Tianyi Chen

Assistant Professor
Electrical, Computer, and Systems Engineering
JEC 6036
Tianyi Chen has been with Rensselaer Polytechnic Institute (RPI) as an assistant professor since August 2019. Dr. Chen is the inaugural recipient of IEEE Signal Processing Society Best PhD Dissertation Award in 2020, a recipient of NSF CAREER Award in 2021 and a recipient of Amazon Research Award in 2022. He is also a co-author of the Best Student Paper Award at the NeurIPS Federated Learning Workshop in 2020 and at IEEE ICASSP in 2021. Dr. Chen's current research focuses on theoretical and algorithmic foundations of optimization, machine learning, and statistical signal processing.

Education

Ph.D, Electrical and Computer Engineering, University of Minnesota, Twin Cities, USA, 2019

M.S., Electrical and Computer Engineering, University of Minnesota, Twin Cities, USA, 2017

B.S., Communication Science and Engineering, Fudan University, China, 2014

Focus Area

machine learning, optimization, statistical signal processing, wireless networks

Selected Scholarly Works

T. Chen, G. B. Giannakis, T. Sun, and W. Yin, "LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning," Proc. of Neural Information Processing (NeurIPS), Montreal, Canada, December 3-8, 2018.

Y. Shen, T. Chen, and G. B. Giannakis, "Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics," Journal of Machine Learning Research, vol. 20, no. 22, pp. 1-36, February 2019.

J. Sun, T. Chen, G. B. Giannakis, and Z. Yang, "Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients," Proc. of Neural Information Processing (NeurIPS), Vancouver, Canada, December 8-14, 2019.

T. Sun, H. Shen, T. Chen and D. Li, "Adaptive Temporal Difference Learning with Linear Function Approximation," IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear, 2021.

T. Chen, Y. Sun and W. Yin, "Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems," Proc. of Neural Information Processing (NeurIPS), Virtual, December 6-14, 2021.