the official workshop website.

My Research

We live in an era defined by intelligence and connectivity. Artificial intelligence is the new “water” and “electricity”. AI computing and communication systems, such as data centers, communication networks, and IoT systems, are like pipes and wires, enabling intelligence flow smoothly across different platforms, devices, and users.

Just as well-designed power grids ensure electricity reaches every corner of the world with efficiency and reliability, I strive to develop scalable and robust machine learning algorithms to enhance the decision-making process in AI computing and communication systems. By doing so, I hope AI can be produced and delivered in a way that is accessible, adaptable, and reliable across diverse environments and use cases.

My research approach is around building theoretical foundations for online learning and reinforcement learning (RL)-based decision-making, and more importantly, making sure they can be readily translated into real-world applications. My recent focuses are:

Online Learning/Reinforcement Learning (RL) Theory:

• Scalable Combinatorial Decision-making under Uncertainty:
  [LZWJLC, NeurIPS ‘22], [LZCCL, ICML ‘21]
• Generalizable Combinatorial Online Learning with Function Approximation:
  [LDWHL, SIGMETRICS ‘25], [LZWXL, INFOCOM ‘24], [LZWLHWC, ICML ‘23]
• Robust Multi-agent Online Learning in Heterogeneous and Unreliable Environments:
  [WLZLLH, ICLR ‘25], [WCYLHTL, SIGMETRICS ‘25], [WLZX, INFOCOM ‘25], [YLWXLLC, AAAI ‘24], [WXLLL, NeurIPS ‘23], [WYCLHTL, UAI ‘23], [CYWLHLT, AISTATS ‘23], [WYCLHTJ, ICLR ‘23], [LZYLL, UAI ‘23]
• Reinforcement Learning with Large Action Spaces:
  [LWJZWWLHJC, ICML ‘24]

Network Applications:

• Edge/Cloud Computing, Multimedia Networking, and IoT Systems:
  [DLZXJL, IEEE/ACM TON], [DZYXLL, ACM MM ‘24], [LZXJL, INFOCOM ‘23], [CLCL, INFOCOM ‘18], [CLCL, IEEE TMC]
• Social Networks and Conversational Recommendation Systems:
  [WLLL, AAAI ‘23], [ZLJLC, AISTATS ‘22], [DWXLL, IEEE TKDE]
• Large Language Model (LLM) Training/Serving Systems:
  [DLLYL, ArXiv ‘24]

Collaborations

For my research, I am fortunate to collaborate with many outstanding researchers, including Dr. Wei Chen (IEEE/ACM 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 the 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 ‘25] 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.
  [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.
  [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.
  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

• 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)!