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 recommendation systems, structured foundation model, spatial-temporal prediction, LLM and ChatBI, 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 400 papers with an H-index of 91 (29000+ citations), including 290+ CCF A/CORE A* and 90+ CCF B/CORE A, such as ICML, ICLR, NeurIPS, KDD, SIGIR, WWW, ACL, WSDM, SIGMOD, VLDB, ICDE, 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 300. 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 three PhD scholarships.

Latest News

  1. [20 May 2026] Our ARC Linkage Project 2025 "AI-Powered Design Co-Pilot for Reimagining Australian Single-Family Homes" has been successfully granted and funded.

  2. [16 May 2026] We have three research papers accepted by the top conference KDD 2026 Research Track (CORE A*, CCF A, Acceptance Rate ~18%).

  3. [12 May 2026] I have been recognised in 2026 Edition of Best Scientists in the field of Computer Science and ranked 42 in Australia on Research.com, a leading academic platform.

  4. [8 May 2026] Our research paper "Efficient Prompt Learning for Traffic Forecasting" has been accepted by VLDB Journal (CORE A*, CCF A)

  5. [3 April 2026] We have 3 research papers accepted by the top conference SIGIR 2026.

    • Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems

    • ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems

    • ProEchoMem: Enhancing Long Video Understanding via Multi-Trace Probe-Echo Memory

  6. [27 March 2026] We have successfully secured the opportunity to host the top-tier conference ICDM 2027 in Brisbane.

  7. [24 Feb 2026] We have 3 research papers on ChatBI accepted by the top conferences ICLR 2026 (CORE A*, CCF A) and ICDE 2026 (CORE A*, CCF A).

  8. [21 Feb 2026] I’m pleased to join the Organizing Committee of the premier conference WSDM 2027 as the Conference Awards Co-Chair.

  9. [18 Feb 2026] I’m pleased to join the Organizing Committee of the data mining flagship conference ADMA 2026 as the PC Co-Chair.

  10. [5 Feb 2026] We are organizing a workshop "LLM-UP: LLM-powered User Profiling for Search and Recommendation" at SIGIR 2026.

  11. [26 January 2026] We have 3 papers accepted by the top conference ICLR 2026 (CORE A*).

  12. [20 January 2026] I was invited to serve as Area Chair in the top conference KDD 2026 (CORE A*, CCF A), IJCAI 2026 (CORE A*, CCF A), ARR-ACL 2026 (CORE A*, CCF A) and ICDM 2026 (CORE A*, CCF B).

  13. [14 January] We have two research papers accepted by the top conference WWW 2026 (CORE A*, CCF A). Congratulations to Xinyi and Hung.

  14. [13 January 2025] We have two research papers recognized as ESI Hot Papers and five research papers recognized as ESI Highly Cited Papers.

  15. [19 December 2025] I was invited to serve as Area Chair in the top conference SIGIR 2026 (CORE A*, CCF A) and senior PC member at the top conference ICMR 2026 (CORE A, CCF B).

  16. [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.

  17. [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.

  18. [4 November 2025] We have released the first survey on Reasoning-Aware Recommender Systems in the LLM Era.

  19. [28 October 2025] My ARC Discovery Project 2026 "Advancing Federated Learning for Unified Urban Spatio-Temporal Predictions" has been successfully granted and funded.

  20. [13 October 2025] I was invited to be Area Chair for ACL Rolling Review (ARR).

  21. [1 October 2025] I have been recognised in the Stanford/Elsevier Top 2% Scientists List Career Long (2022-2025) and Single Year (2020-2025).

Availability

Professor Hongzhi Yin is:
Available for supervision

Qualifications

  • Postgraduate Diploma, Peking University
  • Doctor of Philosophy, Peking University

Research interests

  • Structured Foundation Model

  • Spatial-temporal Prediction

  • LLM and ChatBI

  • Recommender System and User Modeling

  • Edge Machine Learning and Applications

  • Time Series and Sequence Mining and Prediction

  • Trustworthy Machine Learning and Applications

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

422 works between 2011 and 2026
All (422) Book (2) Journal Article (168) Edited Outputs (6) Conference Publication (243) Book Chapter (3)

301 - 320 of 422 works

2020

Conference Publication

EPARS: Early prediction of at-risk students with online and offline learning behaviors

Yang, Yu, Wen, Zhiyuan, Cao, Jiannong, Shen, Jiaxing, Yin, Hongzhi and Zhou, Xiaofang (2020). EPARS: Early prediction of at-risk students with online and offline learning behaviors. International Conference on Database Systems for Advanced Applications, Jeju, South Korea, 24-27 September 2020. Heidelberg, Germany: Springer . doi: 10.1007/978-3-030-59416-9_1

EPARS: Early prediction of at-risk students with online and offline learning behaviors

2020

Conference Publication

Adaptive network alignment with unsupervised and multi-order convolutional networks

Trung, Huynh Thanh, Van Vinh, Tong, Tam, Nguyen Thanh, Yin, Hongzhi, Weidlich, Matthias and Viet Hung, Nguyen Quoc (2020). Adaptive network alignment with unsupervised and multi-order convolutional networks. 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, United States, 20-24 April 2020. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/ICDE48307.2020.00015

Adaptive network alignment with unsupervised and multi-order convolutional networks

2020

Conference Publication

Decentralized embedding framework for large-scale networks

Imran, Mubashir, Yin, Hongzhi, Chen, Tong, Shao, Yingxia, Zhang, Xiangliang and Zhou, Xiaofang (2020). Decentralized embedding framework for large-scale networks. International Conference on Database Systems for Advanced Applications, Jeju, South Korea, 24-27 September 2020. Heidelberg, Germany: Springer . doi: 10.1007/978-3-030-59419-0_26

Decentralized embedding framework for large-scale networks

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

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

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

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

BLOMA: explain collaborative filtering via Boosted Local rank-One Matrix Approximation

Gao, Chongming, Yuan, Shuai, Zhang, Zhong, Yin, Hongzhi and Shao, Junming (2019). BLOMA: explain collaborative filtering via Boosted Local rank-One Matrix Approximation. 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22-25 April 2019. Philadelphia, PA, United States: Elsevier. doi: 10.1007/978-3-030-18590-9_72

BLOMA: explain collaborative filtering via Boosted Local rank-One Matrix Approximation

2019

Conference Publication

Streaming Session-based Recommendation

Guo, Lei, Chen, Tong, Yin, Hongzhi, Zhou, Alexander, Wang, Qinyong and Hung, Nguyen Quoc Viet (2019). Streaming Session-based Recommendation. 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: 10.1145/3292500.3330839

Streaming Session-based Recommendation

2019

Conference Publication

Exploiting centrality information with graph convolutions for network representation learning

Chen, Hongxu, Yin, Hongzhi, Chen, Tong, Nguyen, Quoc Viet Hung, Peng, Wen-Chih and Li, Xue (2019). Exploiting centrality information with graph convolutions for network representation learning. 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.2019.00059

Exploiting centrality information with graph convolutions for network representation learning

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?. 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, HI, United States, 27 January - 1 February, 2019. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence.

Find a reasonable ending for stories: Does logic relation help the story cloze test?

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

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 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

  • Doctor Philosophy

    Revolutionise Australian Strata Management with Large Language Models

    Principal Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    Principal Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    LLM-enhanced Recommender System

    Principal Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    Reliable Multimodal Recommender Systems

    Principal Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

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

    Principal Advisor

  • Doctor Philosophy

    Sustainable On-Device Recommender Systems

    Associate Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    Lightweight Graph Neural Networks for Recommendation

    Associate Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    Scalable and Lightweight On-Device Recommender Systems

    Associate Advisor

    Other advisors: Associate Professor Rocky Chen

  • Doctor Philosophy

    Robustness Verification of Neural Network

    Associate Advisor

    Other advisors: Dr Naipeng Dong

  • 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: Associate Professor Rocky Chen

  • Doctor Philosophy

    Scalable and Generalizable Graph Neural Networks

    Associate Advisor

    Other advisors: Associate Professor Rocky Chen

Completed supervision

Enquiries

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