Thornton E317
Thornton E205
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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, 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.

He received the NSF CAREER award in 2022. He was the recipient of the Best Paper Award in 2021 IEEE International Conference on Communications (ICC), and the Excellent Paper Award in the 9th International Conference on Ubiquitous and Future Networks (ICUFN 2017). Currently, he serves as an associate editor for the IEEE Transactions on Communications, an editor for the IEEE Transactions on Green Communications and Networking, an editor for the IEEE Wireless Communications Letters, and an associate editor for IEEE Transactions on Machine Learning in Communications and Networking. He was the TPC co-chair of the Wireless Communications Symposium of IEEE Globecom 2021, and actively serves as (senior) program committee members/reviewers for Conference on Neural Information Processing Systems (NeurIPS), International Conference on Machine Learning (ICML), International Conference on Learning Representations (ICLR), International Conference on Artificial Intelligence and Statistics (AISTATS), International Joint Conference on Artificial Intelligence (IJCAI), and AAAI Conference on Artificial Intelligence (AAAI). He is a member of SpectrumX, an NSF Spectrum Innovation Center.


B.S. Tsinghua University, China, 2002

M.S. Tsinghua University, China, 2004

Ph.D. University of California Los Angeles (UCLA), 2009

"I'm fascinated by the power of machine learning, communications, and networking."

Cong Shen

Research Interests

Wireless communications and networking
Distributed learning and optimization, federated learning
Multi-armed bandits and reinforcement learning

Selected Publications

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

Courses Taught

ECE 4784/6784: Wireless Communications Spring 2020, Spring 2021, Spring 2022
ECE 4501/6501: Matrix Analysis in Engineering and Science Fall 2020
APMA 3100: Probability Fall 2021, Fall 2022, Fall 2023


NSF CAREER Award 2022
Best Paper Award, IEEE International Conference on Communications (ICC) 2021

Featured Grants & Projects

NSF - Spectrum and Wireless Innovation enabled by Future Technologies (SWIFT) Program 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.
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NSF - ECCS Core Program 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.
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NSF/Intel Partnership on Machine Learning for Wireless Networking Systems (MLWiNS) Program 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.
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NSF CAREER 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.
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