Cong Shen

Associate Professor, Electrical and Computer Engineering

Contact

cong@virginia.edu

Office

(434) 924-8940

Location

Thornton E317

Lab

Thornton C309

Google Scholar Personal Website

About

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 an Associate 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, signal processing, communication systems, and networking. In particular, his current research focuses on generative models, in-context learning, 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 Wireless Communications, an editor for the IEEE Transactions on Green Communications and Networking, 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.

Education

B.S. Tsinghua University, China

M.S. Tsinghua University, China

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

Research Interests

Machine Learning Transformers and In-Context Learning; Distributed Learning and Optimization; Reinforcement Learning; Federated Learning

Wireless Communications MIMO; Signal processing for communications; ML for wireless; Communication systems

Selected Publications

Cost-Aware Optimal Pairwise Pure Exploration Di Wu, Chengshuai Shi, Ruida Zhou, and Cong Shen

The 28th International Conference on Artificial Intelligence and Statistics (AISTATS)

Efficient Prompt Optimization Through the Lens of Best Arm Identification Chengshuai Shi, Kun Yang, Zihan Chen, Jundong Li, Jing Yang, Cong Shen

Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024)

Random Orthogonalization for Federated Learning in Massive MIMO Systems Xizixiang Wei, Cong Shen, Jing Yang, H. Vincent Poor

IEEE Transactions on Wireless Communications

Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models Chengshuai Shi, Kun Yang, Jing Yang, Cong Shen

Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024)

Federated Multi-armed Bandits C. Shi and C. Shen

AAAI 2021

Design and Analysis of Uplink and Downlink Communications for Federated Learning S. Zheng, C. Shen, and X. Chen

IEEE Journal on Selected Areas in Communications

An Iterative BP-CNN Architecture for Channel Decoding F. Liang, C. Shen, and F. Wu

IEEE Journal of Selected Topics in Signal Processing

Courses Taught

ECE 4784/6784: Machine Learning for Wireless Communications Spring 2020, Spring 2021, Spring 2022, Spring 2024, Spring 2025

ECE 4501/6501: Matrix Analysis in Engineering and Science Fall 2020, Fall 2024

APMA 3100: Probability Fall 2021, Fall 2022, Fall 2023

Awards

NSF CAREER Award 2022

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

Best Paper of Science Robotics 2024

Finalist for the Best Student Paper Award, Asilomar 2024 Asilomar 2024

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.

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.

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.

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.