Tianyi Chen

Tianyi Chen is an associate professor in the School of Electrical and Computer Engineering at Cornell University. Prior to joining Cornell, he was an associate professor at the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute, where his research was supported by the RPI-IBM Artificial Intelligence Research Partnership. His research interests include the theoretical and algorithmic foundations of optimization and machine learning, with applications in AI, emerging computing systems, and signal processing. Chen received his Ph.D. from the University of Minnesota in 2019 and his B.Sc. from Fudan University in 2014. He has been recognized with several awards, including the IEEE Signal Processing Society Best Ph.D. Dissertation Award (2020), the NSF CAREER Award (2021), and the Amazon Research Award (2022).

Tianyi Chen is an Associate Professor of Electrical and Computer Engineering at Cornell University, specializing in machine learning, optimization, signal processing, and physical computing. His research focuses on developing robust and efficient algorithms for distributed learning, particularly in the presence of heterogeneous data and Byzantine attacks. Chen's work includes methods such as RSA and LAG, which enhance communication efficiency and convergence in federated and decentralized learning settings. He also explores online convex optimization, reinforcement learning, and IoT management, emphasizing scalability and adaptivity. His contributions address challenges in network resource allocation, data leakage, and multi-agent systems.