B.S. Tsinghua University, China, 2002M.S. Tsinghua University, China, 2004Ph.D. University of California Los Angeles (UCLA), 2009
"I'm fascinated by the power of machine learning, communications, and networking."
Cong Shen received his B.E. and M.E. degrees from the Department of Electronic Engineering, Tsinghua University, China. He received the Ph.D. degree from the Electrical Engineering Department, University of California Los Angeles (UCLA). He is currently an Assistant Professor of the Electrical and Computer Engineering Department at University of Virginia (UVa). Prior to joining UVa, He was a professor in the School of Information Science and Technology at University of Science and Technology of China (USTC). He also has extensive industry experience, having worked for Qualcomm Research, SpiderCloud Wireless, Silvus Technologies, and Xsense.ai, in various full time and consulting roles. His general research interests are in the area of machine learning and wireless communications. In particular, his current research focuses on multi-armed bandit, reinforcement learning, federated learning, and their engineering applications.
Best Paper Award, IEEE International Conference on Communications (ICC)2021
Wireless communications and networking
Distributed learning and optimization, federated learning
Multi-armed bandits and reinforcement learning
Federated Multi-armed Bandits with Personalization C. Shi, C. Shen, and J. Yang, “Federated Multi-armed Bandits with Personalization” Proceedings of the 24th Conference on Artificial Intelligence and Statistics (AISTATS), April 2021
Federated Multi-armed Bandits C. Shi and C. Shen, “Federated Multi-armed Bandits,” Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), Feb. 2021
Design and Analysis of Uplink and Downlink Communications for Federated Learning S. Zheng, C. Shen, and X. Chen, “Design and Analysis of Uplink and Downlink Communications for Federated Learning,” IEEE J. Select. Areas Commun., Series on Machine Learning for Communications and Networks, 2021
An Iterative BP-CNN Architecture for Channel Decoding F. Liang, C. Shen, and F. Wu, “An Iterative BP-CNN Architecture for Channel Decoding,” IEEE Journal of Selected Topics in Signal Processing, Vol. 12, No. 1, Page(s): 144-159, Feb. 2018
Learning-Efficient Spectrum Access for No-Sensing Devices in Shared Spectrum
This project develops a novel online learning based framework for distributed low-cost devices to efficiently and effectively access the shared spectrum without spectrum sensing. It specifically focuses on no-sensing devices that do not have the powerful radio-frequency (RF) components to enable wideband spectrum sensing, and addresses the cross-technology spectrum access problem in a decentralized setting.
Towards a Resource Rationing Framework for Wireless Federated Learning
This project aims at developing a novel and rigorous resource allocation framework for wireless FL, which we term resource rationing to emphasize balancing resources over time so that the long-term impact to the final learning outcome is explicitly captured.
Dino-RL: A Domain Knowledge Enriched Reinforcement Learning Framework for Wireless Network Optimization
The goal of this project is to develop a novel domain knowledge enriched RL framework, or Dino-RL, to address this problem. The Dino-RL framework aims to seamlessly integrate the physical-law based modeling and an abstract episodic memory into the RL process, and has the potential to revamp the operation and management of future wireless networks.
CAREER: Towards a Communication Foundation for Distributed and Decentralized Machine Learning
This project aims at developing the theoretical foundation and novel communication algorithms for distributed and decentralized ML, thereby catalyzing a paradigm shift of wireless communications towards connecting intelligence.