Jun Zhang
 |
Associate Professor, IEEE Fellow Distinguished Lecturer, IEEE Communications Society Office: Room 2430 |
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]
(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.
(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.
(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
 |
Our Group WeChat Account (in Chineses) |
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
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%)
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]