PROFILE / Jingyang Zhu
About
01About me
I received my Ph.D. in Computer Science at the School of Information Science and Technology (SIST), ShanghaiTech University, advised by Prof. Yuanming Shi.
I am a researcher in AI-Flow at the Institute of Artificial Intelligence, China Telecom (TeleAI), under the direction of Prof. Jiawei Shao and Prof. Xuelong Li. Prof. Li is Chief Technology Officer and Chief Scientist of China Telecom, and the director of TeleAI.
I interned at Nokia Bell Labs, Shanghai, from May to September 2025.
I received my bachelor's degree in Electronic and Information Engineering from the School of Electronic Engineering, Xidian University, in 2021.
My research interest lies in edge large AI models, in particular end-edge-cloud collaborative inference and compression, and their applications to satellite edge AI and space computing power networks.
AddressFloor 29, Building F1, International Media Port, No. 199 Longwen Road, Xuhui District, Shanghai, China
02Research
- Distributed learning and optimization
- Edge large AI models
- End-edge-cloud collaborative training and inference
- Space computing power networks
03Services
- IEEE Transactions on Mobile Computing
- IEEE Internet of Things Journal
- IEEE Transactions on Communications
- IEEE Transactions on Wireless Communications
- IEEE Journal of Selected Topics in Signal Processing
- IEEE Transactions on Aerospace and Electronic Systems
- IEEE Open Journal of the Communications Society
- IEEE Wireless Communications Letters
04Awards
- Shanghai Municipal Outstanding Graduate, 2026
- ShanghaiTech University Outstanding Graduate, 2026
05Accepted
- Y. Feng, J. Zhu, X. Liu, Y. Zhou, T. Wang, and Y. Shi, “LLM-Enhanced Edge Embodied Agents via Reward Shaping and Preference Regularization,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2026, Macau, China.
- J. Zhu, J. Zhu, Y. Bai, Y. Wu, Y. Shi, and W. Chen, “Robust Task-Oriented Communication for Cooperative Multi-View SAGIN Edge Inference,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2026, Macau, China.
06Publications
2026
- H. Yang, S. Xia, J. Zhu, Y. Wu, Y. Zhou, and Y. Shi, “Provable Guarantees for Over-the-Air Federated Learning with Polynomial Neural Networks,” IEEE Trans. Wireless Commun., early access, Sept. 2026. [pdf]
- Y. Shi, J. Zhu*, Y. Zhou, T. Wang, and C. Jiang, “Satellite Edge Artificial Intelligence with Agents: Architectures and Technologies,” Scientia Sinica Informationis, published online, Sept. 2026 (in Chinese). [pdf]
- J. Zhu, J. Zhu, Y. Bai, Y. Wu, T. Wang, Y. Shi, W. Chen, and K. B. Letaief, “Robust Information Bottleneck for Satellite Edge Inference Over MIMO Channel,” IEEE Trans. Wireless Commun., vol. 25, pp. 22097-22113, 2026. [pdf]
- L. Zeng, J. Zhu, Z. Wang, Y. Shi, and K. B. Letaief, “Unseen Cost of Space Computing: Quantifying LEO Battery Aging via Physics-Driven Modeling,” in Proc. IEEE Int. Conf. Commun. (ICC), May 2026, Glasgow, Scotland. [pdf]
- R. Li, J. Zhu, Y. Shi, L. Kuang, and C. Jiang, “Asynchronous Satellite Federated Learning with Intermittent Ground-to-Satellite Links,” in Proc. IEEE Int. Conf. Commun. (ICC), May 2026, Glasgow, Scotland. [pdf]
- Y. Zhu, J. Zhu, T. Wang, Y. Shi, C. Jiang, and K. B. Letaief, “Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks,” IEEE Trans. Wireless Commun., vol. 25, pp. 6600-6616, 2026. [pdf]
2025
- Z. Wang, Y. Shi, Y. Zhou, J. Zhu, and K. B. Letaief, “Edge Large AI Models: Revolutionizing 6G Networks,” IEEE Commun. Mag., vol. 63, no. 10, pp. 36-42, Oct. 2025. [pdf]
- J. Zhu, Y. Shi, Y. Zhou, C. Jiang, and L. Kuang, “Hierarchical Learning and Computing over Space-Ground Integrated Networks,” IEEE Trans. Mobile Comput., vol. 24, no. 10, pp. 10423-10440, Oct. 2025. [pdf][code]
- Y. Shi, J. Zhu*, C. Jiang, L. Kuang, and K. B. Letaief, “Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologies,” Sci. China Inf. Sci., vol. 68, no. 7, Art. no. 170302, Jul. 2025. [pdf]
- Z. Yang, P. Zhang, J. Zhu, D. Wen, Y. Shi, and W. Chen, “Hierarchical Federated Learning with Integrated Sensing-Communication-Computation over Space-Air-Ground Integrated Networks,” in Proc. IEEE Int. Conf. Commun. (ICC), Jun. 2025, Montreal, Canada. [pdf]
- R. Li, J. Zhu, Y. Mao, Y. Shi, T. Wang, and C. Jiang, “Topology-Aware Routing for Federated Learning Over Multi-Layer Satellite Networks,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Mar. 2025, Milan, Italy. [pdf]
2024
- Y. Zhu, P. Yang, J. Zhu, D. Wen, T. Wang, Y. Zhou, and C. Jiang, “Satellite Federated Fine-Tuning for Foundation Models: Architecture Design and System Optimization,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2024, Cape Town, South Africa. [pdf]
- Y. Shi, L. Zeng, J. Zhu, Y. Zhou, C. Jiang, and K. B. Letaief, “Satellite Federated Edge Learning: Architecture Design and Convergence Analysis,” IEEE Trans. Wireless Commun., vol. 23, no. 10, pp. 15212-15229, Oct. 2024. [pdf]
- J. Zhu, Y. Shi, Y. Zhou, C. Jiang, W. Chen, and K. B. Letaief, “Over-the-Air Federated Learning and Optimization,” IEEE Internet Things J., vol. 11, no. 10, pp. 16996-17020, May 2024. [pdf][code]
- J. Zhu, Y. Shi, M. Fu, Y. Zhou, Y. Wu, and L. Fu, “Latency Minimization for Wireless Federated Learning with Heterogeneous Local Model Updates,” IEEE Internet Things J., vol. 11, no. 1, pp. 444-461, Jan. 2024. [pdf]
2023
- Y. Wang, J. Zhu, Y. Mao, D. Wen, X. Tian, and Y. Shi, “Hierarchical Federated Edge Learning over Space-Air-Ground Integrated Networks,” in Proc. IEEE GLOBECOM Workshops (GC Wkshps), Dec. 2023, Kuala Lumpur, Malaysia. [pdf]
- J. Zhu, Y. Shi, M. Fu, Y. Zhou, Y. Wu, and L. Fu, “Latency Minimization for Wireless Federated Learning with Heterogeneous Local Updates,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Mar. 2023, Glasgow, Scotland. [pdf]
2021
- S. Xia, J. Zhu, Y. Yang, Y. Zhou, Y. Shi, and W. Chen, “Fast Convergence Algorithm for Analog Federated Learning,” in Proc. IEEE Int. Conf. Commun. (ICC), Jun. 2021, Online. [pdf]
* Corresponding author