Jun Zhang

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

Distinguished Lecturer, IEEE Communications Society

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

What's new

  • (Vista) “Vista: A generalizable driving world model with high fidelity and versatile controllability,” submitted. [Paper] [Code] [Demo]

  • (GaussianImage) “GaussianImage: 1000 FPS image representation and compression by 2D Gaussian splatting,” submitted. [Paper]

  • (WirelessLLM) “WirelessLLM: Empowering large language models towards wireless intelligence,” to appear. [Paper]

  • (ICML 24) “Individual contributions as intrinsic exploration scaffolds for multi-agent reinforcement learning”, accepted by ICML 2024. [Paper] [Code]

  • (CVPR 24) Three papers accepted to CVPR 2024.

    • “Generalized predictive model for autonomous driving” (Highlight, Top 2.8%) [Paper]

    • “Boosting neural representations for videos with a conditional decoder” (Highlight, Top 2.8%) [Paper]

    • “Task-aware encoder control for deep video compression” [Paper]

  • (AAMAS 24) “Context-aware communication for multi-agent reinforcement learning,” accepted by AAMAS 2024. [Paper]

  • (Nature Communications) Paper “Selective knowledge sharing for privacy-preserving federated distillation without a good teacher” was accepted by Nature Communications. [Paper]

  • (EdgeGPT) Our EdgeGPT paper “Large language models empowered autonomous edge AI for connected intelligence” was accepted by IEEE Communications Magazine. [Paper]

Research Interests

  • Generative AI, Foundation Models

  • Neural Data Representation and Compression

  • Reinforcement Learning

  • Safe and Trustworthy AI

  • O-RAN, Edge AI Systems

Selected Publications

  • GenAI, Foundation Models, and Applications

    • Z. Li, Q. Guo, J. Shao, L. Song, J. Bian, Jun Zhang, and R. Wang, “Graph neural network enhanced retrieval for question answering of LLMs,” submitted. [Paper]

    • S. Gao, J. Yang, L. Chen, K. Chitta, Y. Qiu, A. Geiger, J. Zhang, and H. Li, “Vista: A generalizable driving world model with high fidelity and versatile controllability,” submitted. [Paper] [Code] [Demo]

    • W. Sun, Z. Wang, S. Chen, Y. Wang, Z. Chen, J. Zhu, and J. Zhang, “Freeplane: Unlocking free lunch in triplane-based sparse-view reconstruction models,” submitted. [Paper] [Project Page]

    • J. Shao, J. Tong, Q. Wu, W. Guo, Z. Li, Z. Lin, and Jun Zhang, “WirelessLLM: Empowering large language models towards wireless intelligence,” Journal of Communications and Information Networks, to appear. [Paper]

    • J. Yang*, S. Gao*, Y. Qiu*, L. Chen, T. Li, B. Dai, K. Chitta, P. Wu, J. Zeng, J. Zhang, A. Geiger, Y. Qiao, and H. Li, “Generalized predictive model for autonomous driving,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, Jun. 2024. (* equal contribution) (Acceptance Rate: 23.6%) (Highlight, Top 2.8%) [Paper]

    • Y. Shen, J. Shao, X. Zhang, Z. Lin, H. Pan, D. Li, J. Zhang, K. B. Letaief, “Large language models empowered autonomous edge AI for connected intelligence,” IEEE Commun. Mag., to appear. [Paper]

  • Neural Data Representation and Compression

    • X. Zhang*, X. Ge*, T. Xu, D. He, Y. Wang, H. Qin, G. Lu, J. Geng, and J. Zhang, “GaussianImage: 1000 FPS image representation and compression by 2D Gaussian splatting,” submitted. (* equal contribution) [Paper]

    • X. Zhang, S. Gao, Z. Liu, X. Ge, D. He, T. Xu, Y. Wang, and J. Zhang, “Content-aware masked image modeling transformer for stereo image compression,” submitted. [Paper]

    • X. Zhang, R. Yang, D. He, X. Ge, T. Xu, Y. Wang, H. Qin, and J. Zhang, “Boosting neural representations for videos with a conditional decoder,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, Jun. 2024. (Acceptance Rate: 23.6%) (Highlight, Top 2.8%) [Paper]

    • X. Ge, J. Luo, X. Zhang, T. Xu, G. Lu, D. He, J. Geng, Y. Wang, J. Zhang, and H. Qin, “Task-aware encoder control for deep video compression,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, Jun. 2024. (Acceptance Rate: 23.6%) [Paper]

    • X. Zhang, J. Shao, and J. Zhang, “LDMIC: Learning-based distributed multi-view image coding,” International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023. [Paper] [GitHub]

  • Reinforcement Learning

    • Z. Liu, X. Li. S. Chen, G. Li, J. Jiang, and J. Zhang, “Reinforcement learning with intrinsically motivated feedback graph for lost-sales inventory control,” submitted. [Paper]

    • X. Li, Z. Liu, S. Chen, and J. Zhang, “Individual contributions as intrinsic exploration scaffolds for multi-agent reinforcement learning,” International Conference on Machine Learning (ICML), Vienna, Austria, July 2024. (Acceptance Rate: 27.5%) [Paper] [Code]

    • X. Li, J. Zhang, “Context-aware communication for multi-agent reinforcement learning,” International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Auckland, New Zealand, May 2024. (Acceptance Rate: 25%) [Paper]

    • X. Wang*, X. Li*, J. Shao, and J. Zhang, “AC2C: Adaptively controlled two-hop communication for multi-agent reinforcement learning,” International Conference on Autonomous Agents and Multiagent Systems (AAMAS), London, United Kingdom, May-June 2023. (Acceptance Rate: 23.3%) (* equal contribution) [Paper]

    • Y. Zhang, Z. Yu, J. Zhang, L. Wang, T. Luan, B. Guo, and C. Yuen, “Learning decentralized traffic signal controllers with multi-agent graph reinforcement learning,” IEEE Trans. Mobile Computing, to appear. [Paper]

  • Federated Learning, Safe/Robust AI

    • J. Shao, Z. Li, W. Sun, T. Zhou, Y. Sun, L. Liu, Z. Lin, and J. Zhang, “A survey of what to share in federated learning: Perspectives on model utility, privacy leakage, and communication efficiency,” submitted. [Paper]

    • J. Shao, F. Wu, and J. Zhang, “Selective knowledge sharing for privacy-preserving federated distillation without a good teacher,” Nature Communications, Jan 2024. [Paper]

    • Y. Sun, Y. Mao, and J. Zhang, “MimiC: Combating client dropouts in federated learning by mimicking central updates,” IEEE Trans. Mobile Computing, to appear. [Paper]

    • T. Zhou, J. Zhang, and D. Tsang, “FedFA: Federated learning with feature anchors to align feature and classifier for heterogeneous data,” IEEE Trans. Mobile Computing, to appear. [Paper]

    • B. Li*, Y. Shen*, J. Yang, Y. Wang, J. Ren, T. Che, J. Zhang, and Z. Liu, “Sparse Mixture-of-Experts are Domain Generalizable Learners,” International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023. (Oral presentation) [Paper] [GitHub]

    • J. Shao, Y. Sun, S. Li, and J. Zhang, “DReS-FL: Dropout-resilient secure federated learning for non-IID clients via secret data sharing,” NeurIPS 2022. [Paper] (Acceptance Rate: 25.6%)

  • Edge AI and Edge Computing

    • Y. Mao, X. Yu, K. Huang, A.-Y. Zhang, and J. Zhang, “Green edge AI: A contemporary survey,” submitted. [Paper]

    • J. Shao, X. Zhang, and J. Zhang, “Task-oriented communication for edge video analytics,” IEEE Trans. Wireless Commun., vol. 23, no. 5, pp. 4141-4154, May 2024. [Paper] [GitHub]

    • J. Shao, Y. Mao, and J. Zhang, “Task-oriented communication for multidevice cooperative edge inference,” IEEE Trans. Wireless Commun., vol. 11, no. 1, pp. 73-87, Jan. 2023. [Paper]

    • J. Shao, Y. Mao, and J. Zhang, “Learning task-oriented communication for edge inference: An information bottleneck approach,” IEEE J. Select. Areas Commun, vol. 40, no. 1, pp. 197-211, Jan. 2022. [Paper] [GitHub]

    • J. Zhang and K. B. Letaief, “Mobile edge intelligence and computing for the Internet of vehicles,” Proc. IEEE, vol. 108, no. 2, pp. 246–261, Feb. 2020. [Paper]

  • Integrated AI and Communications

    • W. Yu, H. He, X. Yu, S. Song, J. Zhang, R. D. Murch, and K. B. Letaief, “Bayes-optimal unsupervised learning for channel estimation in near-field holographic MIMO,” IEEE J. Sel. Topics Signal Process., to appear. [Paper]

    • W. Yu, Y. Shen, H. He, X. Yu, S.H. Song, J. Zhang, and K. B. Letaief, “An adaptive and robust deep learning framework for THz ultra-massive MIMO channel estimation,” IEEE J. Sel. Topics Signal Process., vol. 17, no. 4, pp. 761-776, July 2023. [Paper] [GitHub]

    • Y. Shen, J. Zhang, S.H. Song, and K. B. Letaief, “Graph neural networks for wireless communications: From theory to practice,” IEEE Trans. Wireless Commun., vol. 22, no. 5, pp. 3554-3569, May 2023. [Paper] [GitHub]

    • 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]