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, the Australian Research Council Future Fellowship 2021, the Discovery Early Career Researcher Award 2016, the UQ Foundation Research Excellence Award 2019, the 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 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 300 papers with an H-index of 77, including 210+ CCF A/CORE A* and 80+ CCF B/CORE A, such as KDD, SIGIR, WWW, 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 200+. 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, Chinese Academy of Sciences ranking Q1, and CCF B), Science China Information Sciences (JCR Q1, Chinese Academy of Sciences ranking Q1, and CCF A), Data Science and Engineering (DSE, JCR Q1, Chinese Academy of Sciences ranking Q2), Journal of Computer Science and Technology (JCST, CCF B), Journal of Social Computing, ACM Transactions on Information Systems 2022-2023 (TOIS, CCF A), ACM Transactions on Intelligent Systems and Technology 2020-2021 (TIST, 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, and ACM Computing Reviews.

Dr. Hongzhi Yin is looking for highly motivated and high-quality 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 47 in the QS World University Rankings, 52 in the US News Best Global Universities Rankings, 60 in the Times Higher Education World University Rankings, and 55 in the Academic Ranking of World Universities.

Latest News

  1. [23 September 2024] We have three journal papers recognized as ESI Hot and Highly Cited papers.

  2. [10 September 2024] I have been recognized with the 2024 Rising Star of Science Award in Research.com and ranked #8 in Australia among Rising Stars for 2024.

  3. [24 August 2024] Two of my PhD graduates have been awarded the competitive ARC DECRA Fellowship. Congratulations to Weiqing and Junliang.

  4. [23 July 2024] Recently, we have released 3 comprehensive survey papers.

  5. [2 July 2024] I have been invited to serve as area chair at KDD 2025.

  6. [27 June 2024] Our ARC Linkage Project "Building an Aussie Information Recommendation System You Can Trust" has been granted and funded.

  7. [16 June 2024] I have been invited to co-chair the User modeling, personalization and recommendation track at The Web Conference 2025.

  8. [6 June 2024] Recently, we have released 2 comprehensive survey papers.

  9. [23 May 2024] Our project Personalized On-Device Large Language Models was shortlisted as a finalist for the 2024 iAwards.

  10. [22 May 2024] Our research paper "Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion" has been accepted by the top journal TOIS 2024 (CORE A and CCF A).

  11. [17 May 2024] We have 4 full research research papers accepted by the prestigious conference KDD 2024 (CORE A*, CCF A).

  12. [24 April 2024] I have been recognized with 2024 Computer Science in Australia Leader Award in Research.com.

  13. [26 March 2024] We have three research papers accepted by the top conference SIGIR 2024 (CORE A*, CCF A).

  14. [14 March 2024] I have again been recognized as the 2024 AI 2000 Most Influential Scholar Honorable Mention in Data Mining.

  15. [10 March 2024] We have 8 research papers accepted by the prestigious conference ICDE 2024 (CORE A*, CCF A), including 4 accepted in the first round and 4 in the second round.

  16. [13 February 2024] Congratulations to Dr. Junliang Yu, my Ph.D. graduate, on winning the UQ Graduate School 2023 Dean's Award for Outstanding Higher Degree by Research Theses.

  17. [11 February 2024] We have 2 research papers directly accepted in the second round of the prestigious conference ICDE 2024 (CORE A*, CCF A). It's noteworthy that out of over 1000 submissions, only 19 were directly accepted.

  18. [2 February 2024] We are organizing a special issue, "Cloud-Edge Collaboration for On-Device Recommendation", in the top journal - Science China Information Sciences (CCF Ranking A, CIC Ranking A, CAA Ranking A ), and call for paper is online.

  19. [31 January 2024] Our research paper "Personalized Elastic Embedding Learning for On-Device Recommendation" has been accepted by the top journal TKDE 2024 (CORE A* and CCF A).

  20. [24 January 2024] We have five research papers and one tutorial accepted by The Web Conference 2024 (CORE A*, CCF A).

  21. [23 January 2024] We have released three timely surveys:

  22. [19 January 2024] I have been invited to serve as Official Nominator for VinFuture Prize (US$3,000,000). The nomination is open!

  23. [13 January 2024] I have been invited to serve as Area Chair in the Research Track of KDD 2024.

  24. [1 January 2024] I began to serve as Action/Associate Editor for Neural Networks (JCR Q1, Chinese Academy of Sciences ranking Q1, and CCF B), Data Science and Engineering (DSE, JCR Q1, Chinese Academy of Sciences ranking Q2).

  25. [1 January 2024] I have been promoted to Professor (Level E) at The University of Queensland.

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

338 works between 2011 and 2024
All (338) Journal Article (128) Book Chapter (2) Conference Publication (200) Book (2) Edited Outputs (6)

261 - 280 of 338 works

2018

Conference Publication

From anomaly detection to rumour detection using data streams of social platforms

Tam, Nguyen Thanh, Weidlich, Matthias, Zheng, Bolong, Yin, Hongzhi, Hung, Nguyen Quoc Viet and Stantic, Bela (2018). From anomaly detection to rumour detection using data streams of social platforms. 45th International Conference on Very Large Data Bases (VLDB 2019), Los Angeles, CA, United States, 26-30 August 2017. New York, NY, United States: Association for Computing Machinery (ACM). doi: 10.14778/3329772.3329778

From anomaly detection to rumour detection using data streams of social platforms

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

2018

Conference Publication

Streaming ranking based recommender systems

Wang, Weiqing, Yin, Hongzhi, Huang, Zi, Wang, Qinyong, Du, Xingzhong and Nguyen, Quoc Viet Hung (2018). Streaming ranking based recommender systems. 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, United States, 8-12 July 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3209978.3210016

Streaming ranking based recommender systems

2018

Conference Publication

A privacy-preserving framework for subgraph pattern matching in cloud

Gao, Jiuru, Xu, Jiajie, Liu, Guanfeng, Chen, Wei, Yin, Hongzhi and Zhao, Lei (2018). A privacy-preserving framework for subgraph pattern matching in cloud. 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_20

A privacy-preserving framework for subgraph pattern matching in cloud

2018

Conference Publication

Eliminating temporal conflicts in uncertain temporal knowledge graphs

Lu, Lingjiao, Fang, Junhua, Zhao, Pengpeng, Xu, Jiajie, Yin, Hongzhi and Zhao, Lei (2018). Eliminating temporal conflicts in uncertain temporal knowledge graphs. 19th International Conference on Web Information Systems Engineering, WISE 2018, Dubai, United Arab Emirates, November 12 - 15, 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-02922-7_23

Eliminating temporal conflicts in uncertain temporal knowledge graphs

2018

Conference Publication

Modeling patient visit using electronic medical records for cost profile estimation

Zhao, Kangzhi, Zhang, Yong, Wang, Zihao, Yin, Hongzhi, Zhou, Xiaofang, Wang, Jin and Xing, Chunxiao (2018). Modeling patient visit using electronic medical records for cost profile estimation. 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, Gold Coast, QLD, Australia, 21-24 May 2018. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-91458-9_2

Modeling patient visit using electronic medical records for cost profile estimation

2018

Conference Publication

Joint event-partner recommendation in event-based social networks

Yin, Hongzhi, Zou, Lei, Nguyen, Quoc Viet Hung, Huang, Zi and Zhou, Xiaofang (2018). Joint event-partner recommendation in event-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.2018.00088

Joint event-partner recommendation in event-based social networks

2018

Conference Publication

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

Zhou, Yiming, Han, Yuehui, Liu, An, Li, Zhixu, Yin, Hongzhi and Zhao, Lei (2018). Extracting representative user subset of social networks towards user characteristics and topological features. 19th International Conference on Web Information Systems Engineering, WISE 2018, Dubai, United Arab Emirates, November 12 - 15, 2018. Cham, Switzerland: Springer . doi: 10.1007/978-3-030-02922-7_15

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

2018

Conference Publication

PME: projected metric embedding on heterogeneous networks for link prediction

Chen, Hongxu, Wang, Hao, Yin, Hongzhi, Nguyen, Quoc Viet Hung, Wang, Weiqing and Li, Xue (2018). PME: projected metric embedding on heterogeneous networks for link prediction. 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: 10.1145/3219819.3219986

PME: projected metric embedding on heterogeneous networks for link prediction

2018

Conference Publication

Discrete ranking-based matrix factorization with self-paced learning

Zhang, Yan, Wang, Haoyu, Lian, Defu, Tsang, Ivor W., Yin, Hongzhi and Yang, Guowu (2018). Discrete ranking-based matrix factorization with self-paced learning. 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), London, United Kingdom, 19 - 23 August 2018. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3219819.3220116

Discrete ranking-based matrix factorization with self-paced learning

2018

Journal Article

Computing crowd consensus with partial agreement

Viet Hung, Nguyen Quoc, Viet, Huynh Huu, Tam, Nguyen Thanh, Weidlich, Matthias, Yin, Hongzhi and Zhou, Xiaofang (2018). Computing crowd consensus with partial agreement. IEEE Transactions on Knowledge and Data Engineering, 30 (1), 1-14. doi: 10.1109/TKDE.2017.2750683

Computing crowd consensus with partial agreement

2018

Conference Publication

Neural memory streaming recommender networks with adversarial training

Wang, Qinyong, Lian, Defu, Yin, Hongzhi, Wang, Hao, Hu, Zhiting and Huang, Zi (2018). Neural memory streaming recommender networks with adversarial training. 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: 10.1145/3219819.3220004

Neural memory streaming recommender networks with adversarial training

2018

Conference Publication

LC-RNN: A deep learning model for traffic speed prediction

Lv, Zhongjian, Xu, Jiajie, Zheng, Kai, Yin, Hongzhi, Zhao, Pengpeng and Zhou, Xiaofang (2018). LC-RNN: A deep learning model for traffic speed prediction. 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden, 13-19 July 2018. FREIBURG: International Joint Conferences on Artificial Intelligence. doi: 10.24963/ijcai.2018/482

LC-RNN: A deep learning model for traffic speed prediction

2018

Conference Publication

User guidance for efficient fact checking

Nguyen, Thanh Tam, Weidlich, Matthias, Yin, Hongzhi, Zheng, Bolong, Hung Nguyen, Quoc Viet and Stantic, Bela (2018). User guidance for efficient fact checking. 45th International Conference on Very Large Data Bases, Los Angeles, CA United States, 2019. New York, NY United States: Association for Computing Machinery. doi: 10.14778/3324301.3324303

User guidance for efficient fact checking

2018

Conference Publication

Mining subgraphs from propagation networks through temporal dynamic analysis

Hosseini, Saeid, Yin, Hongzhi, Zhang, Meihui, Elovici, Yuval and Zhou, Xiaofang (2018). Mining subgraphs from propagation networks through temporal dynamic analysis. 19th IEEE International Conference on Mobile Data Management, MDM 2018, Aalborg University, Aalborg, Denmark, 26-28 June 2018. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/MDM.2018.00023

Mining subgraphs from propagation networks through temporal dynamic analysis

2018

Conference Publication

Adaptive implicit friends identification over heterogeneous network for social recommendation

Yu, Junliang, Gao, Min, Li, Jundong, Yin, Hongzhi and Liu, Huan (2018). Adaptive implicit friends identification over heterogeneous network for social recommendation. 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, 22-26 October 2018. New York, NY, United States: Association for Computing Machinery (ACM). doi: 10.1145/3269206.3271725

Adaptive implicit friends identification over heterogeneous network for social recommendation

Current funding

  • 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

  • Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    This project tackles the challenging problem of personalised predictive analytics with resource-constrained personal devices and massive-scale data. The knowledge to be generated concerns privacy, fairness, and resource efficiency in the era of Internet of Things. The expected outcomes include a collaborative learning paradigm for building personalised models on personal smart devices in open and fully decentralised settings. Privacy and model fairness are core tenets of the paradigm. Personalised predictive analytics is frontier research that will position Australia at the forefront of AI and give business the tools needed to deploy innovative business systems for market exploitation with a secure, equitable and competitive advantage.

    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

    Meeting Challenges on Secure Recommender Systems

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Image Generation from Texts

    Principal Advisor

    Other advisors: Dr Thomas Taimre, Dr Slava Vaisman

  • Doctor Philosophy

    Knowledge Graph-based Conversational Recommender Systems

    Principal Advisor

    Other advisors: Dr Miao Xu

  • Doctor Philosophy

    Deep Learning for Graph Data Analysis

    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

    Decentralised Collaborative Predictive Analytics on Personal Smart Devices

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Federated Graph Neural Network-based Recommender Systems

    Principal Advisor

    Other advisors: Dr Miao Xu

  • 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

    Scalable and Lightweight On-Device Recommender Systems

    Associate Advisor

    Other advisors: 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

  • Doctor Philosophy

    Understanding nitrous oxide emissions from wastewater treatment processes with stable isotopes and mathematical modelling

    Associate Advisor

    Other advisors: Dr Haoran Duan, Professor Liu Ye

Completed supervision

Enquiries

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