Jun Zhang
 |
Associate Professor, IEEE Fellow Distinguished Lecturer, IEEE Communications Society Office: Room 2430 |
What's new
(Student Awards) Congratulations to Yuanfang, Yufei, Yuchang, Xinyu, Ruiqi, and Jiawei!
(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]
(Hong Kong 6G Summit) The IEEE Hong Kong 6G Wireless Summit (IEEE HK6GWS 2024), 11-12 September 2024. [Website]
(Vista) “Vista: A generalizable driving world model with high fidelity and versatile controllability,” submitted. [Paper] [Code] [Demo]
(WirelessLLM) “WirelessLLM: Empowering large language models towards wireless intelligence,” to appear. [Paper]
(ECCV 24) Two papers accepted to ECCV 2024.
(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.
(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
 |
Our Group WeChat Account (in Chineses) |
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, vol. 9, no. 2, pp. 99-112, June 2024. [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,” 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. 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] [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]
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, vol. 23, no. 6, pp. 7180-7195, June 2024. [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]
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]