PhD (Stanford University)

ME (McMaster University)

BE (Beijing University of Posts and Telecommunications)

Shuguang Cui received his Ph.D. in Electrical Engineering from Stanford University, California, USA, in 2005. Afterwards, he has been working as assistant, associate, full, Chair Professor in Electrical and Computer Engineering at the Univ. of Arizona, Texas A&M University, UC Davis, and CUHK at Shenzhen respectively. His current research interests focus on the merging between AI and communication networks. He was selected as the Thomson Reuters Highly Cited Researcher and listed in the Worlds’ Most Influential Scientific Minds by ScienceWatch in 2014. He was the recipient of the IEEE Signal Processing Society 2012 Best Paper Award. He was also the Vice Chair of the IEEE VT Fellow Evaluation Committee and a member of the IEEE ComSoc Award Committee. He was elected as an IEEE Fellow in 2013, an IEEE ComSoc Distinguished Lecturer in 2014, and IEEE VT Society Distinguished Lecturer in 2019. In 2020, he won the IEEE ICC best paper award, ICIP best paper finalist, the IEEE Globecom best paper award. In 2021, he won the IEEE WCNC best paper award. In 2023, he won the IEEE Marconi Best Paper Award, got elected as a Fellow of both Canadian Academy of Engineering and the Royal Society of Canada, and started to serve as the Editor-in-Chief for IEEE Transactions on Mobile Computing.

Selected List:

1. M. Chen, H. V. Poor, W. Saad, and S. Cui “Convergence Time Optimization for Federated Learning over Wireless Networks,” IEEE Transactions on Wireless Communications, 2020. (Highly Cited Paper)
2. S. Zhang, S. Cui, and Z. Ding, “Hypergraph based Image Processing,” 2020 IEEE International Conference on Image Processing (ICIP), Oct. 2020. (Paper Award Finalist)
3. Y. Hu, M. Chen, W. Saad, H. V. Poor, and S. Cui, “Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks,” IEEE GLOBECOM, Taiwan, Dec. 2020. (IEEE Globecom Best Paper Award)
4. Y. Ma, Y. Gao, C. Fu, W. Rong, Z. Xiong, and S. Cui, "TV White Space Spectrum Analysis Based on Machine Learning," Journal of Communications and Information Networks, Vol. 4, No. 2, pp. 68-80. Jun. 2019. (Best Paper Award)
5. C. Liu, C. Guo, Y. Yang, M. Chen, H. V. Poor, and S. Cui, "Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC\RF Systems,"IEEE Wireless Communications and Networking Conference (WCNC), Nanjing,China, March 2021. (Best Paper Award)
6. M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, S. Cui, “A Joint Learning and Communications Framework for Federated Learning over Wireless Networks,” IEEE Transactions on Wireless Communications, 2020. (Marconi Prize Paper Award)
7. M. Chen, N. Shlezinger, H. V. Poor, Y. C. Eldar, and S. Cui, "Communication-efficient federated learning," Proceedings: National Academy of Sciences of the United States of America (PNAS), vol. 118, no. 17, Apr. 2021.
8. X. Cao, F. Wang, J. Xu, and Shuguang Cui, “Joint Computation and Communication Cooperation for Energy-Efficient Mobile Edge Computing,” IEEE Internet of Things Journal, Vol. 6, No. 3, pp. 4188-4200, June 2019. (Highly Cited Paper)
9. Z. Wang, L. Liu, S. Cui, “Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications: Framework, Algorithms, and Analysis,” IEEE Transactions on Wireless Communications, Vol. 19, No. 10, pp. 6607-6620, Oct. 2020 (Highly Cited Paper)