
Overview
Background
Prof. Hongzhi Yin works as an ARC Future Fellow and Professor and director of the Responsible Big Data Intelligence Lab (RBDI) at The University of Queensland, Australia. He has made notable contributions to predictive analytics, recommendation systems, graph learning, social media analytics, and decentralized and edge intelligence. He has received numerous awards and recognition for his research achievements. He has been named to IEEE Computer Society’s AI’s 10 to Watch 2022 and Field Leader of Data Mining & Analysis in The Australian's Research 2020 magazine. In addition, he has received the prestigious 2023 Young Tall Poppy Science Awards, Australian Research Council Future Fellowship 2021, the Discovery Early Career Researcher Award 2016, UQ Foundation Research Excellence Award 2019, Rising Star of Science Award (2022-2024) and 2024 Computer Science in Australia Leader Award, AI 2000 Most Influential Scholar Honorable Mention in Data Mining (2022-2024). His research has won 8 international and national Best Paper Awards, including Best Student Full Paper Award at CIKM 2024, Best Paper Award - Honorable Mention at WSDM 2023, Best Paper Award at ICDE 2019, Best Student Paper Award at DASFAA 2020, Best Paper Award Nomination at ICDM 2018, ACM Computing Reviews' 21 Annual Best of Computing Notable Books and Articles, Best Paper Award at ADC 2018 and 2016. His Ph.D. thesis won Peking University Outstanding Ph.D. Dissertation Award 2014 and CCF Outstanding Ph.D. Dissertation Award (Nomination) 2014. He has ten conference papers recognized as the Most Influential Papers in Paper Digest, including KDD 2021 and 2013, AAAI 2021, SIGIR 2022, WWW 2023 and 2021, CIKM 2021, 2019, 2016, and 2015. He has published over 350+ papers with an H-index of 83 (22000+ citations), including 280+ CCF A/CORE A* and 70+ CCF B/CORE A, such as ICML, KDD, SIGIR, WWW, ACL, WSDM, SIGMOD, VLDB, ICDE, NeurIPS, AAAI, IJCAI, ACM Multimedia, ECCV, IEEE TKDE, TNNL, VLDB Journal, and ACM TOIS. He has been the leading author (first/co-first author or corresponding author) for 250+. He has been an SPC/PC member for many top conferences, such as AAAI, IJCAI, KDD, ICML, ICLR, NeurIPS, SIGIR, WWW, WSDM, VLDB, ICDE, ICDM, and CIKM. He has been serving as Associate Editor/Guest Editor/Editorial Board for Neural Networks (JCR Q1, CCF B, 中科院一区), Science China Information Sciences (JCR Q1, CCF A, 中科院一区), Data Science and Engineering (JCR Q1, 中科院一区), Journal of Computer Science and Technology (JCST, CCF B), Journal of Social Computing, ACM Transactions on Information Systems 2022-2023 (JCR Q1, CCF A, CORE A, 中科院一区), ACM Transactions on Intelligent Systems and Technology 2020-2021 (JCR Q1), Information Systems 2020-2021 (CORE A*), and World Wide Web 2020-2021 and 2017-2018 (CORE A, CCF B). Dr. Yin has also been attracting wide media coverage, such as The Australian, SBS Radio Interviews, UQ News, Sohu.com, Faculty News of EAIT, IEEE Computer Society, ACM Computing Reviews.
I am now looking for highly motivated Ph.D. students. The University of Queensland ranks in the top 50 as measured by the Performance Ranking of Scientific Papers for World Universities. The University also ranks 40 in the QS World University Rankings and 41 in the US News Best Global Universities Rankings. The University of Queensland is the best in Australia according to the Australian Financial Review (AFR), which has now ranked UQ in the #1 position for 2 consecutive years. Please find the following two PhD scholarships.
Latest News
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[23 May 2025] I was ranked #52 in Australia among Best Scientists for 2025 and have also been recognized with the Computer Science Leader Award for 2025 in Research.com.
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[15 May 2025] We have four research papers and one applied data science paper accepted by the top conference KDD 2025 (CORE A*, CCF A).
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Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation
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Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning
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FLUID-MMRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation
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Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
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[11 May 2025] Our research work "RobGC: Towards Robust Graph Condensation" has been accepted by the top journal TKDE 2025 (CORE A*, CCF A). Congratulations to Xinyi.
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[1 May 2025] Our research work "Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective" has been accepted by the top conference ICML 2025 (CORE A*, CCF A). Congratulations to Hechuan.
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[4 April 2025] We have four full research papers accepted by the top conference SIGIR 2025 (CORE A*, CCF A).
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[2 April 2025] Congratulations to the four new doctors, Dr. Wei Yuan, Dr. Jing Long, Dr. Yuting Sun and Dr. Ruiqi Zheng, who were awarded their PhD by The University of Queensland.
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[10 March 2025] Our survey paper "A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security " has been accepted by TKDE 2025 (CORE A*, CCF A).
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[21 Feb 2025] Our joint foundation work "On the Trustworthiness of Generative Foundation Models– Guideline, Assessment, and Perspective" has been released on both arXiv and Hugging Face. This research is the result of a broad collaboration with leading universities and research institutions worldwide, including the University of Notre Dame, Massachusetts Institute of Technology, University of Waterloo, Carnegie Mellon University, University of Illinois Urbana-Champaign, Stanford University, University of California, Santa Barbara, IBM Research, Microsoft Research, The University of Queensland and more.
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[20 Feb 2025] I have been recognized as a Highly Ranked Scholar - Prior 5 Years (top 0.05% of all scholars) and #15 in Data Mining on ScholarGPS.
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[26 January 2025] Our survey paper "Graph Condensation: A Survey" has been accepted by TKDE 2025 (CORE A*, CCF A).
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[20 January 2025] We have three full research papers and one demo paper accepted by the top conference WWW 2025 (CORE A*, CCF A).
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Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition
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BiasNavi: LLM-Empowered Data Bias Management
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[18 January 2025] We have two research papers accepted by AAAI 2025 (CCF A, CORE A*) for Oral Presentation.
Availability
- Professor Hongzhi Yin is:
- Available for supervision
Qualifications
- Postgraduate Diploma, Peking University
- Doctor of Philosophy, Peking University
Research interests
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Recommender System and User Modeling
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Graph Mining and Embedding
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Decentralized and Federated Learning
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Edge Machine Learning and Applications
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Trustworthy Machine Learning and Applications
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QA, Chatbot and Information Retrieval
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Time Series and Sequence Mining and Prediction
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Spatiotemporal Data Mining
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Smart Healthcare
Research impacts
Prof. Yin is currently directing the Responsible Big Data Intelligence Lab (RBDI). RBDI Lab aims and strives to develop decentralized, on-device, and trustworthy (e.g., privacy-preserving, robust, explainable and fair) data mining and machine learning techniques with theoretical backbones to better discover actionable patterns and intelligence from large-scale, heterogeneous, networked, dynamic and sparse data. RBDI joins forces with other fields such as urban transportation, healthcare, agriculture, E-commerce and marketing to help solve societal, environmental and economic challenges facing humanity in pursuit of a sustainable future. His research has also attracted media coverage, such as The Australian, SBS, UQ News, Faculty News of EAIT, ACM Computing Reviews, 360 News.
Works
Search Professor Hongzhi Yin’s works on UQ eSpace
2024
Conference Publication
Physical trajectory inference attack and defense in decentralized POI recommendation
Long, Jing, Chen, Tong, Ye, Guanhua, Zheng, Kai, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2024). Physical trajectory inference attack and defense in decentralized POI recommendation. WWW '24: The ACM Web Conference 2024, Singapore, 13 - 17 May 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3589334.3645410
2024
Conference Publication
Graph condensation for inductive node representation learning
Gao, Xinyi, Chen, Tong, Zang, Yilong, Zhang, Wentao, Hung Nguyen, Quoc Viet, Zheng, Kai and Yin, Hongzhi (2024). Graph condensation for inductive node representation learning. 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, Netherlands, 13-16 May 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/icde60146.2024.00237
2024
Conference Publication
Decentralized collaborative learning with adaptive reference data for on-device POI recommendation
Zheng, Ruiqi, Qu, Liang, Chen, Tong, Cui, Lizhen, Shi, Yuhui and Yin, Hongzhi (2024). Decentralized collaborative learning with adaptive reference data for on-device POI recommendation. WWW '24: The ACM Web Conference 2024, Singapore, 13 - 17 May 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3589334.3645696
2024
Conference Publication
HeteFedRec: federated recommender systems with model heterogeneity
Yuan, Wei, Qu, Liang, Cui, Lizhen, Tong, Yongxin, Zhou, Xiaofang and Yin, Hongzhi (2024). HeteFedRec: federated recommender systems with model heterogeneity. 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, Netherlands, 13-16 May 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/icde60146.2024.00109
2024
Conference Publication
Prompt-enhanced federated content representation learning for cross-domain recommendation
Guo, Lei, Lu, Ziang, Yu, Junliang, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2024). Prompt-enhanced federated content representation learning for cross-domain recommendation. 33rd ACM Web Conference, WWW 2024, Singapore, 13 May 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3589334.3645337
2024
Conference Publication
Unraveling the ‘Anomaly’ in time series anomaly detection: a self-supervised tri-domain solution
Sun, Yuting, Pang, Guansong, Ye, Guanhua, Chen, Tong, Hu, Xia and Yin, Hongzhi (2024). Unraveling the ‘Anomaly’ in time series anomaly detection: a self-supervised tri-domain solution. 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, Netherlands, 13-16 May 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/icde60146.2024.00080
2024
Conference Publication
On-device recommender systems: a tutorial on the new-generation recommendation paradigm
Yin, Hongzhi, Chen, Tong, Qu, Liang and Cui, Bin (2024). On-device recommender systems: a tutorial on the new-generation recommendation paradigm. 33rd ACM Web Conference, WWW 2024, Singapore, 13 - 17 May 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3589335.3641250
2024
Conference Publication
Accelerating scalable graph neural network inference with node-adaptive propagation
Gao, Xinyi, Zhang, Wentao, Yu, Junliang, Shao, Yingxia, Nguyen, Quoc Viet Hung, Cui, Bin and Yin, Hongzhi (2024). Accelerating scalable graph neural network inference with node-adaptive propagation. 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, Netherlands, 13-16 May 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/icde60146.2024.00236
2024
Conference Publication
Towards personalized privacy: user-governed data contribution for federated recommendation
Qu, Liang, Yuan, Wei, Zheng, Ruiqi, Cui, Lizhen, Shi, Yuhui and Yin, Hongzhi (2024). Towards personalized privacy: user-governed data contribution for federated recommendation. WWW '24: The ACM Web Conference 2024, Singapore, 13 - 17 May 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3589334.3645690
2024
Conference Publication
Challenging low homophily in social recommendation
Jiang, Wei, Gao, Xinyi, Xu, Guandong, Chen, Tong and Yin, Hongzhi (2024). Challenging low homophily in social recommendation. WWW '24: The ACM Web Conference 2024, Singapore, 13-17 May 2024. New York, United States: Association for Computing Machinery. doi: 10.1145/3589334.3645460
2024
Journal Article
Variational counterfactual prediction under runtime domain corruption
Wen, Hechuan, Chen, Tong, Chai, Li Kheng, Sadiq, Shazia, Gao, Junbin and Yin, Hongzhi (2024). Variational counterfactual prediction under runtime domain corruption. IEEE Transactions on Knowledge and Data Engineering, 36 (5) 10271745, 2271-2284. doi: 10.1109/tkde.2023.3321893
2024
Journal Article
OntoMedRec: logically-pretrained model-agnostic ontology encoders for medication recommendation
Tan, Weicong, Wang, Weiqing, Zhou, Xin, Buntine, Wray, Bingham, Gordon and Yin, Hongzhi (2024). OntoMedRec: logically-pretrained model-agnostic ontology encoders for medication recommendation. World Wide Web-Internet and Web Information Systems, 27 (3) 28. doi: 10.1007/s11280-024-01268-1
2024
Conference Publication
Defense against model extraction attacks on recommender systems
Zhang, Sixiao, Yin, Hongzhi, Chen, Hongxu and Long, Cheng (2024). Defense against model extraction attacks on recommender systems. 17th ACM International Conference on Web Search and Data Mining (WSDM), Merida, Mexico, 4-8 March 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3616855.3635751
2024
Conference Publication
Budgeted embedding table for recommender systems
Qu, Yunke, Chen, Tong, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2024). Budgeted embedding table for recommender systems. 17th ACM International Conference on Web Search and Data Mining (WSDM), Merida, Mexico, 4-8 March 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3616855.3635778
2024
Conference Publication
Motif-based prompt learning for universal cross-domain recommendation
Hao, Bowen, Yang, Chaoqun, Guo, Lei, Yu, Junliang and Yin, Hongzhi (2024). Motif-based prompt learning for universal cross-domain recommendation. 17th ACM International Conference on Web Search and Data Mining (WSDM), Merida, Mexico, 4-8 March 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3616855.3635754
2024
Journal Article
Time interval-enhanced graph neural network for shared-account cross-domain sequential recommendation
Guo, Lei, Zhang, Jinyu, Tang, Li, Chen, Tong, Zhu, Lei and Yin, Hongzhi (2024). Time interval-enhanced graph neural network for shared-account cross-domain sequential recommendation. IEEE Transactions on Neural Networks and Learning Systems, 35 (3), 4002-4016. doi: 10.1109/tnnls.2022.3201533
2024
Conference Publication
Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge
Qiu, Wei, Yin, He, Wu, Yuru, Zeng, Chujie, Chen, Chang, Dong, Yuqing and Liu, Yilu (2024). Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge. 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC United States, 19-22 February 2024. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/isgt59692.2024.10454172
2024
Journal Article
MCRPL: A pretrain, prompt, and fine-tune paradigm for non-overlapping many-to-one cross-domain recommendation
Liu, Hao, Guo, Lei, Zhu, Lei, Jiang, Yongqiang, Gao, Min and Yin, Hongzhi (2024). MCRPL: A pretrain, prompt, and fine-tune paradigm for non-overlapping many-to-one cross-domain recommendation. ACM Transactions on Information Systems, 42 (4) 97, 1-24. doi: 10.1145/3641860
2024
Journal Article
XSimGCL: towards extremely simple graph contrastive learning for recommendation
Yu, Junliang, Xia, Xin, Chen, Tong, Cui, Lizhen, Hung, Nguyen Quoc Viet and Yin, Hongzhi (2024). XSimGCL: towards extremely simple graph contrastive learning for recommendation. IEEE Transactions on Knowledge and Data Engineering, 36 (2), 913-926. doi: 10.1109/tkde.2023.3288135
2024
Journal Article
HiTSKT: A hierarchical transformer model for session-aware knowledge tracing
Ke, Fucai, Wang, Weiqing, Tan, Weicong, Du, Lan, Jin, Yuan, Huang, Yujin and Yin, Hongzhi (2024). HiTSKT: A hierarchical transformer model for session-aware knowledge tracing. Knowledge-Based Systems, 284 111300. doi: 10.1016/j.knosys.2023.111300
Funding
Current funding
Past funding
Supervision
Availability
- Professor Hongzhi Yin is:
- Available for supervision
Before you email them, read our advice on how to contact a supervisor.
Available projects
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Building an Trustworthy Information Recommendation System
Build a trustworthy information recommender system by spearheading the design and development of cutting-edge LLM4Rec techniques, misinformation filters, and privacy protection mechanisms.
This Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.
Supervision history
Current supervision
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Doctor Philosophy
Decentralised Collaborative Predictive Analytics on Personal Smart Devices
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Decentralised Collaborative Predictive Analytics on Personal Smart Devices
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Decentralised Collaborative Predictive Analytics on Personal Smart Devices
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Joint Feature Learning for Recommender System
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Lightweight Graph Neural Networks for Recommendation
Associate Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Scalable and Generalizable Graph Neural Networks
Associate Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Integrated high-throughput material synthesis and characterisation system
Associate Advisor
Other advisors: Associate Professor Jingwei Hou
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Doctor Philosophy
Scalable and Lightweight On-Device Recommender Systems
Associate Advisor
Other advisors: Dr Rocky Chen, Dr Junliang Yu
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Doctor Philosophy
Scalable and Lightweight On-Device Recommender Systems
Associate Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Sustainable On-Device Recommender Systems
Associate Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Causal Analysis for Decision Support in Public Health
Associate Advisor
Other advisors: Professor Shazia Sadiq, Dr Rocky Chen
Completed supervision
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2025
Doctor Philosophy
Decentralized Learning for On-device Recommendation
Principal Advisor
Other advisors: Dr Rocky Chen
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2025
Doctor Philosophy
Deep Learning for Univariate Time Series Anomaly Detection in Industrial IoT
Principal Advisor
Other advisors: Dr Thomas Taimre, Dr Slava Vaisman
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2025
Doctor Philosophy
Decentralized Point-Of-Interest (POI) Recommender Systems
Principal Advisor
Other advisors: Dr Rocky Chen
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2025
Doctor Philosophy
Secure Cross-device Federated Recommender Systems
Principal Advisor
Other advisors: Dr Miao Xu
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2024
Doctor Philosophy
Federated Graph Neural Network-based Recommender Systems
Principal Advisor
Other advisors: Dr Miao Xu
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2023
Doctor Philosophy
From Cloud to Device: Transforming Recommender Systems for On-Device Deployment
Principal Advisor
Other advisors: Dr Miao Xu
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2023
Doctor Philosophy
Decentralized On-device Machine Learning and Unlearning for IoT Collaboration
Principal Advisor
Other advisors: Dr Miao Xu
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2023
Doctor Philosophy
Enhancing Recommender Systems wtih Self-Supervised Learning
Principal Advisor
Other advisors: Professor Helen Huang
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2022
Doctor Philosophy
Toward Deep Conversational Recommender Systems
Principal Advisor
Other advisors: Professor Helen Huang
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2022
Doctor Philosophy
Secure Recommender Systems
Principal Advisor
Other advisors: Professor Helen Huang
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2022
Doctor Philosophy
Decentralized Framework for Embedding Large-scale Networks
Principal Advisor
Other advisors: Professor Helen Huang
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2021
Doctor Philosophy
Lightweight and Secure Deep Learning-based Mobile Recommender Systems
Principal Advisor
Other advisors: Professor Helen Huang
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2020
Doctor Philosophy
Sequence Modelling for E-Commerce
Principal Advisor
Other advisors: Professor Xue Li
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2020
Doctor Philosophy
Graph Representation Learning with Attribute Information
Principal Advisor
Other advisors: Professor Xue Li
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2020
Master Philosophy
Advanced Machine Learning Algorithms for Discrete Datasets
Principal Advisor
Other advisors: Professor Shazia Sadiq
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2017
Doctor Philosophy
POINT OF INTERESTS RECOMMENDATION IN LOCATION-BASED SOCIAL NETWORKS
Principal Advisor
Other advisors: Professor Shazia Sadiq
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2025
Doctor Philosophy
Understanding and mitigating greenhouse gas emissions from wastewater system in the data era
Associate Advisor
Other advisors: Dr Haoran Duan, Professor Liu Ye
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2023
Doctor Philosophy
Multi-modal Data Modeling with Awareness of Efficiency, Reliability, and Privacy
Associate Advisor
Other advisors: Professor Helen Huang
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2022
Doctor Philosophy
Neural Attentive Recommender Systems
Associate Advisor
Other advisors: Professor Helen Huang, Dr Rocky Chen
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2022
Master Philosophy
An exploration into the correlation between users' intentions and candidates for query- and non-query-based retrieval
Associate Advisor
Other advisors: Professor Helen Huang
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2021
Doctor Philosophy
Towards Efficient Similarity Search with Semantic Hashing Techniques
Associate Advisor
Other advisors: Professor Helen Huang
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2021
Doctor Philosophy
Multimedia Content Analytics with Modality Transition
Associate Advisor
Other advisors: Professor Helen Huang
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2018
Doctor Philosophy
Understand Video Event by Exploiting Semantic and Temporal Information for Classification and Retrieval
Associate Advisor
Other advisors: Professor Helen Huang
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Doctor Philosophy
Modelling Sequential Patterns of User Behaviour in Recommender Systems
Associate Advisor
Other advisors: Professor Helen Huang
Media
Enquiries
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