PhD (Zhejiang University)

BE (Zhejiang University)

Jinke Ren received the B.Eng. and Ph.D. degrees from Zhejiang University, Hangzhou, China. From 2019 to 2021, he was a Joint-Training Ph.D. student in the Department of Electrical and Computer Engineering at Northwestern University, Evanston, IL, USA. He is currently a Research Assistant Professor with the Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen), the School of Science and Engineering (SSE), and the Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong, Shenzhen, China. His current research interests are in AI networking, blockchains, and machine learning with applications to Internet-of-things and wireless networks.

Dr. Ren has been serving as a Reviewer for over 20 international journals and conferences, such as IEEE Transactions on Mobile Computing, IEEE Journal on Selected Areas in Communications, and IEEE Transactions on Wireless Communications. He was a recipient of the Meritorious Winner Award of the International Mathematical Contest in Modeling (MCM) in 2016, the Exemplary Reviewer Award in IEEE Transactions on Communications in 2020 (Top 2%), and the Second Prize Award in China Robotics and Artificial Intelligence Competition (Guangdong) in 2024. He was listed in the World’s Top 2% Scientists by Stanford University and Elsevier for three consecutive years (2022-2024).

1. J. Ren, G. Yu, Y. He, and G. Y. Li, “Collaborative cloud and edge computing for latency minimization,” IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 5031-5044, May 2019. [ESI Highly Cited Paper]
2. J. Ren, G. Yu, Y. Cai, and Y. He, “Latency optimization for resource allocation in mobile-edge computation offloading,” IEEE Transactions on Wireless Communications, vol. 17, no. 8, pp. 5506-5519, Aug. 2018. [ESI Highly Cited Paper]
3. J. Ren, Z. Zhang, J. Xu, G. Chen, Y. Sun, P. Zhang, and S. Cui, “Knowledge base enabled semantic communication: A generative perspective,” IEEE Wireless Communications, vol. 31, no. 4, pp. 14-22, Aug. 2024.
4. J. Ren, G. Yu, and G. Ding, “Accelerating DNN training in wireless federated edge learning systems,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 1, pp. 219-232, Jan. 2021. [CCF-A]
5. J. Ren, Y. He, D. Wen, G. Yu, K. Huang, and D. Guo, “Scheduling for cellular federated edge learning with importance and channel awareness,” IEEE Transactions on Wireless Communications, vol. 19, no. 11, pp. 7690-7703, Nov. 2020.
6. J. Ren, Y. He, G. Huang, G. Yu, Y. Cai, and Z. Zhang, “An edge-computing based architecture for mobile augmented reality,” IEEE Network, vol. 33, no. 4, pp. 162-169, Jul./Aug. 2019.
7. J. Ren, Y. Ruan, and G. Yu, “Data transmission in mobile edge networks: Whether and where to compress?” IEEE Communications Letters, vol. 23, no. 3, pp. 490-493, Mar. 2019.
8. J. Ren, Y. He, G. Yu, and G. Y. Li, “Joint communication and computation resource allocation for cloud-edge collaborative system,” in Proc. IEEE Conference on Wireless Communications and Networking (WCNC), Marrakesh, Morocco, Apr. 2019, pp. 1-6.
9. J. Ren, G. Yu, Y. Cai, Y. He, and F. Qu, “Partial offloading for latency minimization in mobile-edge computing,” in Proc. IEEE Global Communications Conference (GLOBECOM), Singapore, Dec. 2017, pp. 1-6.
10. Y. Lin, Z. Gao, H. Du, D. Niyato, G. Gui, S. Cui, and J. Ren# (corresponding author), “Scalable federated unlearning via isolated and coded sharding,” in Proc. International Joint Conference on Artificial Intelligence (IJCAI), Jeju, South Korea, Aug. 2024. [CCF-A]