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.
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 AreaMachine Learning, Optimization, Signal Processing, Wireless Networks
Selected Scholarly WorksT. 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.
B. Li, T. Chen, and G. B. Giannakis, "Bandit Online Learning with Unknown Delays," Proc. of the Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), Naha, Okinawa, Japan, April 16-18, 2019.
T. Chen, S. Barbarossa, X. Wang, G. B. Giannakis, and Z.-L. Zhang, "Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability," Proceedings of the IEEE, vol. 107, no. 4, pp. 778-796, April 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.