Tianyi Chen

Tianyi Chen
Assistant Professor
Electrical, Computer, and Systems Engineering

Tianyi Chen has been with Rensselaer Polytechnic Institute (RPI) as an assistant professor since August 2019. Prior to joining RPI, he received the doctoral degree from the University of Minnesota (UMN). He has also held visiting positions at Harvard University, University of California, Los Angeles, and University of Illinois Urbana-Champaign.

Dr. Chen was a finalist for the Best Student Paper Award at the Asilomar Conference on Signals, Systems, and Computers in 2017, a recipient of the Doctoral Dissertation Fellowship at UMN in 2018, a senior co-author of the Best Student Paper Award at the NeurIPS Federated Learning Workshop in 2020, a recipient of IEEE Signal Processing Society Best PhD Dissertation Award in 2020, a senior co-author of the Best Student Paper Award at ICASSP in 2021, and a recipient of NSF CAREER Award in 2021.

Dr. Chen's current research focuses on the theory and application of optimization, machine Learning, and statistical signal processing to problems emerging in data science and wireless communication networks.

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, 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.