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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, Rising Star of Science Award (2023 and 2022) and 2024 Computer Science in Australia Leader Award, 2024 Computer Science in Australia Leader Award, AI 2000 Most Influential Scholar Honorable Mention in Data Mining (2024, 2023 and 2022). 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 75, including 200+ CCF A and 80+ CCF B, 200+ CORE A* and 80+ 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, 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. [2 July 2024] I have been invited to serve as area chair at KDD 2025.

  2. [27 June 2024] Our ARC Linkage Project "Building an Aussie Information Recommendation System You Can Trust" has been granted and funded.

  3. [16 June 2024] I have been invited to co-chair the User modeling, personalization and recommendation track at The Web Conference 2025.

  4. [6 June 2024] Recently, we have released 2 comprehensive survey papers.

  5. [23 May 2024] Our project Personalized On-Device Large Language Models was shortlisted as a finalist for the 2024 iAwards.

  6. [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).

  7. [17 May 2024] We have 4 full research research papers accepted by the prestigious conference KDD 2024 (CORE A*, CCF A).

  8. [24 April 2024] I have been recognized with 2024 Computer Science in Australia Leader Award in Research.com.

  9. [26 March 2024] We have three research papers accepted by the top conference SIGIR 2024 (CORE A*, CCF A).

  10. [14 March 2024] I have again been recognized as the 2024 AI 2000 Most Influential Scholar Honorable Mention in Data Mining.

  11. [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.

  12. [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.

  13. [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.

  14. [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.

  15. [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).

  16. [24 January 2024] We have five research papers and one tutorial accepted by The Web Conference 2024 (CORE A*, CCF A).

  17. [23 January 2024] We have released three timely surveys:

  18. [19 January 2024] I have been invited to serve as Official Nominator for VinFuture Prize (US$3,000,000). The nomination is open!

  19. [13 January 2024] I have been invited to serve as Area Chair in the Research Track of KDD 2024.

  20. [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).

  21. [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.

Works

Search Professor Hongzhi Yin’s works on UQ eSpace

338 works between 2011 and 2024

81 - 100 of 338 works

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

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

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

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

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

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

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

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

Comprehensive privacy analysis on federated recommender system against attribute inference attacks

Zhang, Shijie, Yuan, Wei and Yin, Hongzhi (2023). Comprehensive privacy analysis on federated recommender system against attribute inference attacks. IEEE Transactions on Knowledge and Data Engineering, 36 (3), 987-999. doi: 10.1109/tkde.2023.3295601

Comprehensive privacy analysis on federated recommender system against attribute inference attacks

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, DASFAA 2023, Tianjin, China, 17-20 April 2023. Cham, Switzerland: Springer Nature Switzerland. 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

Proactive privacy-preserving learning for cross-modal retrieval

Zhang, Peng-Fei, Bai, Guangdong, Yin, Hongzhi and Huang, Zi (2023). Proactive privacy-preserving learning for cross-modal retrieval. ACM Transactions on Information Systems, 41 (2) ARTN 35, 1-23. doi: 10.1145/3545799

Proactive privacy-preserving learning for cross-modal retrieval

2023

Conference Publication

Imbalanced node classification beyond homophilic assumption

Liu, Jie, He, Mengting, Wang, Guangtao, Nguyen, Quoc Viet Hung, Shang, Xuequn and Yin, Hongzhi (2023). Imbalanced node classification beyond homophilic assumption. Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23), Macao, SAR, 19-25 August 2023. Palo Alto, CA, United States: AAAI Press. doi: 10.24963/ijcai.2023/848

Imbalanced node classification beyond homophilic assumption

2023

Edited Outputs

Database Systems for Advanced Applications : 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part III

Wang, Xin, Sapino, Maria Luisa, Han, Wook-Shin, El Abbadi, Amr, Dobbie, Gill, Feng, Zhiyong, Shao, Yingxiao and Yin, Hongzhi eds. (2023). Database Systems for Advanced Applications : 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part III. 28th International Conference, DASFAA 2023, Tianjin, China, 17–20 April 2023. Cham, Switzerland: Springer.

Database Systems for Advanced Applications : 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part III

2023

Journal Article

Cost-effective synchrophasor data source authentication based on multiscale adaptive coupling correlation detrended analysis

Bai, Feifei, Cui, Yi, Yan, Ruifeng, Yin, Hongzhi, Chen, Tong, Dart, David and Yaghoobi, Jalil (2023). Cost-effective synchrophasor data source authentication based on multiscale adaptive coupling correlation detrended analysis. International Journal of Electrical Power and Energy Systems, 144 108606, 108606. doi: 10.1016/j.ijepes.2022.108606

Cost-effective synchrophasor data source authentication based on multiscale adaptive coupling correlation detrended analysis

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

2023

Journal Article

TinyAD: memory-efficient anomaly detection for time series data in industrial IoT

Sun, Yuting, Chen, Tong, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2023). TinyAD: memory-efficient anomaly detection for time series data in industrial IoT. IEEE Transactions on Industrial Informatics, 20 (1), 824-834. doi: 10.1109/tii.2023.3254668

TinyAD: memory-efficient anomaly detection for time series data in industrial IoT

Funding

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

Supervision

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

    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

    Meeting Challenges on Secure Recommender Systems

    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

    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

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

    Associate Advisor

    Other advisors: Dr Haoran Duan, Professor Liu Ye

  • 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

    Sustainable On-Device Recommender Systems

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Scalable and Lightweight On-Device Recommender Systems

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Scalable and Lightweight On-Device Recommender Systems

    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

    Causal Analysis for Decision Support in Public Health

    Associate Advisor

    Other advisors: Professor Shazia Sadiq, 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