91破解版

路 Ph.D. in Electrical Engineering from Arizona State University;

路 M.S. in Telecommunications from HKUST;

路 B.E. in Electrical Engineering from Zhejiang University

Sen Lin is an Assistant Professor in the CS department at 91破解版. Previously, he was a Postdoc in the NSF AI-EDGE Institute at The Ohio State University. His research interests broadly fall in the intersection of machine learning and wireless networking. Currently, his research focuses on developing algorithms and theories in continual learning, reinforcement learning, large language models, mixture-of-experts, and bilevel optimization, with applications in multiple domains, e.g., edge computing, physics, security, network control. His research results have been published in top conferences and journals in machine learning and networking. He co-authored books Edge Intelligence in the Making: Optimization, Deep Learning, and Applications (Morgan & Claypool Publishers, 2020) and Continual and Reinforcement Learning for Edge AI: Framework, Foundation, and Algorithm Design (Springer, 2025).

路 ASE'21 - First-place award winner in research

• H. Li, S. Lin, L. Duan, Y. Liang, and N. Shroff, Theory on mixture-of-experts in continual learning, ICLR (Spotlight), 2025.
• S. Lin, D. Sow, K. Ji, Y. Liang and N. Shroff, Non-convex bilevel optimization with time-varying objective functions, NeurIPS, 2023.
• S. Lin, P. Ju, Y. Liang and N. Shroff, Theory on forgetting and generalization of continual learning, ICML, 2023.
• S. Lin, L. Yang, D. Fan and J. Zhang, TRGP: trust region gradient projection for continual learning, ICLR (Spotlight), 2022.
• S. Lin, G. Yang and J. Zhang, A collaborative learning framework via federated meta-learning, ICDCS, 2020.