Skip to menu Skip to content Skip to footer
Professor Hongzhi Yin
Professor

Hongzhi Yin

Email: 
Phone: 
+61 7 336 54739

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

  1. [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.

  2. [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).

  3. [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.

  4. [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.

  5. [4 April 2025] We have four full research papers accepted by the top conference SIGIR 2025 (CORE A*, CCF A).

  6. [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.

  7. [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).

  8. [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.

  9. [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.

  10. [26 January 2025] Our survey paper "Graph Condensation: A Survey" has been accepted by TKDE 2025 (CORE A*, CCF A).

  11. [20 January 2025] We have three full research papers and one demo paper accepted by the top conference WWW 2025 (CORE A*, CCF A).

  12. [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

  • Recommender System and User Modeling

  • Graph Mining and Embedding

  • Decentralized and Federated Learning

  • Edge Machine Learning and Applications

  • Trustworthy Machine Learning and Applications

  • QA, Chatbot and Information Retrieval

  • Time Series and Sequence Mining and Prediction

  • Spatiotemporal Data Mining

  • 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

361 works between 2011 and 2025

81 - 100 of 361 works

2023

Journal Article

Structure learning via meta-hyperedge for dynamic rumor detection

Sun, Xiangguo, Yin, Hongzhi, Liu, Bo, Meng, Qing, Cao, Jiuxin, Zhou, Alexander and Chen, Hongxu (2023). Structure learning via meta-hyperedge for dynamic rumor detection. IEEE Transactions on Knowledge and Data Engineering, 35 (9), 9128-9139. doi: 10.1109/tkde.2022.3221438

Structure learning via meta-hyperedge for dynamic rumor detection

2023

Conference Publication

Efficient bi-level optimization for recommendation denoising

Wang, Zongwei, Gao, Min, Li, Wentao, Yu, Junliang, Guo, Linxin and Yin, Hongzhi (2023). Efficient bi-level optimization for recommendation denoising. 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, United States, 6-10 August 2023. New York, NY, United States: ACM. doi: 10.1145/3580305.3599324

Efficient bi-level optimization for recommendation denoising

2023

Journal Article

Trustworthy recommendation and search: introduction to the special section - part 2

Yin, Hongzhi, Sun, Yizhou, Xu, Guandong and Kanoulas, Evangelos (2023). Trustworthy recommendation and search: introduction to the special section - part 2. ACM Transactions on Information Systems, 41 (4) 82, 1-6. doi: 10.1145/3604776

Trustworthy recommendation and search: introduction to the special section - part 2

2023

Conference Publication

Manipulating federated recommender systems: poisoning with synthetic users and its countermeasures

Yuan, Wei, Nguyen, Quoc Viet Hung, He, Tieke, Chen, Liang and Yin, Hongzhi (2023). Manipulating federated recommender systems: poisoning with synthetic users and its countermeasures. 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, 23-27 July 2023. New York, NY, United States: ACM. doi: 10.1145/3539618.3591722

Manipulating federated recommender systems: poisoning with synthetic users and its countermeasures

2023

Conference Publication

Model-agnostic decentralized collaborative learning for on-device POI recommendation

Long, Jing, Chen, Tong, Nguyen, Quoc Viet Hung, Xu, Guandong, Zheng, Kai and Yin, Hongzhi (2023). Model-agnostic decentralized collaborative learning for on-device POI recommendation. 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, 23-27 July 2023. New York, NY, United States: ACM. doi: 10.1145/3539618.3591733

Model-agnostic decentralized collaborative learning for on-device POI recommendation

2023

Conference Publication

DREAM: adaptive reinforcement learning based on attention mechanism for temporal knowledge graph reasoning

Zheng, Shangfei, Yin, Hongzhi, Chen, Tong, Nguyen, Quoc Viet Hung, Chen, Wei and Zhao, Lei (2023). DREAM: adaptive reinforcement learning based on attention mechanism for temporal knowledge graph reasoning. 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, 23–27 July 2023. New York, NY, United States: ACM. doi: 10.1145/3539618.3591671

DREAM: adaptive reinforcement learning based on attention mechanism for temporal knowledge graph reasoning

2023

Conference Publication

Continuous input embedding size search for recommender systems

Qu, Yunke, Chen, Tong, Zhao, Xiangyu, Cui, Lizhen, Zheng, Kai and Yin, Hongzhi (2023). Continuous input embedding size search for recommender systems. The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, 23-27 July 2023. New York, NY, United States: ACM. doi: 10.1145/3539618.3591653

Continuous input embedding size search for recommender systems

2023

Conference Publication

KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment

Wang, Lingzhi, Chen, Tong, Yuan, Wei, Zeng, Xingshan, Wong, Kam-Fai and Yin, Hongzhi (2023). KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment. 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, 9 - 14 July 2023. Stroudsburg, PA United States: Association for Computational Linguistics.

KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment

2023

Journal Article

Reinforcement learning-enhanced shared-account cross-domain sequential recommendation

Guo, Lei, Zhang, Jinyu, Chen, Tong, Wang, Xinhua and Yin, Hongzhi (2023). Reinforcement learning-enhanced shared-account cross-domain sequential recommendation. IEEE Transactions on Knowledge and Data Engineering, 35 (7), 7397-7411. doi: 10.1109/tkde.2022.3185101

Reinforcement learning-enhanced shared-account cross-domain sequential recommendation

2023

Journal Article

Spatial-temporal meta-path guided explainable crime prediction

Sun, Yuting, Chen, Tong and Yin, Hongzhi (2023). Spatial-temporal meta-path guided explainable crime prediction. World Wide Web, 26 (4), 2237-2263. doi: 10.1007/s11280-023-01137-3

Spatial-temporal meta-path guided explainable crime prediction

2023

Journal Article

Trustworthy Recommendation and Search: Introduction to the Special Issue - Part 1

Yin, Hongzhi, Sun, Yizhou, Xu, Guandong and Kanoulas, Evangelos (2023). Trustworthy Recommendation and Search: Introduction to the Special Issue - Part 1. ACM Transactions on Information Systems, 41 (3) 51, 1-5. doi: 10.1145/3579995

Trustworthy Recommendation and Search: Introduction to the Special Issue - Part 1

2023

Conference Publication

Semi-decentralized federated ego graph learning for recommendation

Qu, Liang, Tang, Ningzhi, Zheng, Ruiqi, Nguyen, Quoc Viet Hung, Huang, Zi, Shi, Yuhui and Yin, Hongzhi (2023). Semi-decentralized federated ego graph learning for recommendation. The ACM Web Conference 2023, Austin, TX, United States, 30 April - 4 May 2023. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3543507.3583337

Semi-decentralized federated ego graph learning for recommendation

2023

Conference Publication

Interaction-level membership inference attack against federated recommender systems

Yuan, Wei, Yang, Chaoqun, Nguyen, Quoc Viet Hung, Cui, Lizhen, He, Tieke and Yin, Hongzhi (2023). Interaction-level membership inference attack against federated recommender systems. The ACM Web Conference 2023, Austin, TX, United States, 30 April - 4 May 2023. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3543507.3583359

Interaction-level membership inference attack against federated recommender systems

2023

Edited Outputs

Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II

Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao and Hongzhi Yin eds. (2023). Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II. 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), Tianjin, China, 17-20 April 2023. Heidelberg, Germany: Springer.

Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II

2023

Edited Outputs

Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part IV

Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao and Hongzhi Yin eds. (2023). Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part IV. 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), Tianjin, China, 17-20 April 2023. Heidelberg, Germany: Springer.

Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part IV

2023

Edited Outputs

Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17-20, 2023, Proceedings, Part I

Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao and Hongzhi Yin eds. (2023). Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17-20, 2023, Proceedings, Part I. 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), Tianjin, China, 17-20 April 2023. Heidelberg, Germany: Springer.

Database systems for advanced applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17-20, 2023, Proceedings, Part I

2023

Journal Article

Interpretable signed link prediction with signed infomax hyperbolic graph

Luo, Yadan, Huang, Zi, Chen, Hongxu, Yang, Yang, Yin, Hongzhi and Baktashmotlagh, Mahsa (2023). Interpretable signed link prediction with signed infomax hyperbolic graph. IEEE Transactions on Knowledge and Data Engineering, 35 (4), 3991-4002. doi: 10.1109/TKDE.2021.3139035

Interpretable signed link prediction with signed infomax hyperbolic graph

2023

Conference Publication

Disconnected emerging knowledge graph oriented inductive link prediction

Zhang, Yufeng, Wang, Weiqing, Yin, Hongzhi, Zhao, Pengpeng, Chen, Wei and Zhao, Lei (2023). Disconnected emerging knowledge graph oriented inductive link prediction. 2023 IEEE 39th International Conference on Data Engineering (ICDE), Anaheim, CA, United States, 3-7 April 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/icde55515.2023.00036

Disconnected emerging knowledge graph oriented inductive link prediction

2023

Journal Article

Who are the best adopters? User selection model for free trial item promotion

Wang, Shiqi, Gao, Chongming, Gao, Min, Yu, Junliang, Wang, Zongwei and Yin, Hongzhi (2023). Who are the best adopters? User selection model for free trial item promotion. IEEE Transactions on Big Data, 9 (2), 746-757. doi: 10.1109/tbdata.2022.3205334

Who are the best adopters? User selection model for free trial item promotion

2023

Conference Publication

Efficient and effective multi-modal queries through heterogeneous network embedding (extended abstract)

Tam Nguyen, Thanh, Thang Duong, Chi, Yin, Hongzhi, Weidlich, Matthias, Son Mai, Thai, Aberer, Karl and Viet Hung Nguyen, Quoc (2023). Efficient and effective multi-modal queries through heterogeneous network embedding (extended abstract). 39th International Conference on Data Engineering (ICDE), Anaheim, CA, United States, 3-7 April 2023. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/icde55515.2023.00322

Efficient and effective multi-modal queries through heterogeneous network embedding (extended abstract)

Funding

Current funding

  • 2025 - 2028
    Building an Aussie Information Recommendation System You Can Trust
    ARC Linkage Projects
    Open grant
  • 2024 - 2027
    Privacy-Aware and Personalised Explanation Overlays for Recommender Systems (ARC Discovery Project administered by Griffith University)
    ARC Discovery Projects
    Open grant
  • 2022 - 2026
    Decentralised Collaborative Predictive Analytics on Personal Smart Devices
    ARC Future Fellowships
    Open grant
  • 2021 - 2026
    ARC Training Centre for Information Resilience
    ARC Industrial Transformation Training Centres
    Open grant

Past funding

  • 2022 - 2023
    A Secured Smart Sensing and Industry Analytics Facility for Industry 4.0 (ARC LIEF application led by University of Technology Sydney)
    University of Technology Sydney
    Open grant
  • 2020 - 2021
    Developing a Privacy-Preserving and Energy-Efficient Mobile Recommender System Architecture
    UQ Foundation Research Excellence Awards
    Open grant
  • 2019 - 2024
    Challenging Big Data for Scalable, Robust and Real-time Recommendations
    ARC Discovery Projects
    Open grant
  • 2017 - 2020
    Monitoring Social Events for User Online Behaviour Analytics
    ARC Discovery Projects
    Open grant
  • 2016 - 2018
    Mobile User Modeling for Intelligent Recommendation
    ARC Discovery Early Career Researcher Award
    Open grant

Supervision

Availability

Professor Hongzhi Yin is:
Available for supervision

Before you email them, read our advice on how to contact a supervisor.

Available projects

  • 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

  • Doctor Philosophy

    Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Joint Feature Learning for Recommender System

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    LLM-enhanced Recommender System

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Scalable and Generalizable Graph Neural Networks

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Integrated high-throughput material synthesis and characterisation system

    Associate Advisor

    Other advisors: Associate Professor Jingwei Hou

  • Doctor Philosophy

    Scalable and Lightweight On-Device Recommender Systems

    Associate Advisor

    Other advisors: Dr Rocky Chen, Dr Junliang Yu

  • Doctor Philosophy

    Scalable and Lightweight On-Device Recommender Systems

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Sustainable On-Device Recommender Systems

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Causal Analysis for Decision Support in Public Health

    Associate Advisor

    Other advisors: Professor Shazia Sadiq, Dr Rocky Chen

  • Doctor Philosophy

    Lightweight Graph Neural Networks for Recommendation

    Associate Advisor

    Other advisors: Dr Rocky Chen

Completed supervision

Media

Enquiries

For media enquiries about Professor Hongzhi Yin's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au