B.S. Tsinghua University, China, 2002M.S. Tsinghua University, China, 2004Ph.D. University of California Los Angeles (UCLA), 2009
"5G and beyond created new challenges for communication theorists and practitioners to rethink the system designs, and new tools from machine learning may benefit this task.
Cong Shen received his B.S. and M.S. degrees, in 2002 and 2004 respectively, from the Department of Electronic Engineering, Tsinghua University, China. He obtained the Ph.D. degree from the Electrical Engineering Department, University of California Los Angeles (UCLA), in 2009. Prior to joining the Electrical and Computer Engineering Department at University of Virginia, Dr. Shen 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 communication theory, wireless communications, and machine learning.
He was the recipient of the “Excellent Paper Award” in the 9th International Conference on Ubiquitous and Future Networks (ICUFN 2017). Currently, he serves as an editor for the IEEE Transactions on Wireless Communications, and editor for the IEEE Wireless Communications Letters.
Excellent Paper Award, the 9th International Conference on Ubiquitous and Future Networks (ICUFN)2017
IEEE Senior MemberSince 2014
Machine Learning For Wireless System Designs
Multi-Armed Bandit And Its Applications
Regional Multi-Armed Bandits Z. Wang, R. Zhou, and C. Shen, “Regional Multi-Armed Bandits,” Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 84:510-518, Playa Blanca, Lanzarote, Canary Islands, April 9-11, 2018
Cost-aware Cascading Bandits R. Zhou, C. Gan, J. Yang, and C. Shen, “Cost-aware Cascading Bandits,” Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), Pages 3228-3234, July 2018
A Non-stationary Online Learning Approach to Mobility Management Y. Zhou, C. Shen, and M. van der Schaar, “A Non-stationary Online Learning Approach to Mobility Management,” IEEE Trans. Wireless Commun., vol. 18, no. 2, pp. 1434-1446, Feb. 2019
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
Cost-Aware Learning and Optimization for Opportunistic Spectrum Access C. Gan, R. Zhou, J. Yang, and C. Shen, “Cost-Aware Learning and Optimization for Opportunistic Spectrum Access,” IEEE Trans. Cogn. Commun. Netw., vol. 5, no. 1, pp. 15-27, Mar. 2019
Small Cell Transmit Power Assignment Based on Correlated Bandit Learning Z. Wang and C. Shen, “Small Cell Transmit Power Assignment Based on Correlated Bandit Learning,” IEEE Journal on Selected Areas in Communications, Vol. 35, No. 5, Page(s): 1030-1045, May 2017