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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, 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.

Works

Search Professor Hongzhi Yin’s works on UQ eSpace

399 works between 2011 and 2026

281 - 300 of 399 works

2019

Journal Article

An efficient framework for multiple subgraph pattern matching models

Gao, Jiu-Ru, Chen, Wei, Xu, Jia-Jie, Liu, An, Li, Zhi-Xu, Yin, Hongzhi and Zhao, Lei (2019). An efficient framework for multiple subgraph pattern matching models. Journal of Computer Science and Technology, 34 (6), 1185-1202. doi: 10.1007/s11390-019-1969-x

An efficient framework for multiple subgraph pattern matching models

2019

Journal Article

Online sales prediction via trend alignment-based multitask recurrent neural networks

Chen, Tong, Yin, Hongzhi, Chen, Hongxu, Wang, Hao, Zhou, Xiaofang and Li, Xue (2019). Online sales prediction via trend alignment-based multitask recurrent neural networks. Knowledge and Information Systems, 62 (6), 2139-2167. doi: 10.1007/s10115-019-01404-8

Online sales prediction via trend alignment-based multitask recurrent neural networks

2019

Journal Article

Group-level personality detection based on text generated networks

Sun, Xiangguo, Liu, Bo, Meng, Qing, Cao, Jiuxin, Luo, Junzhou and Yin, Hongzhi (2019). Group-level personality detection based on text generated networks. World Wide Web, 23 (3), 1887-1906. doi: 10.1007/s11280-019-00729-2

Group-level personality detection based on text generated networks

2019

Journal Article

Semi-supervised clustering with deep metric learning and graph embedding

Li, Xiaocui, Yin, Hongzhi, Zhou, Ke and Zhou, Xiaofang (2019). Semi-supervised clustering with deep metric learning and graph embedding. World Wide Web, 23 (2), 781-798. doi: 10.1007/s11280-019-00723-8

Semi-supervised clustering with deep metric learning and graph embedding

2019

Journal Article

Efficient user guidance for validating participatory sensing data

Cong, Phan Thanh, Tam, Nguyen Thanh, Yin, Hongzhi, Zheng, Bolong, Stantic, Bela and Hung, Nguyen Quoc Viet (2019). Efficient user guidance for validating participatory sensing data. ACM Transactions on Intelligent Systems and Technology, 10 (4) 37, 1-30. doi: 10.1145/3326164

Efficient user guidance for validating participatory sensing data

2019

Conference Publication

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

Shang, Mingyue, Fu, Zhenxin, Yin, Hongzhi, Tang, Bo, Zhao, Dongyan and Yan, Rui (2019). Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?. The Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, HI United States, 27 January – 1 February 2019. Menlo Park, CA United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v33i01.330110031

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

2019

Book Chapter

Spatiotemporal recommendation with big geo-social networking data

Wang, Weiqing and Yin, Hongzhi (2019). Spatiotemporal recommendation with big geo-social networking data. Big data recommender systems - Volume 1: Algorithms, architectures, big data, security and trust. (pp. 193-224) edited by Osman Khalid, Samee U. Khan and Albert Y. Zomaya. Stevenage, United Kingdom: The Institution of Engineering and Technology. doi: 10.1049/pbpc035f_ch9

Spatiotemporal recommendation with big geo-social networking data

2019

Journal Article

MCP: a multi-component learning machine to predict protein secondary structure

Khalatbari, Leila, Kangavari, M. R., Hosseini, Saeid, Yin, Hongzhi and Cheung, Ngai-Man (2019). MCP: a multi-component learning machine to predict protein secondary structure. Computers in Biology and Medicine, 110, 144-155. doi: 10.1016/j.compbiomed.2019.04.040

MCP: a multi-component learning machine to predict protein secondary structure

2019

Journal Article

Leveraging multi-aspect time-related influence in location recommendation

Hosseini, Saeid, Yin, Hongzhi, Zhou, Xiaofang, Sadiq, Shazia, Kangavari, Mohammad Reza and Cheung, Ngai-Man (2019). Leveraging multi-aspect time-related influence in location recommendation. World Wide Web, 22 (3), 1001-1028. doi: 10.1007/s11280-018-0573-2

Leveraging multi-aspect time-related influence in location recommendation

2019

Journal Article

Spatiotemporal representation learning for translation-based POI recommendation

Qian, Tieyun, Liu, Bei, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2019). Spatiotemporal representation learning for translation-based POI recommendation. ACM Transactions on Information Systems, 37 (2) 18, 1-24. doi: 10.1145/3295499

Spatiotemporal representation learning for translation-based POI recommendation

2019

Conference Publication

Inferring substitutable products with deep network embedding

Zhang, Shijie, Yin, Hongzhi, Wang, Qinyong, Chen, Tong, Chen, Hongxu and Nguyen, Quoc Viet Hung (2019). Inferring substitutable products with deep network embedding. International Joint Conference on Artificial Intelligence, Macao, China, 10-16 August 2019. California: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2019/598

Inferring substitutable products with deep network embedding

2019

Conference Publication

Multi-hop path queries over knowledge graphs with neural memory networks

Wang, Qinyong, Yin, Hongzhi, Wang, Weiqing, Huang, Zi, Guo, Guibing and Nguyen, Quoc Viet Hung (2019). Multi-hop path queries over knowledge graphs with neural memory networks. 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22 - 25 April 2019. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-18576-3_46

Multi-hop path queries over knowledge graphs with neural memory networks

2019

Conference Publication

Enhancing collaborative filtering with generative augmentation

Wang, Qinyong, Nguyen, Quoc Viet Hung, Yin, Hongzhi, Huang, Zi, Wang, Hao and Cui, Lizhen (2019). Enhancing collaborative filtering with generative augmentation. 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), Anchorage, AK United States, 4-8 August 2019. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3292500.3330873

Enhancing collaborative filtering with generative augmentation

2019

Journal Article

Spatiotemporal recommendation with big geo-social networking data

Wang, Weiqing and Yin, Hongzhi (2019). Spatiotemporal recommendation with big geo-social networking data. IET Professional Applications of Computing Series, 35, 193-224.

Spatiotemporal recommendation with big geo-social networking data

2019

Conference Publication

Generating reliable friends via adversarial training to improve social recommendation

Yu, Junliang, Gao, Min, Yin, Hongzhi, Li, Jundong, Gao, Chongming and Wang, Qinyong (2019). Generating reliable friends via adversarial training to improve social recommendation. IEEE International Conference on Data Mining , Beijing, China, 8-11 November 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDM.2019.00087

Generating reliable friends via adversarial training to improve social recommendation

2019

Conference Publication

Social influence-based group representation learning for group recommendation

Yin, Hongzhi, Wang, Qinyong, Zheng, Kai, Li, Zhixu, Yang, Jiali and Zhou, Xiaofang (2019). Social influence-based group representation learning for group recommendation. 35th International Conference on Data Engineering (ICDE 2019), Macao, Macao, 8-11 April 2019. New York, NY, United States: IEEE Computer Society. doi: 10.1109/ICDE.2019.00057

Social influence-based group representation learning for group recommendation

2019

Conference Publication

AIR: Attentional intention-aware recommender systems

Chen, Tong, Yin, Hongzhi, Chen, Hongxu, Yan, Rui, Nguyen, Quoc Viet Hung and Li, Xue (2019). AIR: Attentional intention-aware recommender systems. IEEE 35th International Conference on Data Engineering (ICDE), Macau, China, 8-11 April 2019. Piscataway, NJ United States: IEEE Computer Society. doi: 10.1109/ICDE.2019.00035

AIR: Attentional intention-aware recommender systems

2019

Conference Publication

Rethinking the item order in session-based recommendation with graph neural networks

Qiu, Ruihong, Li, Jingjing, Huang, Zi and Yin, Hongzhi (2019). Rethinking the item order in session-based recommendation with graph neural networks. CIKM '19 28th ACM International Conference on Information and Knowledge Management, Beijing, China, 3 - 7 November, 2019. New York, New York, USA: ACM Press. doi: 10.1145/3357384.3358010

Rethinking the item order in session-based recommendation with graph neural networks

2019

Conference Publication

Online user representation learning across heterogeneous social networks

Wang, Weiqing, Yin, Hongzhi, Du, Xingzhong, Hua, Wen, Li, Yongjun and Nguyen, Quoc Viet Hung (2019). Online user representation learning across heterogeneous social networks. 42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), Paris, France, 21-25 July 2019. New York, NY, United States: Association for Computing Machinery (ACM). doi: 10.1145/3331184.3331258

Online user representation learning across heterogeneous social networks

2019

Conference Publication

Semi-supervised Clustering with Deep Metric Learning

Li, Xiaocui, Yin, Hongzhi, Zhou, Ke, Chen, Hongxu, Sadiq, Shazia and Zhou, Xiaofang (2019). Semi-supervised Clustering with Deep Metric Learning. 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22-15 April 2019. Heidelberg, Germany: Springer . doi: 10.1007/978-3-030-18590-9_50

Semi-supervised Clustering with Deep Metric Learning

Funding

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

Supervision

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

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