Jian Li is an Associate Professor in the Department of Applied Mathematics and Statistics and the Department of Computer Science at Stony Brook University. He contributes to the interdisciplinary Data Science Program jointly offered by the two departments. He is also a core faculty member of the AI Innovation Institute (AI3) and an affiliate of the Institute for Advanced Computational Science (IACS).
His research lies at the intersection of networking, machine learning, and optimization, with an emphasis on developing principled learning and decision-making methods for dynamic and resource-constrained networked systems.
Before joining Stony Brook University in 2023, he was an Assistant Professor at Binghamton University and a postdoctoral researcher at the University of Massachusetts Amherst, hosted by Prof. Don Towsley. He received his Ph.D. in Computer Engineering from Texas A&M University in 2016 under the supervision of Prof. Srinivas Shakkottai and worked closely with Prof. Vijay Subramanian from University of Michigan. He received his B.E. from Shanghai Jiao Tong University in 2012.
Dr. Li received the NSF CAREER Award in 2024 and NSF CISE CRII Award in 2021. His work has also received finalist recognition for the ACM/IEEE SC'24 Best Student Paper Award and Best Paper Runner-Up Award at ACM e-Energy'21 and IEEE MASCOTS'18. His research has been supported by NSF, NIH, ARO, and DOE.
My research centers on structured learning and optimization for networked intelligence. From a networking perspective, I develop theory, algorithms, and systems that make learning and decision-making efficient, adaptive, and trustworthy under uncertainty, heterogeneity, non-stationarity, and resource constraints. My work spans three closely connected areas:
Structured learning, control, and optimization: structured reinforcement learning (RL); restless and non-stationary multi-armed bandits; learning-augmented online optimization; RL with human, preference and language feedback; and RL for large language model (LLM) reasoning and Agentic AI.
Federated and decentralized intelligence: federated, decentralized, continual, personalized, and multimodal learning, with an emphasis on communication efficiency, asynchronous operation, robustness, and adaptation across heterogeneous devices and data sources.
Networked AI systems and applications: next-generation wireless networks; edge and cloud computing; caching and content delivery; serverless computing; scheduling, deployment, and routing for LLM and multimodal AI services; and smart health.
08/2026: [Paper] One paper accepted to ACM MobiHoc 2026 (Acceptance rate: 23%).
08/2026: [Service] I will serve as an Area Chair for ICLR 2027.
06/2026: [Service] I will serve as an Action Editor for the Transactions on Machine Learning Research (TMLR).
06/2026: [Service] I will serve as a Registration Chair for ACM SIGMETRICS 2027.
05/2026: [Milestone] I have been promoted to Associate Professor with tenure, effective September 1, 2026!