Zhe Li
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Zhe Li |
Bio
Zhe is currently a second-year Ph.D. student in Computing and Information Sciences program at Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT), advised by Dr. Haibo Yang. He applies tools from optimization theory in the following fields:
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Large-Scale Distributed/Federated Learning: Tackling system and statistical heterogeneity, enhancing communication efficiency, and accelerating convergence in collaborative training.
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Efficient Training of Large Language Models (LLMs): Designing memory-, communication-, and parameter-efficient algorithms and frameworks for scalable fine-tuning and pretraining.
I am glad to discuss ideas about my research interests, so please feel free to reach out!
Recent News
May. 2025: Successfully passed the Ph.D. Qualifying Exam.
Apr. 2025: Our paper "FAST: A Lightweight Mechanism Unleashing Arbitrary Client Participation in Federated Learning" has been accepted to IJCAI 2025.
Jan. 2025: Our paper "Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization" has been accepted to ICLR 2025.
Nov. 2024: Our paper "DeComFL: Federated Learning with Dimension-Free Communication" has been accepted to FL@FM-NeurIPS'24 (Oral).
Aug. 2024: Joined RIT to study for my Ph.D., advised by Dr. Haibo Yang.
Education
Ph.D., Computing and Information Sciences, Rochester Institute of Technology, Present
M.S., Electrical Engineering, University of Southern California, May 2024
B.Eng., Software Engineering, Shanghai University of Electric Power, July 2019
Professional Services
Reviewer: KDD'24, INFOCOM'24, IJCNN'25, FedVision@CVPR'25, CFAgentic@ICML'25, NeurIPS'25, DSAA'25