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Gang Yan (็‡•ๅˆš)

Full Professor

Jilin University

๐Ÿ“ง gyan8@jlu.edu.cn

๐Ÿ“ Changchun, Jilin, China

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About Me

I received my Ph.D. in Electrical and Computer Engineering from the State University of New York at Binghamton in 2023. Prior to that, I earned an M.S. (2019) and a B.S. (2016) in Statistics from Nankai University (China). In 2024, I served as a postdoctoral scholar at the University of California, Merced, where I further advanced my research expertise.

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Research Interests

My research centers on high-performance network optimization, cloud and edge computing, distributed machine learning, and federated learning. A key focus is addressing security challenges in distributed systems, with an emphasis on designing robust attack mitigation and defense strategies. I strive to advance the efficiency, scalability, and resilience of modern computing infrastructures through innovative and interdisciplinary approaches.

Join My Team

I am seeking highly motivated Ph.D. students to join my research team. Specifically: (a) One position is available for students starting in Fall 2025, (b) Three positions are available for students starting in Fall 2026. If you are interested in pursuing a Ph.D. under my supervision, please feel free to contact me via email. Masterโ€™s students interested in joining my team are also welcome to reach out.

Recent News

[December 2024] [Position] I will join Jilin University (China), as a full professor starting in 2025.

[November 2024] [Service] I will serve as the heavy Program Committee for USENIX ATC 2025.

[August 2024] [News] Congratulations to my PhD advisor, Prof. Jian Li, on receiving the prestigious NSF CAREER Award!

[August 2024] [Paper] Our paper "Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Update" accepted to MobiHoc 2024.

[June 2024] [Paper] Our paper "Enhancing Model Poisoning Attacks to Byzantine-Robust Federated Learning via Critical Learning Periods" accepted to RAID 2024.

[June 2024] [Service] I will serve as a Program Committee (PC) member for AAAI 2025.

[May 2024] [Paper] Our paper "FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation" accepted to ACM KDD 2024.