Professor Hongzhi Yin
Professor

Hongzhi Yin

Email: 
Phone: 
+61 7 336 54739

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.

Search Professor Hongzhi Yin’s works on UQ eSpace

361 works between 2011 and 2025
All (361) Journal Article (140) Book Chapter (4) Conference Publication (209) Book (2) Edited Outputs (6)

301 - 320 of 361 works

2018

Conference Publication

Modeling patient visit using electronic medical records for cost profile estimation

Zhao, Kangzhi, Zhang, Yong, Wang, Zihao, Yin, Hongzhi, Zhou, Xiaofang, Wang, Jin and Xing, Chunxiao (2018). Modeling patient visit using electronic medical records for cost profile estimation. 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, Gold Coast, QLD, Australia, 21-24 May 2018. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-91458-9_2

Modeling patient visit using electronic medical records for cost profile estimation

2018

Conference Publication

Joint event-partner recommendation in event-based social networks

Yin, Hongzhi, Zou, Lei, Nguyen, Quoc Viet Hung, Huang, Zi and Zhou, Xiaofang (2018). Joint event-partner recommendation in event-based social networks. 34th IEEE International Conference on Data Engineering (ICDE 2018), Paris, France, 16-19 April 2018. NEW YORK: IEEE. doi: 10.1109/ICDE.2018.00088

Joint event-partner recommendation in event-based social networks

2018

Conference Publication

Extracting representative user subset of social networks towards user characteristics and topological features

Zhou, Yiming, Han, Yuehui, Liu, An, Li, Zhixu, Yin, Hongzhi and Zhao, Lei (2018). Extracting representative user subset of social networks towards user characteristics and topological features. 19th International Conference on Web Information Systems Engineering, WISE 2018, Dubai, United Arab Emirates, November 12 - 15, 2018. Cham, Switzerland: Springer . doi: 10.1007/978-3-030-02922-7_15

Extracting representative user subset of social networks towards user characteristics and topological features

2018

Conference Publication

PME: projected metric embedding on heterogeneous networks for link prediction

Chen, Hongxu, Wang, Hao, Yin, Hongzhi, Nguyen, Quoc Viet Hung, Wang, Weiqing and Li, Xue (2018). PME: projected metric embedding on heterogeneous networks for link prediction. 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, United Kingdom, 19 - 23 August 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3219819.3219986

PME: projected metric embedding on heterogeneous networks for link prediction

2018

Conference Publication

Discrete ranking-based matrix factorization with self-paced learning

Zhang, Yan, Wang, Haoyu, Lian, Defu, Tsang, Ivor W., Yin, Hongzhi and Yang, Guowu (2018). Discrete ranking-based matrix factorization with self-paced learning. 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), London, United Kingdom, 19 - 23 August 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3219819.3220116

Discrete ranking-based matrix factorization with self-paced learning

2018

Journal Article

Computing crowd consensus with partial agreement

Viet Hung, Nguyen Quoc, Viet, Huynh Huu, Tam, Nguyen Thanh, Weidlich, Matthias, Yin, Hongzhi and Zhou, Xiaofang (2018). Computing crowd consensus with partial agreement. IEEE Transactions on Knowledge and Data Engineering, 30 (1), 1-14. doi: 10.1109/TKDE.2017.2750683

Computing crowd consensus with partial agreement

2018

Conference Publication

Neural memory streaming recommender networks with adversarial training

Wang, Qinyong, Lian, Defu, Yin, Hongzhi, Wang, Hao, Hu, Zhiting and Huang, Zi (2018). Neural memory streaming recommender networks with adversarial training. 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, United Kingdom, 19 - 23 August 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3219819.3220004

Neural memory streaming recommender networks with adversarial training

2018

Conference Publication

LC-RNN: A deep learning model for traffic speed prediction

Lv, Zhongjian, Xu, Jiajie, Zheng, Kai, Yin, Hongzhi, Zhao, Pengpeng and Zhou, Xiaofang (2018). LC-RNN: A deep learning model for traffic speed prediction. 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden, 13-19 July 2018. FREIBURG: International Joint Conferences on Artificial Intelligence. doi: 10.24963/ijcai.2018/482

LC-RNN: A deep learning model for traffic speed prediction

2018

Conference Publication

User guidance for efficient fact checking

Nguyen, Thanh Tam, Weidlich, Matthias, Yin, Hongzhi, Zheng, Bolong, Hung Nguyen, Quoc Viet and Stantic, Bela (2018). User guidance for efficient fact checking. 45th International Conference on Very Large Data Bases, Los Angeles, CA United States, 2019. New York, NY United States: Association for Computing Machinery. doi: 10.14778/3324301.3324303

User guidance for efficient fact checking

2017

Journal Article

Spatial-aware hierarchical collaborative deep learning for POI recommendation

Yin, Hongzhi, Wang, Weiqing, Wang, Hao, Chen, Ling and Zhou, Xiaofang (2017). Spatial-aware hierarchical collaborative deep learning for POI recommendation. Ieee Transactions On Knowledge and Data Engineering, 29 (11) 8013107, 2537-2551. doi: 10.1109/TKDE.2017.2741484

Spatial-aware hierarchical collaborative deep learning for POI recommendation

2017

Journal Article

Exploiting detected visual objects for frame-level video filtering

Du, Xingzhong, Yin, Hongzhi, Huang, Zi, Yang, Yi and Zhou, Xiaofang (2017). Exploiting detected visual objects for frame-level video filtering. World Wide Web, 21 (5), 1-26. doi: 10.1007/s11280-017-0505-6

Exploiting detected visual objects for frame-level video filtering

2017

Journal Article

Answer validation for generic crowdsourcing tasks with minimal efforts

Hung, Nguyen Quoc Viet, Thang, Duong Chi, Tam, Nguyen Thanh, Weidlich, Matthias, Aberer, Karl, Yin, Hongzhi and Zhou, Xiaofang (2017). Answer validation for generic crowdsourcing tasks with minimal efforts. Vldb Journal, 26 (6), 855-880. doi: 10.1007/s00778-017-0484-3

Answer validation for generic crowdsourcing tasks with minimal efforts

2017

Journal Article

Argument discovery via crowdsourcing

Nguyen, Quoc Viet Hung, Duong, Chi Thang, Nguyen, Thanh Tam, Weidlich, Matthias, Aberer, Karl, Yin, Hongzhi and Zhou, Xiaofang (2017). Argument discovery via crowdsourcing. VLDB Journal, 26 (4), 511-535. doi: 10.1007/s00778-017-0462-9

Argument discovery via crowdsourcing

2017

Journal Article

ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation

Wang, Weiqing , Yin, Hongzhi , Chen, Ling , Sun, Yizhou , Sadiq, Shazia and Zhou, Xiaofang (2017). ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation. ACM Transactions on Intelligent Systems and Technology, 8 (3) 48, 48.1-48.25. doi: 10.1145/3011019

ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation

2017

Conference Publication

Graph-based metric embedding for next POI recommendation

Xie, Min, Yin, Hongzhi, Xu, Fanjiang, Wang, Hao and Zhou, Xiaofang (2017). Graph-based metric embedding for next POI recommendation. 17th International Conference on Web Information Systems Engineering (WISE), Shanghai, China, 8 - 10 November 2016. Heidelberg, Germany: Springer . doi: 10.1007/978-3-319-48743-4_17

Graph-based metric embedding for next POI recommendation

2017

Conference Publication

A location-sentiment-aware recommender system for both home-town and out-of-town users

Wang, Hao , Fu, Yanmei , Wang, Qinyong , Yin, Hongzhi , Du, Changying and Xiong, Hui (2017). A location-sentiment-aware recommender system for both home-town and out-of-town users. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Halifax, NS, Canada, 13-17 August 2017. New York, NY, United States: ACM. doi: 10.1145/3097983.3098122

A location-sentiment-aware recommender system for both home-town and out-of-town users

2017

Conference Publication

Retaining data from streams of social platforms with minimal regret

Tam, Nguyen Thanh, Weidlich, Matthias, Thang, Duong Chi, Yin, Hongzhi and Hung, Nguyen Quoc Viet (2017). Retaining data from streams of social platforms with minimal regret. International Joint Conference on Artificial Intelligence, IJCAI, Melbourne, Australia, 19-25 August 2017. Melbourne, Australia: International Joint Conferences on Artificial Intelligence. doi: 10.24963/ijcai.2017/397

Retaining data from streams of social platforms with minimal regret

2017

Conference Publication

Group recommender model based on preference interaction

Zheng, Wei, Li, Bohan, Wang, Yanan, Yin, Hongzhi, Li, Xue, Guan, Donghai and Qin, Xiaolin (2017). Group recommender model based on preference interaction. 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, Singapore, Singapore, 5–6 November 2017. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-69179-4_10

Group recommender model based on preference interaction

2017

Conference Publication

Time-constrained graph pattern matching in a large temporal graph

Xu, Yanxia, Huang, Jinjing, Liu, An, Li, Zhixu, Yin, Hongzhi and Zhao, Lei (2017). Time-constrained graph pattern matching in a large temporal graph. Joint Conference, APWeb-WAIM, Beijing, China, 7-9 July 2017. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-63579-8 9

Time-constrained graph pattern matching in a large temporal graph

2017

Conference Publication

Jointly modeling heterogeneous temporal properties in location recommendation

Hosseini, Saeid, Yin, Hongzhi, Zhang, Meihui, Zhou, Xiaofang and Sadiq, Shazia (2017). Jointly modeling heterogeneous temporal properties in location recommendation. 22nd Internation Conference, DASFAA 2017, Suzhou, China, 27 - 30 March 2017. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-55753-3_31

Jointly modeling heterogeneous temporal properties in location recommendation

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

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

    Chain-of-User-Thought for Personalized Agent in Cyber World

    Principal Advisor

    Other advisors: Dr Junliang Yu

  • Doctor Philosophy

    Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    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

    Graph Condensation for Real-World Graph Representation at Scale

    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

    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

    Sustainable On-Device Recommender Systems

    Associate 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

    Causal Analysis for Decision Support in Public Health

    Associate Advisor

    Other advisors: Professor Shazia Sadiq, Dr Rocky Chen

  • Doctor Philosophy

    Scalable and Lightweight On-Device Recommender Systems

    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

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