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 (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.

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)

101 - 120 of 338 works

2022

Journal Article

Self-supervised graph learning for occasional group recommendation

Hao, Bowen, Yin, Hongzhi, Li, Cuiping and Chen, Hong (2022). Self-supervised graph learning for occasional group recommendation. International Journal of Intelligent Systems, 37 (12), 10880-10902. doi: 10.1002/int.23011

Self-supervised graph learning for occasional group recommendation

2022

Conference Publication

Locality aware temporal FMs for crime prediction

Mansha, Sameen, Rehman, Abdur, Abdullah, Shaaf, Kamiran, Faisal and Yin, Hongzhi (2022). Locality aware temporal FMs for crime prediction. 31st ACM International Conference on Information and Knowledge Management, Atlanta, GA, United States, 17-21 October 2022. New York, NY, United States: ACM. doi: 10.1145/3511808.3557657

Locality aware temporal FMs for crime prediction

2022

Journal Article

Model-agnostic and diverse explanations for streaming rumour graphs

Nguyen, Thanh Tam, Phan, Thanh Cong, Nguyen, Minh Hieu, Weidlich, Matthias, Yin, Hongzhi, Jo, Jun and Nguyen, Quoc Viet Hung (2022). Model-agnostic and diverse explanations for streaming rumour graphs. Knowledge-Based Systems, 253 109438, 1-15. doi: 10.1016/j.knosys.2022.109438

Model-agnostic and diverse explanations for streaming rumour graphs

2022

Journal Article

Multiscale adaptive multifractal detrended fluctuation analysis-based source identification of synchrophasor data

Cui, Yi, Bai, Feifei, Yin, Hongzhi, Chen, Tong, Dart, David, Zillmann, Matthew and Ko, Ryan K. L. (2022). Multiscale adaptive multifractal detrended fluctuation analysis-based source identification of synchrophasor data. IEEE Transactions on Smart Grid, 13 (6), 1-4. doi: 10.1109/tsg.2022.3207066

Multiscale adaptive multifractal detrended fluctuation analysis-based source identification of synchrophasor data

2022

Conference Publication

CIRCLE: continual repair across programming languages

Yuan, Wei, Zhang, Quanjun, He, Tieke, Fang, Chunrong, Hung, Nguyen Quoc Viet, Hao, Xiaodong and Yin, Hongzhi (2022). CIRCLE: continual repair across programming languages. ISSTA '22: 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, Online, 18 - 22 July 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3533767.3534219

CIRCLE: continual repair across programming languages

2022

Conference Publication

Single-shot Embedding Dimension Search in Recommender System

Qu, Liang, Ye, Yonghong, Tang, Ningzhi, Zhang, Lixin, Shi, Yuhui and Yin, Hongzhi (2022). Single-shot Embedding Dimension Search in Recommender System. SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11 - 15 July 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3477495.3532060

Single-shot Embedding Dimension Search in Recommender System

2022

Conference Publication

On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation

Xia, Xin, Yin, Hongzhi, Yu, Junliang, Wang, Qinyong, Xu, Guandong and Nguyen, Quoc Viet Hung (2022). On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation. SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11 - 15 July 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3477495.3531775

On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation

2022

Conference Publication

Thinking inside The Box : Learning Hypercube Representations for Group Recommendation

Chen, Tong, Yin, Hongzhi, Long, Jing, Nguyen, Quoc Viet Hung, Wang, Yang and Wang, Meng (2022). Thinking inside The Box : Learning Hypercube Representations for Group Recommendation. SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11 - 15 July 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3477495.3532066

Thinking inside The Box : Learning Hypercube Representations for Group Recommendation

2022

Conference Publication

Are Graph Augmentations Necessary? : Simple Graph Contrastive Learning for Recommendation

Yu, Junliang, Yin, Hongzhi, Xia, Xin, Chen, Tong, Cui, Lizhen and Nguyen, Quoc Viet Hung (2022). Are Graph Augmentations Necessary? : Simple Graph Contrastive Learning for Recommendation. SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11 - 15 July 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3477495.3531937

Are Graph Augmentations Necessary? : Simple Graph Contrastive Learning for Recommendation

2022

Journal Article

Detecting rumours with latency guarantees using massive streaming data

Nguyen, Thanh Tam, Huynh, Thanh Trung, Yin, Hongzhi, Weidlich, Matthias, Nguyen, Thanh Thi, Mai, Thai Son and Nguyen, Quoc Viet Hung (2022). Detecting rumours with latency guarantees using massive streaming data. VLDB Journal, 32 (2), 1-19. doi: 10.1007/s00778-022-00750-4

Detecting rumours with latency guarantees using massive streaming data

2022

Conference Publication

exRumourLens: Auditable Rumour Detection with Multi-View Explanations

Phan, Thanh Cong, Nguyen, Thanh Tam, Weidlich, Matthias, Yin, Hongzhi, Jo, Jun and Nguyen, Quoc Viet Hung (2022). exRumourLens: Auditable Rumour Detection with Multi-View Explanations. 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, 9-12 May 2022. Piscataway, NJ United States: IEEE. doi: 10.1109/icde53745.2022.00291

exRumourLens: Auditable Rumour Detection with Multi-View Explanations

2022

Conference Publication

Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations

Zhang, Yan, Li, Changyu, Tsang, Ivor W., Xu, Hui, Duan, Lixin, Yin, Hongzhi, Li, Wen and Shao, Jie (2022). Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations. 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, 9-12 May 2022. Piscataway, NJ United States: IEEE. doi: 10.1109/icde53745.2022.00265

Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations

2022

Conference Publication

Network Alignment with Holistic Embeddings (Extended Abstract)

Huynh, Thanh Trung, Duong, Thang Chi, Nguyen, Thanh Tam, Tong, Van Vinh, Sattar, Abdul, Yin, Hongzhi and Nguyen, Quoc Viet Hung (2022). Network Alignment with Holistic Embeddings (Extended Abstract). 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, 9-12 May 2022. Piscataway, NJ United States: IEEE. doi: 10.1109/icde53745.2022.00131

Network Alignment with Holistic Embeddings (Extended Abstract)

2022

Conference Publication

Uniting heterogeneity, inductiveness, and efficiency for graph representation learning (Extended Abstract)

Chen, Tong, Yin, Hongzhi, Ren, Jie, Huang, Zi, Zhang, Xiangliang and Wang, Hao (2022). Uniting heterogeneity, inductiveness, and efficiency for graph representation learning (Extended Abstract). 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, 9-12 May 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/icde53745.2022.00143

Uniting heterogeneity, inductiveness, and efficiency for graph representation learning (Extended Abstract)

2022

Journal Article

Multi-graph heterogeneous interaction fusion for social recommendation

Zhang, Chengyuan, Wang, Yang, Zhu, Lei, Song, Jiayu and Yin, Hongzhi (2022). Multi-graph heterogeneous interaction fusion for social recommendation. ACM Transactions on Information Systems, 40 (2) 3466641, 1-26. doi: 10.1145/3466641

Multi-graph heterogeneous interaction fusion for social recommendation

2022

Journal Article

Sequential-knowledge-aware next POI recommendation: a meta-learning approach

Cui, Yue, Sun, Hao, Zhao, Yan, Yin, Hongzhi and Zheng, Kai (2022). Sequential-knowledge-aware next POI recommendation: a meta-learning approach. ACM Transactions on Information Systems, 40 (2) 3460198, 1-22. doi: 10.1145/3460198

Sequential-knowledge-aware next POI recommendation: a meta-learning approach

2022

Conference Publication

ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs

Wang, Yanling, Zhang, Jing, Li, Haoyang, Dong, Yuxiao, Yin, Hongzhi, Li, Cuiping and Chen, Hong (2022). ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs. WWW '22: The ACM Web Conference 2022, Lyon, France, 25 - 29 April 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3485447.3512207

ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs

2022

Conference Publication

Unified question generation with continual lifelong learning

Yuan, Wei, Yin, Hongzhi, He, Tieke, Chen, Tong, Wang, Qiufeng and Cui, Lizhen (2022). Unified question generation with continual lifelong learning. WWW 2022 - ACM Web Conference 2022, Virtual Event, Lyon, France, 25-29 April 2022. New York, United States: Association for Computing Machinery. doi: 10.1145/3485447.3511930

Unified question generation with continual lifelong learning

2022

Conference Publication

Accepted Tutorials at The Web Conference 2022

Tommasini, Riccardo, Roy, Senjuti Basu, Wang, Xuan, Wang, Hongwei, Ji, Heng, Han, Jiawei, Nakov, Preslav, Da San Martino, Giovanni, Alam, Firoj, Schedl, Markus, Lex, Elisabeth, Bharadwaj, Akash, Cormode, Graham, Dojchinovski, Milan, Forberg, Jan, Frey, Johannes, Bonte, Pieter, Balduini, Marco, Belcao, Matteo, Della Valle, Emanuele, Yu, Junliang, Yin, Hongzhi, Chen, Tong, Liu, Haochen, Wang, Yiqi, Fan, Wenqi, Liu, Xiaorui, Dacon, Jamell, Lye, Lingjuan ... He, Xiangnan (2022). Accepted Tutorials at The Web Conference 2022. The Web Conference 2022, Lyon, France, 25 – 29 April 2022. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3487553.3547182

Accepted Tutorials at The Web Conference 2022

2022

Journal Article

Exploiting positional information for session-based recommendation

Qiu, Ruihong, Huang, Zi, Chen, Tong and Yin, Hongzhi (2022). Exploiting positional information for session-based recommendation. ACM Transactions on Information Systems, 40 (2) 3473339, 1-24. doi: 10.1145/3473339

Exploiting positional information for session-based 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

    Deep Learning for Graph Data Analysis

    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

    Meeting Challenges on Secure Recommender Systems

    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

    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

    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

    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

    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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