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

Openings

There are multiple openings of research assistant professor (RAP)/postdocs/research assistants in our group in the following areas [Opening]:

  • AI agent for wireless networks

  • Multi-modality large models for wireless networks

  • Device-edge-cloud collaboration for large language models

  • Security and privacy issues of large models for wireless networks

Experience in both wireless communication and deep learning is needed. Please send your CV to eejzhang@ust.hk. To apply for a postdoc position, please also provide 3 representative publications. It requires at least one year of postdoc experience to apply for the RAP position.

What's new

  • (Student Awards) Congratulations to Yuanfang, Yufei, Yuchang, Xinyu, Ruiqi, and Jiawei!

    • (Aug 2024) Yuanfang received the Hong Kong PhD Fellowship.

    • (July 2024) Yuanfang and Yufei received the HKUST RedBird PhD Award.

    • (July 2024) Yuchang received the HKUST RedBird Academic Excellence Award for Continuing PhD Students.

    • (May 2024) Yuchang and Xinyu passed their thesis exams. Congratulations, Dr. Sun and Dr. Bian!

    • (May 2024) Ruiqi received the Hong Kong PhD Fellowship.

    • (March 2024) Jiawei received the School of Engineering (SENG) PhD Research Excellence Award 2023-24! [SENG News]

  • (HarmoniCa) “HarmoniCa: Harmonizing training and inference for better feature cache in diffusion transformer acceleration,” preprint. [Paper]

  • (MEGA) “MEGA: Memory-efficient 4D Gaussian splatting for dynamic scenes,” preprint. [Paper]

  • (GI-GS) “GI-GS: Global illumination decomposition on Gaussian splatting for inverse rendering,” preprint. [Paper] [Project Page]

  • (EVA-Gaussian) “EVA-Gaussian: 3D Gaussian-based real-time human novel view synthesis under diverse camera settings,” preprint. [Paper] [Project Page]

  • (ReconX) “ReconX: Reconstruct any scene from sparse views with video diffusion model,” preprint. [Paper] [Code] [Project Page]

  • (WirelessAgent) “WirelessAgent: Large language model agents for intelligent wireless networks,” submitted. [Paper]

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

  • (NeurIPS 24) Two papers accepted to NeurIPS 2024.

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

    • “Kaleidoscope: Learnable masks for heterogeneous multi-agent reinforcement learning” [Paper] [Code]

  • (ECCV 24) Two papers accepted to ECCV 2024.

    • “GaussianImage: 1000 FPS image representation and compression by 2D Gaussian splatting” [Paper] [Code]

    • “Bidirectional stereo image compression with cross-dimensional entropy model” [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]

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

Research Interests

  • Generative AI, Foundation Models

  • Neural Data Representation and Compression

  • Reinforcement Learning

  • Safe and Trustworthy AI

  • O-RAN, Edge AI Systems

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Our Group WeChat Account (in Chineses)
Briefing results of our group.

Selected Publications

  • GenAI, Foundation Models, and Applications

    • Y. Huang, Z. Wang, R. Gong, J. Liu, X. Zhang, J. Guo, X. Liu, and J. Zhang, “HarmoniCa: Harmonizing training and inference for better feature cache in diffusion transformer acceleration,” preprint. [Paper]

    • F. Liu*, W. Sun*, H. Wang*, Y. Wang, H. Chen, J. Ye, J. Zhang, and Y. Duan, “ReconX: Reconstruct any scene from sparse views with video diffusion model,” preprint. (* equal contribution) [Paper] [Code] [Project Page]

    • 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,” Advances in neural information processing systems (NeurIPS), Vancouver, Canada, Dec. 2024. [Paper] [Code] [Demo] (Acceptance Rate: 25.8%)

    • 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]

    • J. Tong, J. Shao, Q. Wu, W. Guo, Z. Li, Z. Lin, and J. Zhang, “WirelessAgent: Large language model agents for intelligent wireless networks,” submitted. [Paper]

    • 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, vol. 9, no. 2, pp. 99-112, June 2024. [Paper]

  • Neural Data Representation and Compression

    • X. Zhang, Z. Liu, Y. Zhang, X. Ge, D. He, T. Xu, Y. Wang, Z. Lin, S. Yan, and J. Zhang, “MEGA: Memory-efficient 4D Gaussian splatting for dynamic scenes,” preprint. [Paper]

    • H. Chen, Z. Lin, and J. Zhang, “GI-GS: Global illumination decomposition on Gaussian splatting for inverse rendering,” preprint. [Paper] [Project Page]

    • Y. Hu, Z. Liu, J. Shao, Z. Lin, and J. Zhang, “EVA-Gaussian: 3D Gaussian-based real-time human novel view synthesis under diverse camera settings,” preprint. [Paper] [Project Page]

    • 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,” European Conference on Computer Vision (ECCV), Milano, Italy, Sept.-Oct. 2024. (* equal contribution) (Acceptance Rate: 27.9%) [Paper] [Code]

    • Z. Liu, X. Zhang, J. Shao, Z. Lin, and J. Zhang, “Bidirectional stereo image compression with cross-dimensional entropy model,” European Conference on Computer Vision (ECCV), Milano, Italy, Sept.-Oct. 2024. (Acceptance Rate: 27.9%) [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] [Code]

    • 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

    • X. Li, L. Pan, and J. Zhang, “Kaleidoscope: Learnable masks for heterogeneous multi-agent reinforcement learning,” Advances in neural information processing systems (NeurIPS), Vancouver, Canada, Dec. 2024. (Acceptance Rate: 25.8%) [Paper] [Code]

    • 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] [Video]

    • 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]

  • 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, vol. 23, no. 7, pp. 7572-7584, July 2024. [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, vol. 23, no. 6, pp. 6731-6742, June 2024. [Paper] [Code]

    • 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,” Proc. IEEE, to appear. [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]