I am currently a Postdoctoral Researcher at City University of Hong Kong (CityUHK), working with Prof. Dapeng Wu. Previously, I was a Postdoctoral Researcher at The Hong Kong University of Science and Technology (HKUST). I received my Ph.D. in Computer Science from The University of Sydney (USYD), advised by A/Prof. Wei Bao and Prof. Albert Y. Zomaya. I also received my Master’s degree in Information Technology from The University of Sydney and my Bachelor’s degree in Information Technology from Queensland University of Technology (QUT). Beyond academia, I have several years of industry experience in cloud-based systems and software development, including serving as a Technical Lead at Link Group Pty Ltd, Australia.

My research focuses on federated learning, distributed machine learning, weakly supervised learning, and MLLMs, with particular interests in improving the efficiency, trustworthy, and generalizability of learning over edge, cloud, and multi-tier networks. I have published 20+ papers at the top journals and international conferences such as NeurIPS, TIP, TPDS, TDSC, TSC, TNNLS, and ICDCS. If you are interested in any form of academic collaboration, please feel free to contact me at zhengjie.yang@sydney.edu.au.

🔥 News

2026.09: Started my new position as a Postdoc at City University of Hong Kong.
2023.11: I successfully defended my PhD dissertation.

📖 Educations

2024, PhD, Computer Science, The University of Sydney (USYD)
2017, MIT, Information Technology, The University of Sydney (USYD)
2015, BIT, Information Technology, Queensland University of Technology (QUT)

💻 Experiences

2026.09 - Present, Postdoc, City University of Hong Kong, HK
2024.09 - 2026.08, Post-doctoral Fellow, HK University of Science and Technology, HK
2024.03 - 2024.09, Postdoc, City University of Hong Kong, HK
2023.05 - 2024.02, Research Assistant, The University of Sydney, AU
2020.12 - 2024.02, Technical Lead, Link Group Pty. Ltd., AU
2017.07 - 2024.02, Academic Tutor, The University of Sydney, AU
2018.08 - 2020.12, Software Engineer, Link Group Pty. Ltd., AU
2018.02 - 2018.08, Software Engineer, Garvan Institute of Medical Research, AU

📝 Publications

(*: equal contribution, underlined name: student first author I co-mentor, #: corresponding author)

IEEE TETCI 2026 SageIQ: Scene-Graph-Guided Blind Image Quality Assessment, Renwei Yang, Zhengjie Yang, Yun Wang, Dapeng Oliver Wu, Shiqi Wang
IEEE TCC 2026 Causality-aware Graph Mixture of Experts for Accurate Multi-cloud Workload Prediction, Yongcan Luo, Zhihao Yu, Jiahao Zheng, Zhengjie Yang, Lei Sun, Dapeng Wu
IEEE TIP 2026 SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation, Yun Wang, Zhengjie Yang#, Jiahao Zheng, Zhanjie Zhang, Dapeng Oliver Wu, Yulan Guo
IEEE TSC 2026 Towards Robust and Fair Partial Label Federated Learning Service, Sen Fu, Wei Bao, Zhengjie Yang, Xinyi Sheng, Yichen Guo, Yuqin Liu, Bing Bing Zhou
TAI 2026 Recent Advances in Artificial Intelligence for Music Education, Guo Yu, Guanyi Zhao, Zhengjie Yang#
TechRxiv 2026 A Survey on Efficient Federated Pruning: Progress, Challenges, and Opportunities, Hong Huang, Zhengjie Yang#, Ning Chen, Juntao Hu, Xue Liu, Jinhai Yang, Dapeng Wu
TAI 2026 FedA 4: Federated Learning with Anti-Bias Aggregation and TrAjectory-Based Adaptation, Guanyi Zhao, Juntao Hu, Zhengjie Yang#, Dapeng Oliver Wu
CIKM 2025 Toward Secure Federated Partial Label Learning Against Poisoning Attacks, Xubin Chen, Zhengjie Yang, Xinyi Sheng, Sen Fu, Wei Bao
TAI 2025 Federated Learning for Medical Image Analysis: Privacy-Preserving Paradigms and Clinical Challenges, Juntao Hu, Zhengjie Yang#, Peng Wang, Guanyi Zhao, Hong Huang, Zhimin Zong, Dapeng Oliver Wu
TAI 2025 Recent Advances in Artificial Intelligence for Management and Financial Technology, Renwei Yang*, Yun Wang*, Yongcan Luo*, Zhengjie Yang#, Zhimin Zong, Dapeng Oliver Wu
IEEE TNNLS 2025 Pleno-Alignment Framework for Stock Trend Prediction, Yongcan Luo*, Jiahao Zheng*, Zhengjie Yang, Ning Chen, Dapeng Wu
IEEE TDSC 2024 FairGuard: A Fairness Attack and Defense Framework in Federated Learning, Xinyi Sheng, Zhengjie Yang, Wei Bao
IEEE TBD 2024 Personalized Federated Learning with Contrastive Momentum, Sen Fu, Zhengjie Yang, Chuang Hu, Wei Bao
IEEE ICDCS 2023 Hierarchical Federated Learning with Adaptive Momentum in Multi-tier Networks, Zhengjie Yang, Sen Fu, Wei Bao, Dong Yuan, Bing Bing Zhou
IEEE TPDS 2023 Hierarchical Federated Learning with Momentum Acceleration in Multi-tier Networks, Zhengjie Yang, Sen Fu, Wei Bao, Dong Yuan, Albert Y. Zomaya
IEEE TPDS 2022 Federated Learning with Nesterov Accelerated Gradient, Zhengjie Yang, Wei Bao, Dong Yuan, Nguyen H. Tran, Albert Y. Zomaya
IEEE TAI 2022 FastSlowMo: Federated Learning with Combined Worker and Aggregator Momenta, Zhengjie Yang, Sen Fu, Wei Bao, Dong Yuan, Albert Y. Zomaya
ACM MSWiM 2018 SFog: Seamless Fog Computing Environment for Mobile IoT Applications, Wei Bao, Dong Yuan, Zhengjie Yang, Shen Wang, Bing Bing Zhou, Stewart Adams, Albert Y. Zomaya
IEEE Communications Magazine 2017 Follow Me Fog: Toward Seamless Handover Timing Schemes in a Fog Computing Environment, Wei Bao, Dong Yuan, Zhengjie Yang, Shen Wang, Wei Li, Bing Bing Zhou, Albert Y. Zomaya

🎖 Honors and Awards

2019-2023, Engineering and Information Technologies Research Scholarship (USYD)
2021-2022, Postgraduate Research Support Scheme (USYD)
2021, School of Computer Science Research Activities Support Fund (USYD)
2020, School of Computer Science Research Students Excellence Prize (USYD)
2016-2017, School of Information Technology Summer Scholarship (USYD)
2015, Science and Engineering School International Merit Scholarship (QUT)

🛠 Services

2026, Special Issue Chair for AI4Science
2025, Track Co-Chair for SmartIoT
2025, Guest Editor of Special Issue “AI-Empowered Internet of Things” for Sensors
2021, Web Chair for EAI ADHOCNETS
Reviewer:
- Journal: ToN, JSAC, TMC, TPDS, TII, TWC, TIFS, TCOM, TC, TCC, TAI
- Conference: INFOCOM, AAAI, IJCAI, ICML, NeurIPS, ICLR, ICDCS, IWQoS, ICA3PP, AdHocNets

💬 Invited Talks

2026.08, “Towards Efficient, Trustworthy, and Generalizable Distributed Machine Learning”, Global Young Scholars’ Forum, CUHK-Shenzhen, Shenzhen, China
2025.06, “Towards Efficient Distributed Machine Learning”, 45th Anniversary of the Computer Science Program and Computer Science-AI Industry-Education Integration Forum, Jinling Institute of Technology, Nanjing, China