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)

221 - 240 of 361 works

2020

Journal Article

TEAGS: time-aware text embedding approach to generate subgraphs

Hosseini, Saeid, Najafipour, Saeed, Cheung, Ngai-Man, Yin, Hongzhi, Kangavari, Mohammad Reza and Zhou, Xiaofang (2020). TEAGS: time-aware text embedding approach to generate subgraphs. Data Mining and Knowledge Discovery, 34 (4), 1136-1174. doi: 10.1007/s10618-020-00688-7

TEAGS: time-aware text embedding approach to generate subgraphs

2020

Journal Article

User account linkage across multiple platforms with location data

Chen, Wei, Wang, Weiqing, Yin, Hongzhi, Fang, Jun-Hua and Zhao, Lei (2020). User account linkage across multiple platforms with location data. Journal of Computer Science and Technology, 35 (4), 751-768. doi: 10.1007/s11390-020-0250-7

User account linkage across multiple platforms with location data

2020

Conference Publication

Discovering subsequence patterns for next POI recommendation

Zhao, Kangzhi, Zhang, Yong, Yin, Hongzhi, Wang, Jin, Zheng, Kai, Zhou, Xiaofang and Xing, Chunxiao (2020). Discovering subsequence patterns for next POI recommendation. Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), Yokohama, Japan, 11-17 July, 2020. California, United States: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2020/445

Discovering subsequence patterns for next POI recommendation

2020

Journal Article

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

Zhou, Yiming, Han, Yuehui, Liu, An, Li, Zhixu, Yin, Hongzhi, Chen, Wei and Zhao, Lei (2020). Extracting representative user subset of social networks towards user characteristics and topological features. World Wide Web, 23 (5), 2903-2931. doi: 10.1007/s11280-020-00828-5

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

2020

Journal Article

Exploiting cross-session information for session-based recommendation with graph neural networks

Qiu, Ruihong, Huang, Zi, Li, Jingjing and Yin, Hongzhi (2020). Exploiting cross-session information for session-based recommendation with graph neural networks. ACM Transactions on Information Systems, 38 (3) 22, 1-23. doi: 10.1145/3382764

Exploiting cross-session information for session-based recommendation with graph neural networks

2020

Journal Article

Social boosted recommendation with folded bipartite network embedding

Chen, Hongxu, Yin, Hongzhi, Chen, Tong, Wang, Weiqing, Li, Xue and Hu, Xia (2020). Social boosted recommendation with folded bipartite network embedding. IEEE Transactions on Knowledge and Data Engineering, 34 (2), 914-926. doi: 10.1109/tkde.2020.2982878

Social boosted recommendation with folded bipartite network embedding

2020

Journal Article

Group-based recurrent neural networks for POI recommendation

Li, Guohui, Chen, Qi, Zheng, Bolong, Yin, Hongzhi, Nguyen, Quoc Viet Hung and Zhou, Xiaofang (2020). Group-based recurrent neural networks for POI recommendation. ACM/IMS Transactions on Data Science, 1 (1) 3, 1-18. doi: 10.1145/3343037

Group-based recurrent neural networks for POI recommendation

2020

Journal Article

Cluster query: a new query pattern on temporal knowledge graph

Huang, Jinjing, Chen, Wei, Liu, An, Wang, Weiqing, Yin, Hongzhi and Zhao, Lei (2020). Cluster query: a new query pattern on temporal knowledge graph. World Wide Web, 23 (2), 755-779. doi: 10.1007/s11280-019-00754-1

Cluster query: a new query pattern on temporal knowledge graph

2020

Journal Article

Local variational feature-based similarity models for recommending top-N new items

Chen, Yifan, Wang, Yang, Zhao, Xiang, Yin, Hongzhi, Markov, Ilya and De Rijke, Maarten (2020). Local variational feature-based similarity models for recommending top-N new items. ACM Transactions on Information Systems, 38 (2) 12, 1-33. doi: 10.1145/3372154

Local variational feature-based similarity models for recommending top-N new items

2020

Journal Article

SGPM: a privacy protected approach of time-constrained graph pattern matching in cloud

Huang, Jinjing, Chen, Wei, Li, Zhixu, Zhao, Pengpeng, Wang, Weiqing, Yin, Hongzhi and Zhao, Lei (2020). SGPM: a privacy protected approach of time-constrained graph pattern matching in cloud. World Wide Web-Internet and Web Information Systems, 23 (1), 519-547. doi: 10.1007/s11280-020-00784-0

SGPM: a privacy protected approach of time-constrained graph pattern matching in cloud

2020

Journal Article

Few-shot deep adversarial learning for video-based person re-identification

Wu, Lin, Wang, Yang, Yin, Hongzhi, Wang, Meng and Shao, Ling (2020). Few-shot deep adversarial learning for video-based person re-identification. IEEE Transactions on Image Processing, 29 8839731, 1233-1245. doi: 10.1109/tip.2019.2940684

Few-shot deep adversarial learning for video-based person re-identification

2020

Conference Publication

GCN-based user representation learning for unifying robust recommendation and fraudster detection

Zhang, Shijie, Yin, Hongzhi, Chen, Tong, Hung, Quoc Viet Nguyen, Huang, Zi and Cui, Lizhen (2020). GCN-based user representation learning for unifying robust recommendation and fraudster detection. SIGIR '20: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Online, July 2020. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3397271.3401165

GCN-based user representation learning for unifying robust recommendation and fraudster detection

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

2020

Conference Publication

Graph embeddings for one-pass processing of heterogeneous queries

Duong, Chi Thang, Yin, Hongzhi, Hoang, Dung, Nguyen, Minn Hung, Weidlich, Matthias, Hung Nguyen, Quoc Viet and Aberer, Karl (2020). Graph embeddings for one-pass processing of heterogeneous queries. 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/ICDE48307.2020.00222

Graph embeddings for one-pass processing of heterogeneous queries

2020

Conference Publication

Sequence-aware factorization machines for temporal predictive analytics

Chen, Tong, Yin, Hongzhi, Nguyen, Quoc Viet Hung, Peng, Wen-Chih, Li, Xue and Zhou, Xiaofang (2020). Sequence-aware factorization machines for temporal predictive analytics. 2020 IEEE 36th International Conference on Data Engineering, Dallas, Texas, United States, 20-24 April 2020. LOS ALAMITOS: IEEE Computer Society. doi: 10.1109/ICDE48307.2020.00125

Sequence-aware factorization machines for temporal predictive analytics

2020

Journal Article

Preference-aware task assignment in spatial crowdsourcing: from individuals to groups

Zhao, Yan, Zheng, Kai, Yin, Hongzhi, Liu, Guanfeng, Fang, Junhua and Zhou, Xiaofang (2020). Preference-aware task assignment in spatial crowdsourcing: from individuals to groups. IEEE Transactions on Knowledge and Data Engineering, 34 (7), 1-1. doi: 10.1109/tkde.2020.3021028

Preference-aware task assignment in spatial crowdsourcing: from individuals to groups

2020

Conference Publication

Group recommendation with latent voting mechanism

Guo, Lei, Yin, Hongzhi, Wang, Qinyong, Cui, Bin, Huang, Zi and Cui, Lizhen (2020). Group recommendation with latent voting mechanism. 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/ICDE48307.2020.00018

Group recommendation with latent voting mechanism

2020

Conference Publication

Where to go next: modeling long- and short-term user preferences for point-of-interest recommendation

Sun, Ke, Qian, Tieyun, Chen, Tong, Liang, Yile, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2020). Where to go next: modeling long- and short-term user preferences for point-of-interest recommendation. AAAI Conference on Artificial Intelligence, New York, NY, United States, 7-12 February 2020. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v34i01.5353

Where to go next: modeling long- and short-term user preferences for point-of-interest 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

    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

Enquiries

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communications@uq.edu.au