Bio

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

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.

Awards

  • Excellent Paper Award, the 9th International Conference on Ubiquitous and Future Networks (ICUFN) 2017
  • IEEE Senior Member Since 2014

Research Interests

  • Wireless communications and networking
  • Artificial intelligence at the wireless edge
  • Multi-armed bandits and reinforcement learning

Selected Publications

  • Federated Multi-armed Bandits with Personalization C. Shi, C. Shen, and J. Yang, “Regional Multi-Armed Bandits,” Proceedings of the 24th Conference on Artificial Intelligence and Statistics (AISTATS), April 2021
  • Federated Multi-Armed Bandits C. Shi and C. Shen, “Cost-aware Cascading 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
  • Machine learning for clinical trials in the era of COVID-19 W. R. Zame, I. Bica, C. Shen, A. Curth, H.-S. Lee, S. Bailey, J. Weatherall, D. Wright, F. Bretz, and M. van der Schaar, “Machine learning for clinical trials in the era of COVID-19,” Statistics in Biopharmaceutical Research, Special Issue on Covid-19

Courses Taught

  • ECE 4784/6784: Wireless Communications SPRING 2020, SPRING 2021
  • ECE 4501/6501: Matrix Analysis in Engineering and Science FALL 2020

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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