Hi, 👋 I’m Jie ZHANG, currently a Research Assistant Professor in the Department of Computer Science and Engineering (CSE) at The Hong Kong University of Science and Technology (HKUST), Hong Kong SAR, China. I obtained my Ph.D. degree from the Department of Computing (COMP) at The Hong Kong Polytechnic University (PolyU) in 2022, under the supervision of Prof. Song Guo, Fellow of IEEE. Prior to that, I obtained my B.E. and M.E. degree from China University of Geosciences, Wuhan, China.

My research interests mainly focus on Pervasive Learning and Intelligence, including Edge AI, Distributed Systems, Foundation Models’ Meta-cognition (Large Reasoning Models), Autonomy (LLM Agents) and Efficiency (Inference Acceleration). My vision is to develop native AI/ML systems that empower ubiquitous intelligent services, particularly in resource-constrained environments, to enrich human life in diverse aspects. I have published multiple top-tier international journal and conference papers, including NeurIPS, AAAI, ICML, ICLR, CVPR, IEEE TC, IEEE TPDS, IEEE TMC, etc.

🔥 News

• 2025.08: [Conference] Serve as Reviewer for ICLR 2026.
• 2025.08: [Conference] Serve as Reviewer for AAAI 2026.
• 2025.07: [Grant]  🎉🎉 One research project has been awarded under the NSFC’s Key Programme.
• 2025.07: [Grant]  🎉🎉 One General Research Fund (GRF) has been granted by Research Grants Council (RGC) of Hong Kong; Gratefulness!

💰 Grants

• [PI]: RGC General Research Fund (GRF)
  Project title: Evolving Edge Foundation Models via Adaptive Knowledge Editing in Diverse Environments
  Funding source: Research Grants Council of Hong Kong, 2025-2028

• [PI]: NSFC’s Key Programme
  Project title: “面向多模态联邦学习多层异质性问题的软硬件协同优化关键技术研究”
  Funding source: National Natural Science Foundation of China, 2026-2028

📝 Selected Publications

–2025–

• [Arxiv] CoRE: Enhancing Metacognition with Label-free Self-evaluation in LRMs
  H. Li, S. Bai, J. Zhang^#, S. Guo.
  arXiv preprint arXiv:2507.06087.

• [Arxiv] Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models
  Y. Liu, H. Li, X. Ma, J. Zhang^#, S. Guo
  arXiv preprint arXiv:2507.02663.

• [Arxiv] LazyEviction: Lagged KV Eviction with Attention Pattern Observation for Efficient Long Reasoning
  H. Zhang, H. Zhang, X. Ma, J. Zhang^#, S. Guo
  arXiv preprint arXiv:2506.15969.

• [Arxiv] ToFe: Lagged Token Freezing and Reusing for Efficient Vision Transformer Inference
  H. Zhang, J. Zhang^#, S. Guo
  arXiv preprint arXiv:2507.16260.

• [NeurIPS] DiEP: Adaptive Mixture-of-Experts Compression through Differentiable Expert Pruning
  S. Bai, J. Zhang^#, H. Li, Z. Hong, S. Guo
  The 39th Annual Conference on Neural Information Processing Systems (NeurIPS), 2025. (CCF-A)

• [ICCV] VA-MoE: Variables-Adaptive Mixture of Experts for Incremental Weather Forecasting
  H. Chen, T. Han, S. Guo, J. Zhang, Y. Yu, Y. Dong, L. Bai
  International Conference on Computer Vision (ICCV), 2025. (CCF-A)

• [ACM-MM] BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation
  J. Lai, W. Wu, J. Zhan, J. Li, B. Gao, J. Liu, J. Zhang, S. Guo
  ACM International Conference on Multimedia (ACM-MM), 2025. (CCF-A)

• [TNNLS] Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning
  Y. Liu, S. Guo, J. Zhang^#, Y. Zhan, Q. Zhou, Y. Wang
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2025. (CCF-B)

• [ICLR] Causally Motivated Sycophancy Mitigation for Large Language Models
  H. Li, X. Tang, J. Zhang^#, S. Guo, S. Bai, P. Dong, Y. Yu
  The Thirteenth International Conference on Learning Representations (ICLR), 2025. (CCF-A).

• [ICLR] Exploring Prosocial Irrationality for LLM Agents: A Social Cognition View
  X. Liu, J. Zhang^#, H. Shang, S. Guo, C. Yang, Q. Zhu
  The Thirteenth International Conference on Learning Representations (ICLR), 2025. (CCF-A).

–2024–

• [ICML] Easing Concept Bleeding in Diffusion via Entity Localization and Anchoring
  J. Zhang, S. Guo, P. Dong, J. Zhang, Z. Liu, Y. Yu, X. Wu
  International Conference on Machine Learning (ICML), 2024. (CCF-A).

• [ICML] Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization
  X. Tang, S. Guo, J. Guo, J. Zhang^#, Y. Yu
  International Conference on Machine Learning (ICML), 2024. (CCF-A).

• [ICML] Amend to Alignment: Decoupled Prompt Tuning for Mitigating Spurious Correlation in Vision-Language Models
  J. Zhang, X. Ma, S. Guo, P. Li, W. Xu, X. Tang, Z. Hong
  International Conference on Machine Learning (ICML), 2024. (CCF-A).

• [NeurIPS] Towards Safe Concept Transfer of Multi-modal Diffusion via Causal Representation Editing
  P. Dong, B. Wang, S. Guo, J. Wang, J. Zhang, Z. Hong
  Advances in Neural Information Processing Systems 37, 12708-12738 (NeurIPS), 2024. (CCF-A).

• [ACM-MM] SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalization
  Y. Wang, J. Guo, S. Guo, Y. Liu, J. Zhang, W. Zhang
  ACM International Conference on Multimedia (ACM-MM), 2024. (CCF-A)

• [TC] Collaborative Neural Architecture Search for Personalized Federated Learning
  Y. Liu, S. Guo, J. Zhang^#, Z. Hong, Y. Zhan, Q. Zhou
  IEEE Transactions on Computers (TC), 2024. (CCF-A)

• [TMC] Model Decomposition and Reassembly for Purified Knowledge Transfer in Personalized Federated Learning
  J. Zhang, S. Guo, X. Ma, W. Xu, Q. Zhou, J. Guo, Z. Hong, S. Jun.
  IEEE Transactions on Mobile Computing (TMC), 2024. (CCF-A).

• [INFOCOM] Otas: An Elastic Transformer Serving System via Token Adaptation
  J. Chen, W. Xu, Z. Hong, S. Guo, H. Wang, J. Zhang, D. Zeng
  IEEE INFOCOM 2024-IEEE Conference on Computer Communications (INFOCOM), 2024. (CCF-A)

• [ICLR] Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients
  X. Tang, S. Guo, J. Zhang^#, J. Guo
  The Twelfth International Conference on Learning Representations (ICLR), 2024. (CCF-A)

• [CVPR] DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
  S. Bai^*, J. Zhang^*, S. Guo, S. Li, J. Guo, J. Hou, T. Han, X. Lu
  The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (CCF-A)

• [AAAI] Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
  S. Bai, S. Li, W. Zhuang, J. Zhang^#, K. Yang, J. Hou, S. Yi, S. Zhang, J. Gao
  Annual AAAI Conference on Artificial Intelligence (AAAI), 2024. (CCF-A)

• [AAAI] On the Robustness of Neural-Enhanced Video Streaming against Adversarial Attacks
  Q. Zhou, J. Guo, S. Guo, R. Li, J. Zhang, B. Wang, Z. Xu
  Annual AAAI Conference on Artificial Intelligence (AAAI), 2024. (CCF-A)

–2023–

• [NeurIPS] SwapPrompt: Test-Time Prompt Adaptation for Vision-Language Models
  X. Ma, J. Zhang^#, S. Guo, W. Xu
  Advances in Neural Information Processing Systems (NeurIPS), 2023. (CCF-A)

• [ICML] Towards Unbiased Training in Federated Open-world Semi-supervised Learning
  J. Zhang, X. Ma, S. Guo, W. Xu
  International Conference on Machine Learning (ICML), 2023. (CCF-A).

• [TC] Towards Data-independent Knowledge Transfer in Model-heterogeneous Federated Learning
  J. Zhang, S. Guo, J. Guo, D. Zeng, J. Zhou, A. Zomaya
  IEEE Transactions on Computers (TC), 2023. (CCF-A).

–2022–

• [TPDS] Adaptive Vertical Federated Learning on Unbalanced Features
  J. Zhang, S. Guo, Z. Qu, D. Zeng, H. Wang, Q. Liu, AY. Zomaya
  IEEE Transactions on Parallel and Distributed Systems (TPDS) 33 (12), 4006-4018, 2022. (CCF-A)

• [CVPR] Layer-wised Model Aggregation for Personalized Federated Learning
  X. Ma^*, J. Zhang^*, S. Guo, W. Xu
  Proceedings of the IEEE/CVF conference on computer vision and pattern (CVPR), 2022. (CCF-A)

–2021–

• [NeurIPS] Parameterized Knowledge Transfer for Personalized Federated Learning
  J. Zhang, S. Guo, X. Ma, H. Wang, W. Xu, F. Wu
  Advances in Neural Information Processing Systems (NeurIPS) 34, 10092-10104, 2021. (CCF-A)

• [TC] Adaptive Federated Learning on Non-IID Data With Resource Constraint
  J. Zhang, S. Guo, Z. Qu, D. Zeng, Y. Zhan, Q. Liu, R. Akerkar
  IEEE Transactions on Computers (TC) 71 (7), 1655-1667, 2021. (CCF-A)

• [CSUR] Edge Learning: The Enabling Technology for Distributed Big Data Analytics in the Edge
  J. Zhang, Z. Qu, C. Chen, H. Wang, Y. Zhan, B. Ye, S. Guo
  ACM Computing Surveys (CSUR) 54 (7), 1-36, 2021. (CCF-B)

More publications can be found on my Google Scholar

👩‍💻 Teachings

As Instructor

• COMP 1021 - Introduction to Computer Science, HKUST, Spring 2025.
• COMP 1021 - Introduction to Computer Science, HKUST, Fall 2025.

As Teaching Assistant

• Programming Fundamentals, PolyU, Spring 2019, Autumn 2019.
• Big Data Analytics, PolyU, Spring 2020, Spring 2021.
• Big Data Computing, PolyU, Autumn 2020, Autumn 2021.

👔 Services

PC Member / Journal Reviewer

• Reviewer for The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR)
• Reviewer for Conference on Neural Information Processing Systems (NeurIPS)
• Reviewer for The International Conference on Machine Learning (ICML)
• Reviewer for IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS)
• Reviewer for IEEE Transactions on Mobile Computing (IEEE TMC)
• Reviewer for IEEE Transactions on Computers (IEEE TC)

🎖 Honors and Awards

• 2nd Prize, CUMCM (Contemporary Undergraduate Mathematical Contest in Modeling) (2023),
  Awarded by CSIAM (China Society for Industrial and Applied Mathematics).

• National Encouragement Scholarship (2014),
  Awarded by China University of Geoscience.

• Outstanding Undergraduate Student Honor (2015),
  Awarded by China University of Geoscience.

• 1st Prize, Science and Technology Paper Seminar (2017),
  Awarded by China University of Geoscience.