Skip to menu Skip to content Skip to footer
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, 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.

Works

Search Professor Hongzhi Yin’s works on UQ eSpace

361 works between 2011 and 2025

101 - 120 of 361 works

2023

Journal Article

Interpretable signed link prediction with signed infomax hyperbolic graph

Luo, Yadan, Huang, Zi, Chen, Hongxu, Yang, Yang, Yin, Hongzhi and Baktashmotlagh, Mahsa (2023). Interpretable signed link prediction with signed infomax hyperbolic graph. IEEE Transactions on Knowledge and Data Engineering, 35 (4), 3991-4002. doi: 10.1109/TKDE.2021.3139035

Interpretable signed link prediction with signed infomax hyperbolic graph

2023

Conference Publication

Disconnected emerging knowledge graph oriented inductive link prediction

Zhang, Yufeng, Wang, Weiqing, Yin, Hongzhi, Zhao, Pengpeng, Chen, Wei and Zhao, Lei (2023). Disconnected emerging knowledge graph oriented inductive link prediction. 2023 IEEE 39th International Conference on Data Engineering (ICDE), Anaheim, CA, United States, 3-7 April 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/icde55515.2023.00036

Disconnected emerging knowledge graph oriented inductive link prediction

2023

Journal Article

AutoML for deep recommender systems: a survey

Zheng, Ruiqi, Qu, Liang, Cui, Bin, Shi, Yuhui and Yin, Hongzhi (2023). AutoML for deep recommender systems: a survey. ACM Transactions on Information Systems, 41 (4) 101, 1-38. doi: 10.1145/3579355

AutoML for deep recommender systems: a survey

2023

Journal Article

Local feature-based mutual complexity for pixel-value-ordering reversible data hiding

Gao, Xinyi, Pan, Zhibin, Fan, Guojun, Zhang, Xiaoran and Yin, Hongzhi (2023). Local feature-based mutual complexity for pixel-value-ordering reversible data hiding. Signal Processing, 204 108833, 1-15. doi: 10.1016/j.sigpro.2022.108833

Local feature-based mutual complexity for pixel-value-ordering reversible data hiding

2023

Conference Publication

Learning to distill graph neural networks

Yang, Cheng, Guo, Yuxin, Xu, Yao, Shi, Chuan, Liu, Jiawei, Wang, Chunchen, Li, Xin, Guo, Ning and Yin, Hongzhi (2023). Learning to distill graph neural networks. Sixteenth ACM International Conference on Web Search and Data Mining, Singapore, Singapore, 27 February - 3 March 2023. New York, NY, United States: ACM. doi: 10.1145/3539597.3570480

Learning to distill graph neural networks

2023

Conference Publication

Federated unlearning for on-device recommendation

Yuan, Wei, Yin, Hongzhi, Wu, Fangzhao, Zhang, Shijie, He, Tieke and Wang, Hao (2023). Federated unlearning for on-device recommendation. Sixteenth ACM International Conference on Web Search and Data Mining, Singapore, Singapore, 27 February - 3 March 2023. New York, NY, United States: ACM. doi: 10.1145/3539597.3570463

Federated unlearning for on-device recommendation

2023

Conference Publication

Knowledge enhancement for contrastive multi-behavior recommendation

Xuan, Hongrui, Liu, Yi, Li, Bohan and Yin, Hongzhi (2023). Knowledge enhancement for contrastive multi-behavior recommendation. Sixteenth ACM International Conference on Web Search and Data Mining, Singapore, Singapore, 27 February - 3 March 2023. New York, NY, United States: ACM. doi: 10.1145/3539597.3570386

Knowledge enhancement for contrastive multi-behavior recommendation

2023

Conference Publication

Simplifying graph-based collaborative filtering for recommendation

He, Li, Wang, Xianzhi, Wang, Dingxian, Zou, Haoyuan, Yin, Hongzhi and Xu, Guandong (2023). Simplifying graph-based collaborative filtering for recommendation. Sixteenth ACM International Conference on Web Search and Data Mining, Singapore, Singapore, 27 February - 3 March 2023. New York, NY, United States: ACM. doi: 10.1145/3539597.3570451

Simplifying graph-based collaborative filtering for recommendation

2023

Journal Article

Time-Aware Dynamic Graph Embedding for Asynchronous Structural Evolution

Yang, Yu, Yin, Hongzhi, Cao, Jiannong, Chen, Tong, Nguyen, Quoc Viet Hung, Zhou, Xiaofang and Chen, Lei (2023). Time-Aware Dynamic Graph Embedding for Asynchronous Structural Evolution. IEEE Transactions on Knowledge and Data Engineering, 35 (9), 1-14. doi: 10.1109/tkde.2023.3246059

Time-Aware Dynamic Graph Embedding for Asynchronous Structural Evolution

2023

Journal Article

Decentralized collaborative learning framework for next POI recommendation

Long, Jing, Chen, Tong, Hung, Nguyen Quoc Viet and Yin, Hongzhi (2023). Decentralized collaborative learning framework for next POI recommendation. ACM Transactions on Information Systems, 41 (3) 66, 66:1-66:25. doi: 10.1145/3555374

Decentralized collaborative learning framework for next POI recommendation

2023

Journal Article

ReFRS: Resource-efficient Federated Recommender System for dynamic and diversified user preferences

Imran, Mubashir, Yin, Hongzhi, Chen, Tong, Hung, Nguyen Quoc Viet, Zhou, Alexander and Zheng, Kai (2023). ReFRS: Resource-efficient Federated Recommender System for dynamic and diversified user preferences. ACM Transactions on Information Systems, 41 (3) 65, 65:1-65:30 . doi: 10.1145/3560486

ReFRS: Resource-efficient Federated Recommender System for dynamic and diversified user preferences

2023

Conference Publication

Beyond double ascent via recurrent neural tangent kernel in sequential recommendation

Qiu, Ruihong, Huang, Zi and Yin, Hongzhi (2023). Beyond double ascent via recurrent neural tangent kernel in sequential recommendation. 22nd IEEE International Conference on Data Mining (ICDM), Orlando, FL USA, 28 November-1 December 2022. New York, NY USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/icdm54844.2022.00053

Beyond double ascent via recurrent neural tangent kernel in sequential recommendation

2023

Journal Article

Deep MinCut: learning node embeddings by detecting communities

Duong, Chi Thang, Nguyen, Thanh Tam, Hoang, Trung-Dung, Yin, Hongzhi, Weidlich, Matthias and Nguyen, Quoc Viet Hung (2023). Deep MinCut: learning node embeddings by detecting communities. Pattern Recognition, 134 109126, 1-11. doi: 10.1016/j.patcog.2022.109126

Deep MinCut: learning node embeddings by detecting communities

2023

Journal Article

Network alignment with holistic embeddings

Huynh, Thanh Trung, Duong, Chi Thang, Nguyen, Thanh Tam, Van, Vinh Tong, Sattar, Abdul, Yin, Hongzhi and Nguyen, Quoc Viet Hung (2023). Network alignment with holistic embeddings. IEEE Transactions on Knowledge and Data Engineering, 35 (2), 1881-1894. doi: 10.1109/TKDE.2021.3101840

Network alignment with holistic embeddings

2023

Journal Article

Uniting heterogeneity, inductiveness, and efficiency for graph representation learning

Chen, Tong, Yin, Hongzhi, Ren, Jie, Huang, Zi, Zhang, Xiangliang and Wang, Hao (2023). Uniting heterogeneity, inductiveness, and efficiency for graph representation learning. IEEE Transactions on Knowledge and Data Engineering, 35 (2), 2103-2117. doi: 10.1109/TKDE.2021.3100529

Uniting heterogeneity, inductiveness, and efficiency for graph representation learning

2023

Journal Article

A multi-strategy based pre-training method for cold-start recommendation

Hao, Bowen, Yin, Hongzhi, Zhang, Jing, Li, Cuiping and Chen, Hong (2023). A multi-strategy based pre-training method for cold-start recommendation. ACM Transactions on Information Systems, 41 (2) 31, 1-24. doi: 10.1145/3544107

A multi-strategy based pre-training method for cold-start recommendation

2023

Journal Article

Special Issue of DASFAA 2023

Wang, Xin, Sapino, Maria Luisa, Han, Wook-Shin, Shao, Yingxiao and Yin, Hongzhi (2023). Special Issue of DASFAA 2023. Data Science and Engineering, 8 (3), 1-2. doi: 10.1007/s41019-023-00231-w

Special Issue of DASFAA 2023

2023

Book

Database Systems for Advanced Applications. DASFAA 2023 International Workshops : BDMS 2023, BDQM 2023, GDMA 2023, BundleRS 2023, Tianjin, China, April 17-20, 2023, Proceedings

Amr El Abbadi, Gillian Dobbie, Zhiyong Feng, Lu Chen, Xiaohui Tao, Yingxia Shao and Hongzhi Yin eds. (2023). Database Systems for Advanced Applications. DASFAA 2023 International Workshops : BDMS 2023, BDQM 2023, GDMA 2023, BundleRS 2023, Tianjin, China, April 17-20, 2023, Proceedings. Lecture Notes in Computer Science, Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-35415-1

Database Systems for Advanced Applications. DASFAA 2023 International Workshops : BDMS 2023, BDQM 2023, GDMA 2023, BundleRS 2023, Tianjin, China, April 17-20, 2023, Proceedings

2023

Edited Outputs

Database systems for advanced applications:  28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part III

Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao and Hongzhi Yin eds. (2023). Database systems for advanced applications:  28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part III. 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), Tianjin, China, 17-20 April 2023. Heidelberg, Germany: Springer . doi: 10.1007/978-3-031-30675-4

Database systems for advanced applications:  28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part III

2023

Journal Article

Multi-hop knowledge graph reasoning in few-shot scenarios

Zheng, Shangfei, Chen, Wei, Wang, Weiqing, Zhao, Pengpeng, Yin, Hongzhi and Zhao, Lei (2023). Multi-hop knowledge graph reasoning in few-shot scenarios. IEEE Transactions on Knowledge and Data Engineering, 36 (4), 1713-1727. doi: 10.1109/tkde.2023.3304665

Multi-hop knowledge graph reasoning in few-shot scenarios

Funding

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

Supervision

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

    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

    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

  • 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

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