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Jingyang (William) Zhu | 朱敬阳

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Hello everyone! I received my Ph.D. at School of Information Science and Technology (SIST), ShanghaiTech University advised by Prof. Yuanming Shi, majoring in Computer Science (CS).

I am currently a researcher in AI-Flow, the Institue of Artificial Intelligence, China Telecom (TeleAI), under the direction of Prof. Jiaowei Shao and Prof. Xuelong Li (Chief Technology Officer and Chief Scientist of China Telecom, and the director of TeleAI).

I was also in internship with the Nokia Bell Labs, Shanghai, China, May to September, 2025.

Earlier, I received my bachelor's degree in Electronic and Information Engineering from School of Electronic Engineering (SEE), Xidian University, in 2021.

My current research interest lies in edge large AI models (LAMs), in particular end-edge-cloud collaborative LAM inference and compression as well as their applications to satellite edge AI and space computing power networks [Google Scholar].

Email: zhujy38@chinatelecom.cn

Address: Floor 29, Building F1, International Media Port, No. 199 Longwen Road, Xuhui District, Shanghai, China

Research

My research interests include

  • Distributed Learning and Optimization.

  • Edge Large AI Models.

  • End-Edge-Cloud Collaborative Training and Inference.

  • Space Computing Power Networks.

Services

  • Reviewer of Journals

  1. IEEE Transactions on Mobile Computing

  2. IEEE Internet of Things Journal

  3. IEEE Transactions on Communications

  4. IEEE Transactions on Wireless Communications

  5. IEEE Journal of Selected Topics in Signal Processing

  6. IEEE Transactions on Aerospace and Electronic Systems

  7. IEEE Open Journal of the Communications Society

  8. IEEE Wireless Communications Letters

Awards

  • Shanghai Municipal Outstanding Graduate 2026.

  • ShanghaiTech University Outstanding Graduate 2026.

Accepted Preprints and Proceedings

  1. J. Zhu, J. Zhu, Y. Bai, Y. Wu, T. Wang, Y. Shi, W. Chen, K. B. Letaief, "Robust Information Bottleneck for Satellite Edge Inference over MIMO Channel," IEEE Trans. Wireless Commun., early access, Aug. 2026.

  2. 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.

  3. 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.

Publications (by year)

[1] 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, England. [pdf]

[2] 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, England. [pdf]

[3] 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, Nov. 2025. [pdf]

[4] 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, Sept. 2025. [pdf]

[5] 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, pp. 1 - 15, Jul. 2025. [pdf]

[6] 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]

[7] 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]

[8] R. Li, J. Zhu, Y. Mao, Y. Shi, T. Wang, 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]

[9] 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 Conf. Commun. (GLOBECOM), Dec.2024, Cape Town, South Africa. [pdf]

[10] 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]

[11] 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. 10, no. 11, pp. 16996 - 17020, May 2024. [pdf] [code]

[12] 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]

[13] 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]

[14] 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]

[15] 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), Online, 2021. [pdf]