Hi, my name is Xutong (James) Liu. I am now a Tenure-Track Assistant Professor at the University of Washington, affiliated with the Department of Computer Science & Systems at the Tacoma School of Engineering & Technology. At UW, I am building the Learning, Evaluation, and Advanced Decision-making (LEAD) research lab and actively recruiting Ph.D. students, master students, and undergraduate interns in Fall 2026, check group information for details if you are interested in joining us.

Previously, I was a postdoctoral researcher in LIONS research group at Carnegie Mellon University, advised by Prof. Carlee Joe-Wong. Before that, I was a visiting postdoc in SOLAR Lab at the 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 developing structure-aware reinforcement learning (RL) and online learning algorithms that leverage inherent action, feedback, and agent structures—such as smoothness, sparsity, and clustering—to enable data-efficient, scalable, and robust decision-making for networked AI systems.

I aim to bridge theory and real-world applications for cost-effective LLM serving (rounting/caching/domain adaptation), mobile/edge/cloud co-optimization (VR/AR immersive computing, CDNs), and robust multi-agent learning systems (federated learning systems), guided by three core questions:

1. Data Efficiency: How much offline and/or online data is needed to identify near-optimal policies?
2. Scalability: How can we design algorithms that scale efficiently across high-dimensional action spaces and multiple agents?
3. Robustness: How can learning remain stable and adaptive in dynamic, uncertain, and heterogeneous learning environments?

My recent projects focus on building:

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

• Hybrid Reinforcement Learning with Fused Offline-Online and Relative-Absolute Data:
  [NeurIPS ‘25], [ICML ‘25b], [KDD ‘25], [arXiv ‘25]

• Reinforcement Learning with Large Action Spaces:
  [ICML ‘25a], [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 Runner-Up], [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 Networked AI Systems:

• Cost-effective Large Language Model (LLM) Training/Serving Systems:
  [AAAI ‘26], [arXiv ‘25a], [arXiv ‘25b], [arXiv ‘25c], [arXiv ‘24]
• 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]

Selected Recent Publications

• [AAAI '26] Online Multi-LLM Selection via Contextual Bandits under Unstructured Context Evolution
  Manhin Poon, XiangXiang Dai, Xutong Liu, Fang Kong, John C.S. Lui, Jinhang Zuo.
  Accepted by the 40th Annual AAAI Conference on Artificial Intelligence (AAAI), 2026.
  [arXiv]

• [Preprint] HiLoRA: Adaptive Hierarchical LoRA Routing for Training-Free Domain Generalization
  Ziyi Han, Huanyu Wang, Zeyu Zhang, Xiangxiang Dai, Xutong Liu, John C.S. Lui.
  [arXiv]

• [Preprint] Faster, Smaller, and Smarter: Task-Aware Expert Merging for Online MoE Inference
  Ziyi Han, Xutong Liu, Ruiting Zhou, Xiangxiang Dai, John C.S. Lui.
  [arXiv]

• [Preprint] Offline Clustering of Preference Learning with Active-data Augmentation
  Jingyuan Liu, Fatemeh Ghaffari, Xuchuang Wang, Xutong Liu#, Mohammad Hajiesmaili, Carlee Joe-Wong.
  [arXiv]

• [NeurIPS '25] Learning Across the Gap: Hybrid Multi-armed Bandits with Heterogeneous Offline and Online Data
  Qijia He, Minghan Wang, Xutong Liu, Zhiyong Wang, Fang Kong.
  The Thirty-nineth Conference on Neural Information Processing Systems (NeurIPS), 2025.
  [Openreview]

• [SIGMETRICS ‘25, 🏆Best Paper Runner-Up] 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 Runner-Up at SIGMETRICS 2025.
  [arXiv] [code] [slides]

• [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

• Nov. 2025: I am invited as a publicity chair for the 32nd International European Conference on Parallel and Distributed Computing (Euro-Par).
• Sept. 2025: Our work on Hybrid Multi-armed Bandits with Heterogeneous Offline and Online Data is accepted by NeurIPS 2025.
• Sept. 2025: I am joining the University of Washington - Tacoma as a Tenure-Track Assistant Professor In CSS.
• June. 2025: I am invited as a TPC for ACM SIGMETRICS 2026 and ACM e-Energy 2026!
• May. 2025: Our paper “Combinatorial Logistic Bandits” has been selected as one of the Best Paper Runner-Up 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)!