Tianyi Chen has been with the Department of Electrical, Computer and Systems Engineering at Rensselaer Polytechnic Institute (RPI) as an assistant professor since August 2019. He received the bachelor degree from Fudan University, and the doctoral degree from the University of Minnesota. He has also held visiting positions at Harvard University, University of California, Los Angeles, and University of Illinois Urbana-Champaign. His background is in machine learning, optimization, and signal processing. His current research focuses on the theory and application of optimization, machine Learning, and statistical signal processing to problems emerging in data science and 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 and AI, Optimization, Signal Processing, Communication 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.