ZHANG, Jun

PhD, FIEEE
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Associate Professor, IEEE Fellow
Department of Electronic and Computer Engineering (ECE)
The Hong Kong University of Science and Technology (HKUST)

Office: Room 2430
Email: eejzhang@ust.hk
Phone: +852 2358-7050
[Google Scholar] [Resume]

Recruiting

  • I am actively recruiting PhD, postdoc and research assistant. Feel free to contact me if interested. [Opening]

What's new

  • I was elevated to IEEE Fellow, effective 1 January 2022 with the citation: “for contributions to dense wireless networks”.

  • (Tutorial) “Task-oriented Communication for Edge AI”, IEEE MeditCom 2021. [Slides]

  • (New Paper) “How neural architectures affect deep learning for communication networks?,” submitted. [Paper]

  • (New Paper) “How powerful is graph convolution for recommendation?” was accepted by ACM International Conference on Information and Knowledge Management (CIKM) 2021 (live spotlight presentation). [Paper]

  • (New Paper) “Learn to communicate with neural calibration: Scalability and generalization,” submitted. [Paper]

  • (New Paper) “Task-oriented communication for multi-device cooperative edge inference,” submitted. [Paper]

  • (New Paper) “Learning task-oriented communication for edge inference: An information bottleneck approach,” IEEE J. Select. Areas Commun, to appear. [Paper]

  • (New Paper) “Graph neural networks for scalable radio resource management: architecture design and theoretical analysis”, IEEE J. Select. Areas Commun, Jan. 2021. [Paper]

  • (Paper Award) Our paper “A Survey of Mobile Edge Computing: The Communication Perspective” received the 2021 Best Survey Paper Award of the IEEE Communications Society. [Award Information]

Research Interests

  • Edge AI and Edge Computing

    • Edge-assisted cooperative perception; task-oriented communication for edge AI

  • Wireless Communications and Networking

    • Machine learning for wireless communications; machine-type communications (URLLC, Massive connectivity)

  • Deep Learning and AI

    • Graph neural networks; multi-agent reinforcement learning; privacy-preserving collaborative learning

Selected Publications

  • Edge AI and Edge Computing

    • J. Shao, Y. Mao, and J. Zhang, “Task-oriented communication for multi-device cooperative edge inference,” submitted. [Paper]

    • J. Shao, Y. Mao, and J. Zhang, “Learning task-oriented communication for edge inference: An information bottleneck approach,” IEEE J. Select. Areas Commun, to appear. [Paper]

    • D. Liu, G. Zhu, Q. Zeng, J. Zhang, and K. Huang, “Wireless data acquisition for edge learning: Data-importance aware retransmission,” IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 406–420, Jan. 2021. [Paper]

    • J. Shao, J. Zhang, “Communication-computation trade-off in resource-constrained edge inference,” IEEE Commun. Mag., vol. 58, no. 12, pp. 20–26, Dec. 2020. [Paper] [GitHub]

    • Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2322-2358, 4th Quart. 2017. [Paper] (The 2021 Best Survey Paper Award of IEEE Communications Society)

    • Y. Mao, J. Zhang, and K. B. Letaief, “Dynamic computation offloading for mobile-edge computing with energy harvesting devices,” IEEE J. Select. Areas Commun. - Series on Green Commun. and Networking, vol. 34, no. 12, pp. 3590-3605, Dec. 2016. [Paper] (The 2019 IEEE Communications Society & Information Theory Society Joint Paper Award)

  • Deep Learning and AI

    • Y. Sun, J. Shao, Y. Mao, and J. Zhang, “Asynchronous semi-decentralized federated edge learning for heterogenous clients,” submitted.

    • L. Liu, J. Zhang, S.H. Song, and K. B. Letaief, “Communication-efficient federated distillation with active data sampling,” submitted.

    • L. Liu, J. Zhang, S.H. Song, and K. B. Letaief, “Hierarchical quantized federated learning: Convergence analysis and system design,” submitted. [Paper]

    • H. Wang, Y. Shen, Z. Wang, D. Li, J. Zhang, K. B. Letaief, and J. Lu, “Decentralized statistical inference with unrolled graph neural networks,” IEEE Conference on Decision and Control (CDC), Austin, TX, USA, Dec. 2021. (Invited Paper) [Paper]

    • Y. Shen, Y. Wu, Y. Zhang, C. Shan, J. Zhang, K. B. Letaief, and D. Li, “How powerful is graph convolution for recommendation?,” ACM International Conference on Information and Knowledge Management (CIKM), virtual conference, Nov. 2021. [Paper] (Acceptance Rate: 21.7%)

    • Y. Shen, Y. Xue, J. Zhang, K. B. Letaief, and V. Lau, “Complete Dictionary Learning via ell_p-norm Maximization,” Conference on Uncertainty in Artificial Intelligence (UAI) 2020, Toronto, Canada, Aug. 2020. [Paper] (Acceptance Rate: 27.5%)

  • Wireless Communications

    • Y. Shen, J. Zhang, and K. B. Letaief, “How neural architectures affect deep learning for communication networks?,” submitted. [Paper]

    • Y. Xue, Y. Shen, V. Lau, J. Zhang, and K. B. Letaief, “Blind data detection in massive MIMO via ell_3-norm maximization over the Stiefel manifold,” IEEE Trans. Wireless Commun., vol. 20, no. 2, pp. 1411–1424, Feb. 2021. [Paper]

    • Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “Graph neural networks for scalable radio resource management: architecture design and theoretical analysis,” IEEE J. Select. Areas Commun, vol. 39, no. 1, pp. 101–115, Jan. 2021. [Paper]

    • Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “LORM: Learning to optimize for resource management in wireless networks with few training samples,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 665–679, Jan. 2020. [Paper]

    • X. Yu, C. Li, J. Zhang, M. Haenggi, and K. B. Letaief, “A unified framework for the tractable analysis of multi-antenna wireless networks,” IEEE Trans. Wireless Commun., vol. 17, no. 12, pp. 7965-7980, Dec. 2018. [Paper]

    • X. Yu, J.-C. Shen, J. Zhang, and K. B. Letaief, “Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems,” IEEE J. Sel. Topics Signal Process., vol. 10, no. 3, pp. 485-500, Apr. 2016. [Paper] [Codes] (The 2018 IEEE Signal Processing Society Young Author Best Paper Award)

    • Y. Shi, J. Zhang, and K. B. Letaief, “Group sparse beamforming for green Cloud-RAN,” IEEE Trans. Wireless Commun., vol. 13, no. 5, pp. 2809-2823, May 2014. [Paper] [Codes] (The 2016 Marconi Prize Paper Award)