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 recipient of the Doctoral Dissertation Fellowship at UMN, a finalist for the Best Student Paper Award at the Asilomar Conference on Signals, Systems, and Computers, and the inaugural recipient of IEEE Signal Processing Society Best PhD Dissertation Award.

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

Research Focus
  • Machine Learning
  • Optimization
  • Signal Processing
  • Wireless Networks
Select 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.
  • 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.