Summary
Dr. Geoffrey Ye Li (Curriculum Vitae) is a Chair Professor at Imperial College London, UK. Before joining Imperial in 2020, he was a Professor at Georgia Institute of Technology, USA, for 20 years and a Principal Technical Staff Member with AT&T Labs – Research (previous Bell Labs) in New Jersey, USA, for five years. He made fundamental contributions to orthogonal frequency division multiplexing for wireless communications, established a framework on resource cooperation in wireless networks, and introduced deep learning to communications. In these areas, he has published over 600 journal and conference papers in addition to over 40 granted patents. His publications have been cited over 67,000 times with an H-index of 118 according to Google Scholar. He has been listed as a Highly Cited Researcher by Clarivate/Web of Science almost every year.
Dr. Geoffrey Ye Li (Curriculum Vitae) was elected to IEEE Fellow and IET Fellow for his contributions to signal processing for wireless communications. He won 2024 IEEE Eric E. Sumner Award, 2019 IEEE ComSoc Edwin Howard Armstrong Achievement Award, and several awards from IEEE Signal Processing, Vehicular Technology, and Communications Societies.
Email: Geoffrey.Li@Imperial.ac.uk
Website: https://www.imperial.ac.uk/intelligent-transmission-and-processing-laboratory
He is currently focusing on deep learning for signal processing and wireless communications.
Selected Publications
Journal Articles
Zhang B, Qin Z, Li GY, 2023, Semantic Communications With Variable-Length Coding for Extended Reality, IEEE Journal of Selected Topics in Signal Processing, Vol:17, ISSN:1932-4553, Pages:1038-1051
Wang O, Gao J, Li GY, 2023, Learn to Adapt to New Environments From Past Experience and Few Pilot Blocks, Ieee Transactions on Cognitive Communications and Networking, Vol:9, Pages:373-385
Zhou S, Li GY, 2023, Federated learning via inexact ADMM, IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN:0162-8828
Ye H, Liang L, Li GY, 2022, Decentralized Federated Learning With Unreliable Communications, IEEE Journal of Selected Topics in Signal Processing, Vol:16, ISSN:1932-4553, Pages:487-500
Xie H, Qin Z, Li G, et al., 2021, Deep learning enabled semantic communication systems, IEEE Transactions on Signal Processing, Vol:69, ISSN:1053-587X, Pages:2663-2675
Liang L, Ye H, Yu G, et al., 2020, Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks, Proceedings of the IEEE, Vol:108, ISSN:0018-9219, Pages:341-356
He H, Wen C-K, Jin S, et al., 2020, Model-Driven Deep Learning for MIMO Detection, IEEE Transactions on Signal Processing, Vol:68, ISSN:1053-587X, Pages:1702-1715
Ye H, Li GY, Juang BHF, 2019, Deep Reinforcement Learning Based Resource Allocation for V2V Communications, IEEE Transactions on Vehicular Technology, Vol:68, ISSN:0018-9545, Pages:3163-3173
Qin Z, Ye H, Li GY, et al., 2019, Deep learning in physical layer communications, IEEE Wireless Communications, Vol:26, ISSN:1536-1284, Pages:93-99
Ye H, Li GY, Juang BH, 2018, Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems, Ieee Wireless Communications Letters, Vol:7, ISSN:2162-2337, Pages:114-117