معرفی
Cong Shen is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Virginia, where he leads a research group focused on machine learning, wireless communications, and networking. He is affiliated with the UVA Link Lab and serves as Deputy Director of Collaboration at SpectrumX, an NSF Spectrum Innovation Center. He has previously held faculty positions at the University of Science and Technology of China (USTC) and maintains strong industry ties with companies such as Qualcomm, SpiderCloud Wireless, Silvus Technologies, and Xsense.ai.
Education:
- B.E. and M.E., Electronic Engineering, Tsinghua University, China
- Ph.D., Electrical Engineering, University of California, Los Angeles (UCLA), USA
His research lies at the intersection of machine learning and communication systems, with a focus on in-context learning, transformers, federated learning, reinforcement learning, distributed optimization, multi-armed bandits, and AI for wireless. His work aims to bridge theoretical foundations with engineering applications in next-generation wireless networks and intelligent systems.
His recent publications (2023–2025) reveal a strong trend toward integrating foundational models with communication constraints, particularly in federated and decentralized settings. Key themes include in-context learning with provable guarantees, efficient prompt optimization using bandit methods, privacy-preserving federated learning, and reinforcement learning for wireless resource management. His work frequently appears in top-tier venues such as NeurIPS, ICML, ICLR, AISTATS, and IEEE ICC.
Scientific Awards:
- NSF CAREER Award (2022)
- Best Paper Award, IEEE ICC (2021)
- Excellent Paper Award, ICUFN (2017)
- Best Paper of 2024, Science Robotics
- Finalist for Best Student Paper Award, Asilomar (2024)
Dr. Shen advises a dynamic group of graduate and undergraduate students, including PhD candidates Chengshuai Shi, Zhoubin Kou, Di Wu, and others. He leads multiple NSF-funded projects, including initiatives under the SWIFT, ECCS Core, MLWiNS, and CAREER programs, focusing on spectrum access, resource rationing in wireless FL, and domain-knowledge-enriched RL for network optimization. His lab actively contributes to open science through GitHub repositories and code releases. He also serves as an associate or editor for several IEEE Transactions journals and participates in program committees of major AI and communications conferences.
