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Jun ZhangProfessor, IEEE Fellow Associate Director Office: Room 2430 |
What's new
Student Awards: Congratulations to Jiawei, Xinran, Zhening, Hongze, Zifan, Xinjie, and Tailin!
(August 2025) Jiawei received the IEEE Communications Society Katherine Johnson Young Author Best Paper Award for paper, “Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 1, pp. 197-211, Jan. 2022. [Award Information]
(August 2025) Zhening received The ECE Best TA Award 2024/25.
(August 2025) Xinran, Zhening, Hongze, and Zifan received the HKUST RedBird Academic Excellence Award for Continuing PhD Students.
(May/July 2025) Xinjie and Tailin passed their thesis exams. Congratulations, Dr. Zhang and Dr. Zhou!
Call for Papers
The IEEE Wireless Communications and Networking Conference (WCNC) 2026, submission deadline: 14 September 2025. [Call for Papers]
Workshop on “AI and ML for Next-Generation Wireless Communications and Networking,” NeurIPS’25, submission deadline: 22 August 2025. [Website]
Special Collection on “AI-Driven Wireless Channel Modeling and Prediction,” npj Wireless Technology, submission deadline: 31 March 2026. [Website]
Recent Research Results
(VLMQ) “VLMQ: Efficient post-training quantization for large vision-language models via Hessian augmentation,” preprint. [Paper]
(RealPlay) “From virtual games to real-world play,” preprint. [Paper] [Project Page]
(UniFork) “UniFork: Exploring modality alignment for unified multimodal understanding and generation,” preprint. [Paper]
(Multi-agent LLMs) “Learn as Individuals, Evolve as a Team: Multi-agent LLMs adaptation in embodied environments,” preprint. [Paper]
(SenseFlow) “SenseFlow: Scaling distribution matching for flow-based text-to-image distillation,” preprint. [Paper]
(QVGen) “QVGen: Pushing the limit of quantized video generative models,” preprint. [Paper]
(ICCV 25) Two papers accepted by ICCV 2025.
“DimensionX: Create any 3D and 4D scenes from a single image with controllable video diffusion” [Paper] [Code] [Project Page]
“MEGA: Memory-efficient 4D Gaussian splatting for dynamic scenes” (Highlight) [Paper]
(ICML 25) Three papers accepted by ICML 2025.
“AdaWorld: Learning adaptable world models with latent actions” [Project Page]
“HarmoniCa: Harmonizing training and inference for better feature cache in diffusion transformer acceleration” [Paper]
“C2IQL: Constraint-conditioned implicit Q-learning for safe offline reinforcement learning”
(ICLR 25) Two papers accepted by ICLR 2025.
“GI-GS: Global illumination decomposition on Gaussian splatting for inverse rendering” [Paper] [Project Page]
“Exponential topology-enabled scalable communication in multi-agent reinforcement learning” [Paper]
(INFOCOM 25) “WRF-GS: Wireless radiation field reconstruction with 3D Gaussian splatting,” accepted by INFOCOM 2025. [Paper] [Code]
(AAAI 25) Two papers accepted by AAAI 2025.
(Nature Communications) Paper “Selective knowledge sharing for privacy-preserving federated distillation without a good teacher” was published by Nature Communications, 2024. [Paper]
Research Interests
At iComAI Lab, we pursue cutting-edge research at the intersection of communications technology and AI, with a particular focus on intelligent decision making and spatial perception. Our work bridges theoretical foundations with practical applications, addressing fundamental challenges in:
Generative AI, Foundation Models
Neural Data Representation and Compression
Reinforcement Learning
Safe and Efficient AI
O-RAN, Edge AI Systems
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Group WeChat Account of iComAI Lab (in Chineses) |
Selected Publications
AI Agent, LLM/MLLM Reasonging
X. Li, C. Bai, Z. Li, J. Zheng, T. Xiao, and J. Zhang, “Learn as Individuals, Evolve as a Team: Multi-agent LLMs adaptation in embodied environments,” preprint. [Paper]
Z. Li, J. Fu, L. Song, J. Bian, J. Zhang, and R. Wang, “Chain of Functions: A programmatic pipeline for fine-grained chart reasoning data,” preprint. [Paper]
Z. Li, Q. Guo, J. Shao, L. Song, J. Bian, J. Zhang, and R. Wang, “Graph neural network enhanced retrieval for question answering of large language models,” The Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL), Albuquerque, New Mexico, USA, April-May 2025. [Paper]
World Models, GenAI
W. Sun, F. Wei, J. Zhao, X. Chen, Z. Chen, H. Zhang, J. Zhang, and Y. Lu, “From virtual games to real-world play,” preprint. [Paper] [Project Page]
T. Li, Q. Lu, L. Zhao, H. Li, X. Zhu, Y. Qiao, J. Zhang, and W. Shao, “UniFork: Exploring modality alignment for unified multimodal understanding and generation,” preprint. [Paper]
S. Gao, S. Zhou, Y. Du, J. Zhang, and C. Gan, “AdaWorld: Learning adaptable world models with latent actions,” International Conference on Machine Learning (ICML), Vancouver, Canada, July 2025. (Acceptance Rate: 26.9%) [Project Page]
W. Sun, S. Chen, F. Liu, Z. Chen, Y. Duan, J. Zhang, and Y. Wang, “DimensionX: Create any 3D and 4D scenes from a single image with controllable video diffusion,” International Conference on Computer Vision (ICCV), Honolulu, Hawai'i, USA, Oct. 2025. (Acceptance Rate: 24%) [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,” The Thirty-Eighth Annual Conference on 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]
Efficient and Safe AI
Y. Xue, Y. Huang, J. Shao, and J. Zhang, “VLMQ: Efficient post-training quantization for large vision-language models via Hessian augmentation,” preprint. [Paper]
X. Ge, X. Zhang, T. Xu, Y. Zhang, X. Zhang, Y. Wang, and J. Zhang, “SenseFlow: Scaling distribution matching for flow-based text-to-image distillation,” preprint. [Paper]
Y. Huang, R. Gong, J. Liu, Y. Ding, C. Lv, H. Qin, and J. Zhang, “QVGen: Pushing the limit of quantized video generative models,” preprint. [Paper]
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,” International Conference on Machine Learning (ICML), Vancouver, Canada, July 2025. (Acceptance Rate: 26.9%) [Paper]
J. Shao, F. Wu, and J. Zhang, “Selective knowledge sharing for privacy-preserving federated distillation without a good teacher,” Nature Communications, vol. 15, no. 349, Jan 2024. [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]
Neural Data Representation and Compression
Z. Liu, Y. Hu, X. Zhang, J. Shao, Z. Lin, and J. Zhang, “Dynamics-aware Gaussian splatting streaming towards fast on-the-fly training for 4D reconstruction,” preprint. [Paper] [Project Page]
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,” International Conference on Computer Vision (ICCV), Honolulu, Hawai'i, USA, Oct. 2025. (Acceptance Rate: 24%) (Highlight) [Paper]
H. Chen, Z. Lin, and J. Zhang, “GI-GS: Global illumination decomposition on Gaussian splatting for inverse rendering,” International Conference on Learning Representations (ICLR), Singapore, April 2025. (Acceptance Rate: 32.08%) [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]
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]
Reinforcement Learning
Z. Liu, X. Li, and J. Zhang, “C2IQL: Constraint-conditioned implicit Q-learning for safe offline reinforcement learning,” International Conference on Machine Learning (ICML), Vancouver, Canada, July 2025. (Acceptance Rate: 26.9%)
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,” International Conference on Artificial Intelligence and Statistics (AISTATS), Mai Khao, Thailand, May 2025. (Acceptance Rate: 31.3%) [Paper]
X. Li, X. Wang, C. Bai, and J. Zhang, “Exponential topology-enabled scalable communication in multi-agent reinforcement learning,” International Conference on Learning Representations (ICLR), Singapore, April 2025. (Acceptance Rate: 32.08%) [Paper]
X. Li, L. Pan, and J. Zhang, “Kaleidoscope: Learnable masks for heterogeneous multi-agent reinforcement learning,” The Thirty-Eighth Annual Conference on 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]
O-RAN, Edge AI
B. Liu, Y. Lu, J. Zhao, Q. Yang, W. Wu, L. Chen, J. Chauhan, and Jun Zhang, “WiLLM: an open framework for LLM services over wireless systems,” preprint. [Paper]
B. Liu, J. Tong, and J. Zhang, “LLM-Slice: Dedicated wireless network slicing for large language models,” The 22nd ACM Conference on Embedded Networked Sensor Systems - Posters and Demos, Hangzhou, China, Nov. 2024. [Paper]
Y. Mao, X. Yu, K. Huang, A.-Y. Zhang, and J. Zhang, “Green edge AI: A contemporary survey,” Proc. IEEE, vol. 112, no. 7, pp. 880-911, July 2024. [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. (The 2025 IEEE Communications Society Katherine Johnson Young Author Best Paper Award) [Paper] [GitHub]
AI for Communications
W. Yu, H. He, S. Song, J. Zhang, L. Dai, L. Zheng, and K. B. Letaief, “AI and deep learning for Terahertz ultra-massive MIMO: From model-driven approaches to foundation models,” Engineering, to appear. [Paper]
C. Wen, J. Tong, Z. Lin, and J. Zhang, “WRF-GS: Wireless radiation field reconstruction with 3D Gaussian splatting,” Proc. IEEE INFOCOM, London, United Kingdom, May 2025. (Acceptance Rate: 18.7%) [Paper] [Code]
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]
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, 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]