Tiehua's Personal Page

Dr. Tiehua Zhang

Tenure-track Professor
School of Computer Science and Technology, Tongji University

Email: tiehuaz[AT]hotmail[DOT]com
Research Interests: Edge Intelligence/Computing, Distributed/Federated Learning, LLM/SLM Synergistic Learning, Graph Learning

# Biography

I am currently a tenure-track Professor at Tongji University. I earned my Bachelor's, Master's, and Doctoral degrees in Computer Science from Jilin University, the University of Melbourne, and Swinburne University of Technology, respectively.

Prior to joining Tongji, I gained extensive experience across both academia and industry globally. My previous roles include Systems Development Engineer at Our Community Group in Australia, Visiting Scientist at KDDI Research in Japan, Postdoctoral Researcher at Macquarie University in Australia, and International Risk Algorithm Architect/Team Leader at Ant Group.

My research interests lie in Edge Intelligence, Distributed/Federated Learning, and LLM/SLM Synergistic Learning. My research has been recognized with several awards and honors, including the Swinburne Outstanding Thesis Award (Highly Commendation) and the Swinburne Research Excellence Award (HDR), etc.

# News

  • [05/2026]: I'm invited to give a talk at CCF The Conference on Graph Machine Learning 2026, Changchun, China.
  • [04/2026]: I will chair the Application and Industry Track at METAVERSE 2026.
  • [04/2026]: One paper accepted by IEEE TNSE, Congrats to Zhishu and others!
  • [03/2026]: We released Safety in Embodied AI (opens new window), a survey paper that reviews over 400 papers in the field, with collaboration more than 10 institutions worldwide. Great team work!
  • [02/2026]: One paper accepted by WWW'26 Oral! Congrats to Yuze and Yunhan (undergraduate student).
  • [01/2026]: One paper accepted by IEEE TMC.

# Selected Publications

Preprint
  • Yuze Liu, Shibo Chu, Tiehua Zhang*, Hao Zhou, Zhishu Shen, Jinze Wang, Jianzhong Qi, Feng Xia.
    ML-ECS: A Collaborative Multimodal Learning Framework for Edge-Cloud Synergies.
    CoRR abs/2602.14107, 2026.
  • Zhenwei Wang, Tiehua Zhang, Ning Xue, Ender Ozcan, Ling Wang, Ruibin Bai.
    Constraints Matrix Diffusion based Generative Neural Solver for Vehicle Routing Problems.
    CoRR abs/2603.07568, 2026.
  • Zhuocheng Liu, Zhishu Shen, Qiushi Zheng, Tiehua Zhang, Zheng Lei, Jiong Jin.
    A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks.
    CoRR abs/2507.22339, 2025.
  • Jinze Wang, Lu Zhang, Yiyang Cui, Zhishu Shen, Xingjun Ma, Jiong Jin, Tiehua Zhang*.
    Do We Really Need SFT? Prompt-as-Policy over Knowledge Graphs for Cold-start Next POI Recommendation.
    CoRR abs/2510.08012, 2025.
  • Zhuocheng Liu, Zhishu Shen, Qiushi Zheng, Tiehua Zhang, Zheng Lei, Jiong Jin.
    A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks.
    arXiv:2507.22339, 2025.
  • Jingyu Li, Tiehua Zhang*, Jinze Wang, Yi Zhang, Yuhuan Li, Yifan Zhao, Zhishu Shen, Jiannan Liu.
    MetaSTH-Sleep: Towards Effective Few-Shot Sleep Stage Classification with Spatial-Temporal Hypergraph Enhanced Meta-Learning.
    CoRR abs/2505.17142, 2025.
  • Zhenwei Wang, Ruibin Bai, Tiehua Zhang.
    Towards Constraint-Based Adaptive Hypergraph Learning for Solving Vehicle Routing: An End-to-End Solution.
    arXiv:2503.10421, 2025.
  • Ziming Zhao, Zhenwei Wang, Tiehua Zhang*, Zhishu Shen, Hai Dong, Xingjun Ma, Zhijun Ding, Yun Yang.
    CHASE: A Causal Heterogeneous Graph based Framework for Root Cause Analysis in Multimodal Microservice Systems.
    CoRR abs/2406.19711, 2024.
2026
  • Ziqi Rong, Zhishu Shen, Wanwei Zhan, Qiushi Zheng, Tiehua Zhang, Jiong Jin.
    Heterogeneous Hypergraph Multi-Agent Learning for UAV Collaboration in Disaster Scenarios.
    IEEE Transactions on Network Science and Engineering, 2026.
  • Yuze Liu, Yunhan Wang, Tiehua Zhang*, Zhishu Shen, Cheng Peng, Libing Wu, Feng Xia, Jiong Jin.
    A Structure-Agnostic Co-Tuning Framework for LLMs and SLMs in Cloud-Edge Systems.
    WWW, 2026.
  • Yuze Liu, Tiehua Zhang*, Zhishu Shen, Libing Wu, Shiping Chen, Jiong Jin.
    Towards Heterogeneity-Aware and Energy-Efficient Topology Optimization for Decentralized Federated Learning in Edge Environment.
    IEEE Transactions on Mobile Computing, 2026.
2025
  • Dawen Jiang, Zhishu Shen, Qiushi Zheng, Tiehua Zhang, Wei Xiang, Jiong Jin.
    Farm-LightSeek: An Edge-Centric Multimodal Agricultural IoT Data Analytics Framework With Lightweight LLMs.
    IEEE Internet of Things Magazine, 2025.
  • Jinze Wang, Jiong Jin*, Lu Zhang, Hong-Ning Dai, Adriano Di Pietro, Tiehua Zhang*.
    Towards Spatial-temporal Meta-hypergraph Learning for Multimodal Few-shot Fault Diagnosis.
    Journal of Industrial Information Integration, 2025.
  • Tiehua Zhang, Yuze Liu, Zhishu Shen, Xingjun Ma, Peng Qi, Zhijun Ding, Jiong Jin.
    Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs.
    IEEE Transactions on Artificial Intelligence, 2025.
  • Kang Wang, Zhishu Shen*, Zhen Lei, Xianhui Liu, Tiehua Zhang*.
    Toward Multi-Agent Reinforcement Learning Based Traffic Signal Control Through Spatio-Temporal Hypergraphs.
    IEEE Transactions on Mobile Computing, 2025.
  • Xiaoxue Mei, Xianfeng Yuan, Jiong Jin, Yong Song, Longda Zhang, Fengyu Zhou, Xiaoqi Chen, Tiehua Zhang.
    ATGCN: An Adaptive Temporal-topological Graph Convolution Network with Nodal Attention for Robot Fault Diagnosis.
    IEEE/ASME Transactions on Mechatronics, 2025.
  • Zhen Lei, Zhishu Shen, Kang Wang, Zhenwei Wang, Tiehua Zhang*.
    DHLight: Multi-Agent Policy-Based Directed Hypergraph Learning for Traffic Signal Control.
    ECAI, 2025.
  • Ziming Zhao, Tiehua Zhang*, Zijian Yi, Zhishu Shen.
    HyperSMOTE: A Hypergraph-based Oversampling Approach for Imbalanced Node Classifications.
    ICASSP, 2025.
  • Jinze Wang*, Tiehua Zhang*, Lu Zhang, Yang Bai, Xin Li, Jiong Jin.
    HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation.
    ICME, 2025.
  • Xiaoxue Mei, Jiong Jin, Jonathan Kua, Xianfeng Yuan, Tiehua Zhang.
    Multi-Robot Fault Diagnosis using Federated Graph Learning with Fused Adjacency Matrix.
    INDIN, 2025.
  • Yuze Liu, Tingjie Liu, Tiehua Zhang*, Youhua Xia, Jinze Wang, Zhishu Shen, Jiong Jin, Zhijun Ding, Fei Richard Yu.
    GRL-Prompt: Towards Prompts Optimization via Graph-Empowered Reinforcement Learning Using LLMs' Feedback.
    PAKDD, 2025.
  • Jiazhao Yu, Yanlun Tu, Zhanlei Zhang, Tiehua Zhang*, Cheng Xu, Weigang Wu, Hong Jin Kang, Xi Zheng*.
    Grey-Box Fuzzing in Constrained Ultra-Large Systems: Lessons for SE Community.
    FSE (Industry Track), 2025.
  • Ye Sun, Hao Zhang, Henghui Ding, Tiehua Zhang, Xingjun Ma, Yu-Gang Jiang.
    SAMA: Towards Multi-Turn Referential Grounded Video Chat with Large Language Models.
    NeurIPS, 2025.
2024
  • Youhua Xia, Tiehua Zhang*, Jiong Jin, Ying He, F. Richard Yu.
    Towards Secure and Efficient Data Scheduling for Vehicular Social Networks.
    IEEE Transactions on Vehicular Technology, 2024.
  • Zhenwei Wang, Ruibin Bai, Fazlullah Khan, Ender Özcan, Tiehua Zhang.
    GASE: Graph Attention Sampling with Edges Fusion for Solving Vehicle Routing Problems.
    Memetic Computing, 2024.
  • Tiehua Zhang, Rui Xu, Jianping Zhang, Yuze Liu, Xin Chen, Jun Yin, Xi Zheng.
    DSHGT: Dual-Supervisors Heterogeneous Graph Transformer - A Pioneer Study of Using Heterogeneous Graph Learning for Detecting Software Vulnerabilities.
    ACM Transactions on Software Engineering and Methodology, 2024.
  • Tiehua Zhang, Yuze Liu, Zhishu Shen, Rui Xu, Xin Chen, Xiaowei Huang, Xi Zheng.
    An Adaptive Federated Relevance Framework for Spatial-Temporal Graph Learning.
    IEEE Transactions on Artificial Intelligence, 2024.
  • Yuze Liu, Ziming Zhao, Tiehua Zhang*, Kang Wang, Xin Chen, Xiaowei Huang, Jun Yin, Zhishu Shen.
    Exploiting Spatial-Temporal Data for Sleep Stage Classification via Hypergraph Learning.
    ICASSP, 2024.
  • Zijian Yi, Ziming Zhao, Zhishu Shen*, Tiehua Zhang*.
    Multimodal Fusion via Hypergraph Autoencoder and Contrastive Learning for Emotion Recognition in Conversation.
    ACM MM, 2024.
  • Ye Sun, Hao Zhang, Tiehua Zhang, Xingjun Ma, Yu-Gang Jiang.
    UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation.
    NeurIPS, 2024.
2023
  • Jingling Yuan, Hua Xiao, Zhishu Shen, Tiehua Zhang, Jiong Jin.
    ELECT: Energy-efficient Intelligent Edge-cloud Collaboration for Remote IoT Services.
    Future Generation Computer Systems, 2023.
  • Feng Zhu, Mingjie Zhong, Xinxing Yang, Longfei Li, Lu Yu, Tiehua Zhang, et al.
    DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation.
    ICDE, 2023.
  • Yuze Liu, Ziming Zhao, Tiehua Zhang*, Kang Wang, Xin Chen, Xiaowei Huang, Jun Yin, Zhishu Shen.
    Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning.
    ICASSP, 2023.
2022
  • Youhua Xia*, Tiehua Zhang*, Libing Wu, James Xi Zheng, Jiong Jin.
    Privacy-Preserving Data Scheduling in Incentive-Driven Vehicular Network.
    IEEE Internet of Things Journal, 2022.
  • Tiehua Zhang, Yuze Liu, Yao Yao, Youhua Xia, Xin Chen, Xiaowei Huang, Jiong Jin.
    Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks.
    AAAI-DLG, 2022.
  • Zhishu Shen, Jiong Jin, Tiehua Zhang, Atsushi Tagami, Teruo Higashino, Qinglong Han.
    Data-driven Edge Computing Fabric for Intelligent Building Energy Management Systems.
    IEEE Industrial Electronics Magazine, 2022.
  • Rong Liang, Tiehua Zhang*, Yujie Lu, Yuze Liu, Zhen Huang, Xin Chen.
    AstBERT: Enabling Language Model for Code Understanding with Abstract Syntax Tree.
    EMNLP-FinNLP, 2022.
2021
  • Tiehua Zhang, Zhishu Shen, Jiong Jin, James Xi Zheng, Atsushi Tagami, Xianghui Cao.
    Achieving Democracy in Edge Intelligence: A Fog-Based Collaborative Learning Scheme.
    IEEE Internet of Things Journal, 2021.
  • Zhishu Shen*, Tiehua Zhang*, Atsushi Tagami, Jiong Jin.
    When RSSI encounters deep learning: An area localization scheme for pervasive sensing systems.
    Journal of Network and Computer Applications, 2021.
  • Yao Deng, Tiehua Zhang, Guannan Lou, Xi Zheng, Jiong Jin, Qing-Long Han.
    Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses.
    IEEE Transactions on Industrial Informatics, 2021.
  • Guannan Lou, Yuze Liu, Tiehua Zhang, James Xi Zheng.
    STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks.
    AAAI-DLG, 2022.
  • Tiehua Zhang, Yuze Liu, Xin Chen, Xiaowei Huang, Feng Zhu, Xi Zheng.
    GPS: A Policy-driven Sampling Approach for Graph Representation Learning.
    KDD-DLG, 2022.
2020 and Before
  • Tiehua Zhang, Jiong Jin, Xi Zheng, Yun Yang.
    Rate-Adaptive Fog Service Platform for Heterogeneous IoT Applications.
    IEEE Internet of Things Journal, 2020.
  • Qiushi Zheng, Jiong Jin, Tiehua Zhang, Longxiang Gao, Yong Xiang.
    Realizing Video Analytic Service in the Fog-Based Infrastructure-Less Environments.
    Fog-IoT, 2020.
  • Youhua Xia, Libing Wu, Jiong Jin, Tiehua Zhang, Xi Zheng.
    Privacy-Aware Key Task Scheduling in Vehicular Networks Based on Incentive Mechanism.
    HPCC, 2020.
  • Tiehua Zhang, Zhishu Shen, Jiong Jin, James Xi Zheng.
    A Democratically Collaborative Learning Scheme for Fog-enabled Pervasive Environments.
    PerCom WiP (Best Paper Finalist), 2020.
  • Zhishu Shen, Tiehua Zhang, Jiong Jin, Kenji Yokota, Atsushi Tagami, Teruo Higashino.
    ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems.
    IEEE Access, 2019.
  • Qiushi Zheng, Jiong Jin, Tiehua Zhang, Jianhua Li, Longxiang Gao, Yong Xiang.
    Energy-Sustainable Fog System for Mobile Web Services in Infrastructure-Less Environments.
    IEEE Access, 2019.
  • Tiehua Zhang, Zhishu Shen, Jiong Jin, Atsushi Tagami, Xi Zheng, Yun Yang.
    ESDA: An Energy-Saving Data Analytics Fog Service Platform.
    ICSOC, 2019.
  • Tiehua Zhang, Jiong Jin, Yun Yang.
    RA-FSD: A Rate-Adaptive Fog Service Delivery Platform.
    ICSOC, 2018.
  • Jianhua Li, Tiehua Zhang, Jiong Jin, Yingying Yang, Dong Yuan, Longxiang Gao.
    Latency estimation for fog-based internet of things.
    ITNAC, 2017.

# Awards & Honors

  • Shanghai Science & Technology 35 Under 35 (S&T35), 2025
  • Shanghai Pudong New Area Pearl Elite Award, 2024
  • CCF Digital Finance Conference Best Poster Award, 2024
  • Shanghai High-Level Talent (Overseas), 2023
  • Ant Group Research Innovation Award of The Year, 2022
  • Swinburne Outstanding Thesis Award (Highly Commendation), 2021
  • Swinburne Research Excellence Award (HDR), 2020
  • Swinburne Research Training Program Scholarship, 2017-2020

# Teaching

  • Spring 2025, Spring 2026 — Advanced Programming Language @ Tongji
  • Fall 2025, Fall 2026 — Introduction to Information Security @ Tongji
  • Fall 2025 — Algorithms and Data Structures @ Tongji
  • Spring 2020 — Deep Learning @ Deakin
  • Fall 2019 — Statistical Data Analysis @ Deakin
  • Fall 2017 — Object-oriented Programming @ Swinburne

# Services

  • Editorial Member: Human-Centric Intelligent Systems, Mathematics (Guest Editor), Journal on Wireless Communications and Networking (Guest Editor), Computer Engineering.
  • Chair/Organizer: Application and Industry Track Chair (METAVERSE 2025, 2026), Publicity Chair (CCF Digital Finance Conference, 2024)
  • PC for Conferences: NeurIPS (2021, 2022, 2023, 2024, 2025), WWW (2022, 2023, 2024, 2025), MSN (2024, 2025), INFOCOM (2023, 2024, 2025), ICASSP (2021, 2022, 2023, 2024, 2025), PAKDD (2019, 2020, 2021, 2023, 2024), ACM MM (2019, 2020, 2021, 2024).
  • Reviewer for Journals: IEEE TAI, IEEE TII, IEEE IoT-J, IEEE TVT, IEEE TKDE, IEEE TBD, ACM TOSEM, IEEE TNSE, ACM TKDD, Information Sciences, Neurocomputing, World Wide Web Journal, Neural Networks, EAAI, Information Processing & Management.

# Students & Collaborators

I'm happy to collaborate with students and researchers interested in Edge Intelligence, Distributed/Federated Learning, and LLM/SLM Synergistic Learning, and Graph Learning. Please drop me an email with your background and interests. Email: tiehuaz@hotmail.com

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