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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

21 - 40 of 361 works

2024

Conference Publication

Physics-guided active sample reweighting for urban flow prediction

Jiang, Wei, Chen, Tong, Ye, Guanhua, Zhang, Wentao, Cui, Lizhen, Huang, Zi and Yin, Hongzhi (2024). Physics-guided active sample reweighting for urban flow prediction. 33rd ACM International Conference on Information and Knowledge Management (CIKM), Boise, ID, United States, 21-25 October 2024. New York, United States: Association for Computing Machinery. doi: 10.1145/3627673.3679738

Physics-guided active sample reweighting for urban flow prediction

2024

Conference Publication

Preference prototype-aware learning for universal cross-domain recommendation

Zhang, Yuxi, Zhang, Ji, Xu, Feiyang, Chen, Lvying, Li, Bohan, Guo, Lei and Yin, Hongzhi (2024). Preference prototype-aware learning for universal cross-domain recommendation. 33rd ACM International Conference on Information and Knowledge Management (CIKM), Boise, ID USA, 21-25 October 2024. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3627673.3679774

Preference prototype-aware learning for universal cross-domain recommendation

2024

Conference Publication

Efficient and robust regularized federated recommendation

Liu, Langming, Wang, Wanyu, Zhao, Xiangyu, Zhang, Zijian, Zhang, Chunxu, Lin, Shanru, Wang, Yiqi, Zou, Lixin, Liu, Zitao, Wei, Xuetao, Yin, Hongzhi and Li, Qing (2024). Efficient and robust regularized federated recommendation. 33rd ACM International Conference on Information and Knowledge Management (CIKM), Boise, ID, United States, 21-25 October 2024. New York, United States: Association for Computing Machinery. doi: 10.1145/3627673.3679682

Efficient and robust regularized federated recommendation

2024

Conference Publication

Watermarking recommender systems

Zhang, Sixiao, Long, Cheng, Yuan, Wei, Chen, Hongxu and Yin, Hongzhi (2024). Watermarking recommender systems. 33rd ACM International Conference on Information and Knowledge Management (CIKM), Boise, ID USA, 21-25 October 2024. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3627673.3679617

Watermarking recommender systems

2024

Conference Publication

Scalable dynamic embedding size search for streaming recommendation

Qu, Yunke, Qu, Liang, Chen, Tong, Zhao, Xiangyu, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2024). Scalable dynamic embedding size search for streaming recommendation. 33rd ACM International Conference on Information and Knowledge Management (CIKM), Boise, ID USA, 21-25 October 2024. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3627673.3679638

Scalable dynamic embedding size search for streaming recommendation

2024

Conference Publication

DNS-Rec: data-aware neural architecture search for recommender systems

Zhang, Sheng, Wang, Maolin, Zhao, Xiangyu, Guo, Ruocheng, Zhao, Yao, Zhuang, Chenyi, Gu, Jinjie, Zhang, Zijian and Yin, Hongzhi (2024). DNS-Rec: data-aware neural architecture search for recommender systems. 18h ACM Conference on Recommender Systems (RecSys), Bari, Italy, 14-18 October 2024. New York, United States: Association for Computing Machinery. doi: 10.1145/3640457.3688117

DNS-Rec: data-aware neural architecture search for recommender systems

2024

Journal Article

Manipulating recommender systems: A survey of poisoning attacks and countermeasures

Nguyen, Thanh Toan, Quoc Viet Hung, Nguyen, Nguyen, Thanh Tam, Huynh, Thanh Trung, Nguyen, Thanh Thi, Weidlich, Matthias and Yin, Hongzhi (2024). Manipulating recommender systems: A survey of poisoning attacks and countermeasures. ACM Computing Surveys, 57 (1) 3, 1-39. doi: 10.1145/3677328

Manipulating recommender systems: A survey of poisoning attacks and countermeasures

2024

Journal Article

Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention

Sakong, Darnbi, Vu, Viet Hung, Huynh, Thanh Trung, Nguyen, Phi Le, Yin, Hongzhi, Nguyen, Quoc Viet Hung and Nguyen, Thanh Tam (2024). Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention. Information Sciences, 680 121165, 121165. doi: 10.1016/j.ins.2024.121165

Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention

2024

Conference Publication

Graph condensation for open-world graph learning

Gao, Xinyi, Chen, Tong, Zhang, Wentao, Li, Yayong, Sun, Xiangguo and Yin, Hongzhi (2024). Graph condensation for open-world graph learning. KDD '24: 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25-29 August 2024. New York, NY, United States: ACM. doi: 10.1145/3637528.3671917

Graph condensation for open-world graph learning

2024

Conference Publication

Hate speech detection with generalizable target-aware fairness

Chen, Tong, Wang, Danny, Liang, Xurong, Risius, Marten, Demartini, Gianluca and Yin, Hongzhi (2024). Hate speech detection with generalizable target-aware fairness. KDD '24: 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25-29 August 2024. New York, NY, United States: ACM. doi: 10.1145/3637528.3671821

Hate speech detection with generalizable target-aware fairness

2024

Conference Publication

Unveiling vulnerabilities of contrastive recommender systems to poisoning attacks

Wang, Zongwei, Yu, Junliang, Gao, Min, Yin, Hongzhi, Cui, Bin and Sadiq, Shazia (2024). Unveiling vulnerabilities of contrastive recommender systems to poisoning attacks. 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25-29 August 2024. New York, NY, United States: ACM. doi: 10.1145/3637528.3671795

Unveiling vulnerabilities of contrastive recommender systems to poisoning attacks

2024

Conference Publication

Diffusion-based cloud-edge-device collaborative learning for next POI recommendations

Long, Jing, Ye, Guanhua, Chen, Tong, Wang, Yang, Wang, Meng and Yin, Hongzhi (2024). Diffusion-based cloud-edge-device collaborative learning for next POI recommendations. 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25-29 August 2024. New York, NY, United States: ACM. doi: 10.1145/3637528.3671743

Diffusion-based cloud-edge-device collaborative learning for next POI recommendations

2024

Journal Article

Explicit knowledge graph reasoning for conversational recommendation

Ren, Xuhui, Chen, Tong, Nguyen, Quoc Viet Hung, Cui, Lizhen, Huang, Zi and Yin, Hongzhi (2024). Explicit knowledge graph reasoning for conversational recommendation. ACM Transactions on Intelligent Systems and Technology, 15 (4) 86, 1-21. doi: 10.1145/3637216

Explicit knowledge graph reasoning for conversational recommendation

2024

Conference Publication

Poisoning decentralized collaborative recommender system and its countermeasures

Zheng, Ruiqi, Qu, Liang, Chen, Tong, Zheng, Kai, Shi, Yuhui and Yin, Hongzhi (2024). Poisoning decentralized collaborative recommender system and its countermeasures. SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, United States: Association for Computing Machinery. doi: 10.1145/3626772.3657814

Poisoning decentralized collaborative recommender system and its countermeasures

2024

Conference Publication

CaseLink: inductive graph learning for legal case retrieval

Tang, Yanran, Qiu, Ruihong, Yin, Hongzhi, Li, Xue and Huang, Zi (2024). CaseLink: inductive graph learning for legal case retrieval. 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657693

CaseLink: inductive graph learning for legal case retrieval

2024

Conference Publication

Lightweight embeddings for graph collaborative filtering

Liang, Xurong, Chen, Tong, Cui, Lizhen, Wang, Yang, Wang, Meng and Yin, Hongzhi (2024). Lightweight embeddings for graph collaborative filtering. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657820

Lightweight embeddings for graph collaborative filtering

2024

Journal Article

Personalized elastic embedding learning for on-device recommendation

Zheng, Ruiqi, Qu, Liang, Chen, Tong, Zheng, Kai, Shi, Yuhui and Yin, Hongzhi (2024). Personalized elastic embedding learning for on-device recommendation. IEEE Transactions on Knowledge and Data Engineering, 36 (7), 3363-3375. doi: 10.1109/tkde.2024.3361562

Personalized elastic embedding learning for on-device recommendation

2024

Journal Article

Bayes-enhanced multi-view attention networks for robust POI recommendation

Xia, Jiangnan, Yang, Yu, Wang, Senzhang, Yin, Hongzhi, Cao, Jiannong and Yu, Philip S. (2024). Bayes-enhanced multi-view attention networks for robust POI recommendation. IEEE Transactions on Knowledge and Data Engineering, 36 (7), 2895-2909. doi: 10.1109/tkde.2023.3329673

Bayes-enhanced multi-view attention networks for robust POI recommendation

2024

Journal Article

Heterogeneous decentralised machine unlearning with seed model distillation

Ye, Guanhua, Chen, Tong, Hung Nguyen, Quoc Viet and Yin, Hongzhi (2024). Heterogeneous decentralised machine unlearning with seed model distillation. CAAI Transactions on Intelligence Technology, 9 (3), 608-619. doi: 10.1049/cit2.12281

Heterogeneous decentralised machine unlearning with seed model distillation

2024

Journal Article

Adversarial item promotion on visually-aware recommender systems by guided diffusion

Chen, Lijian, Yuan, Wei, Chen, Tong, Ye, Guanhua, Hung, Nguyen Quoc Viet and Yin, Hongzhi (2024). Adversarial item promotion on visually-aware recommender systems by guided diffusion. ACM Transactions on Information Systems, 42 (6) 156, 1-26. doi: 10.1145/3666088

Adversarial item promotion on visually-aware recommender systems by guided diffusion

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 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

  • 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

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