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, 2024 and 2025 Computer Science in Australia Leader Award, AI 2000 Most Influential Scholar Honorable Mention in Data Mining (2022-2025), 2024 and 2025 ScholarGPS Highly Ranked Scholar (top 0.05%). 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 360+ papers with an H-index of 88 (25000+ citations), including 270+ CCF A/CORE A* and 80+ 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 280+. 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. [9 December 2025] I have been recognized as 2025 ScholarGPS Highly Ranked Scholar (top 0.05% of all scholars), #3 in Data Mining, #8 in Information Engineering.

  2. [24 November 2025] Our research paper "ProEx: A Unified Framework Leveraging Large Language Model with Profile Extrapolation for Recommendation" was accepted by the top conference KDD 2026 (CCF A and CORE A*). Congratulations to Yi.

  3. [8 November 2025] Our research paper "SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber World" was accepted by the top conference AAAI 2026 (CCF A and CORE A*). Congratulations to Jiaqi.

  4. [4 November 2025] We have released the first survey on Reasoning-Aware Recommender Systems in the LLM Era.

  5. [28 October 2025] My ARC Discovery Project 2026 "Advancing Federated Learning for Unified Urban Spatio-Temporal Predictions" has been successfully granted and funded.

  6. [13 October 2025] I was invited to be Area Chair for ACL Rolling Review (ARR).

  7. [1 October 2025] I have been recognised in the Stanford/Elsevier Top 2% Scientists List Career Long (2022-2025) and Single Year (2020-2025).

  8. [25 Sepbember] I was invited to be an SPC for dual tracks of The Web Conference 2026.

  9. [28 August 2025] I have been recognised in the "2025 AI 2000 Global Artificial Intelligence Scholars List" and awarded the "2025 AI 2000 Most Influential Scholar Award Honorable Mention" in both areas of Data Mining (Ranked #43) and IR and Recommendation (Ranked #60).

  10. [26 August 2025] Our research work "Towards Propagation-aware Representation Learning for Supervised Social Media Graph Analytics" was accetped as regular research paper by the top confernce ICDM 2025 (CORE A*, acceptance rate 13.5%).

  11. [5 August 2025] We have 4 research papers accepted by the top conference CIKM 2025 (CORE A).

  12. [10 July 2025] Our survey paper "On-Device Recommender Systems: A Comprehensive Survey" has been accepted by Data Science and Engineering (Q1, 中科院一区).

  13. [25 June 2025] Our ARC Linkage Project "Revolutionise Australian Strata Management with Large Language Model" has been granted and funded.

  14. [5 May 2025] I was invited to serve as Area Chair for the top data mining conference ICDM 2025 (CORE A*).

  15. [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.

  16. [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).

  17. [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.

  18. [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.

  19. [4 April 2025] We have four full research papers accepted by the top conference SIGIR 2025 (CORE A*, CCF A).

  20. [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.

  21. [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).

  22. [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.

  23. [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.

  24. [26 January 2025] Our survey paper "Graph Condensation: A Survey" has been accepted by TKDE 2025 (CORE A*, CCF A).

  25. [20 January 2025] We have three full research papers and one demo paper accepted by the top conference WWW 2025 (CORE A*, CCF A).

  26. [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

399 works between 2011 and 2026
All (399) Book (2) Journal Article (155) Edited Outputs (6) Conference Publication (233) Book Chapter (3)

341 - 360 of 399 works

2018

Conference Publication

Call attention to rumors: deep attention based recurrent neural networks for early rumor detection

Chen, Tong, Li, Xue, Yin, Hongzhi and Zhang, Jun (2018). Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018, Melbourne, VIC, Australia, 3 June 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-04503-6_4

Call attention to rumors: deep attention based recurrent neural networks for early rumor detection

2018

Conference Publication

Mining geo-social networks - spatial item recommendation

Yin, Hongzhi and Wang, Weiqing (2018). Mining geo-social networks - spatial item recommendation. 29th Australasian Database Conference (ADC), Gold Coast, Australia, 24-27 May 2018. Cham, Switzerland: Springer.

Mining geo-social networks - spatial item recommendation

2018

Conference Publication

Discrete deep learning for fast content-aware recommendation

Zhang, Yan, Yin, Hongzhi, Huang, Zi, Du, Xingzhong, Yang, Guowu and Lian, Defu (2018). Discrete deep learning for fast content-aware recommendation. 11th ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, United States, 5-9 February 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3159652.3159688

Discrete deep learning for fast content-aware recommendation

2018

Conference Publication

Stock assistant: a stock AI assistant for reliability modeling of stock comments

Zhang, Chen, Du, Changying, Wang, Yijun, Yin, Hongzhi, Chen, Can and Wang, Hao (2018). Stock assistant: a stock AI assistant for reliability modeling of stock comments. 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: 10.1145/3219819.3219964

Stock assistant: a stock AI assistant for reliability modeling of stock comments

2018

Conference Publication

Effective and efficient user account linkage across location based social networks

Chen, Wei, Yin, Hongzhi, Wang, Weiqing, Zhao, Lei and Zhou, Xiaofang (2018). Effective and efficient user account linkage across location 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.2018.00101

Effective and efficient user account linkage across location based social networks

2018

Conference Publication

What-If analysis with conflicting goals: recommending data ranges for exploration

Nguyen, Quoc Viet Hung, Zheng, Kai, Weidlich, Matthias, Zheng, Bolong, Yin, Hongzhi, Nguyen, Thanh Tam and Stantic, Bela (2018). What-If analysis with conflicting goals: recommending data ranges for exploration. 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, April 16 - 19, 2018. Los Alamitos, CA, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDE.2018.00018

What-If analysis with conflicting goals: recommending data ranges for exploration

2018

Conference Publication

Restricted boltzmann machine based active learning for sparse recommendation

Wang, Weiqing, Yin, Hongzhi, Huang, Zi, Sun, Xiaoshuai and Hung, Nguyen Quoc Viet (2018). Restricted boltzmann machine based active learning for sparse recommendation. 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, Gold Coast, QLD Australia, 21 - 24 May 2018. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-91452-7_7

Restricted boltzmann machine based active learning for sparse recommendation

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

Understanding the user display names across social networks

Li, Yongjun, Peng, You, Zhang, Zhen, Xu, Quanqing and Yin, Hongzhi (2017). Understanding the user display names across social networks. International Conference on World Wide Web Companion, Perth, Australia, 3-7 April 2017. Geneva, Switzerland: International World Wide Web Conferences Steering Committee. doi: 10.1145/3041021.3051146

Understanding the user display names across social networks

2017

Conference Publication

An integrated model for effective saliency prediction

Sun, Xiaoshuai, Huang, Zi, Yin, Hongzhi and Shen, Heng Tao (2017). An integrated model for effective saliency prediction. 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, CA., United States, 04-10 February 2017. Palo Alto, CA., United States: AAAI press.

An integrated model for effective saliency prediction

2017

Conference Publication

Recommendation in context-rich environment: An information network analysis approach

Sun, Yizhou, Yin, Hongzhi and Ren, Xiang (2017). Recommendation in context-rich environment: An information network analysis approach. 26th International World Wide Web Conference, WWW 2017 Companion, Perth, WA, Australia, April 3 - 7, 2017. Geneva, Switzerland: International World Wide Web Conferences Steering Committee. doi: 10.1145/3041021.3051105

Recommendation in context-rich environment: An information network analysis approach

2017

Conference Publication

Mobi-SAGE: A sparse additive generative model for mobile app recommendation

Yin, Hongzhi, Chen, Liang, Wang, Weiqing, Du, Xingzhong, Nguyen, Quoc Viet Hung and Zhou, Xiaofang (2017). Mobi-SAGE: A sparse additive generative model for mobile app recommendation. IEEE 33rd International Conference on Data Engineering (ICDE), San Diego, CA, United States, 19-22 April 2017. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDE.2017.43

Mobi-SAGE: A sparse additive generative model for mobile app recommendation

2017

Conference Publication

An integrated model for effective saliency prediction

Sun, Xiaoshuai, Huang, Zi, Yin, Hongzhi and Shen, Heng Tao (2017). An integrated model for effective saliency prediction. AAAI Conference on Artificial Intelligence, San Francisco, CA, United States, 4-9 February 2017. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence.

An integrated model for effective saliency prediction

2017

Conference Publication

A time and sentiment unification model for personalized recommendation

Wang, Qinyong , Yin, Hongzhi and Wang, Hao (2017). A time and sentiment unification model for personalized recommendation. Joint Conference, APWeb-WAIM, Beijing, China, 7-9 July 2017. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-63564-4 8

A time and sentiment unification model for personalized recommendation

2017

Conference Publication

Influenced nodes discovery in temporal contact network

Huang, Jinjing, Lin, Tianqiao, Liu, An, Li, Zhixu, Yin, Hongzhi and Zhao, Lei (2017). Influenced nodes discovery in temporal contact network. 18th International Conference on Web Information Systems Engineering, WISE 2017, Puschino, Russia, 7-11 October 2017. Heidelberg, Germany: Springer. doi: 10.1007/978-3-319-68783-4_32

Influenced nodes discovery in temporal contact network

2017

Conference Publication

People opinion topic model: opinion based user clustering in social networks

Chen, Hongxu, Yin, Hongzhi, Li, Xue, Wang, Meng, Chen, Weitong and Chen, Tong (2017). People opinion topic model: opinion based user clustering in social networks. International Conference on World Wide Web Companion, Perth, Australia, 3-7 April 2017. Geneva, Switzerland: International World Wide Web Conferences Steering Committee. doi: 10.1145/3041021.3051159

People opinion topic model: opinion based user clustering in social networks

Current funding

  • 2026 - 2029
    Advancing Federated Learning for Unified Urban Spatio-Temporal Predictions
    ARC Discovery Projects
    Open grant
  • 2025 - 2028
    Revolutionise Australian Strata Management with Large Language Models (ARC Linkage Project - UQ Led)
    ARC Linkage Projects
    Open grant
  • 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

Looking for a supervisor? Read our advice on how to choose 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

Completed supervision

Enquiries

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communications@uq.edu.au