Hongzhi Yin

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 300 papers with an H-index of 83 (20000+ citations), including 250+ CCF A/CORE A* and 80+ CCF B/CORE A, such as 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.

• https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/building-trustworthy-information-recommendation-system

• https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/build-trustworthy-information-recommendation-system

Latest News

1. [4 April 2025] We have four full research papers accepted by the top conference SIGIR 2025 (CORE A*, CCF A).

   • ID-Free Not Risk-Free: LLM-Powered Agents Unveil Risks in ID-Free Recommender Systems

   • Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation

   • Towards Distribution Matching between Collaborative and Language Spaces for Generative Recommendation

   • STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation

2. [2 April 2025] Congratulations to the three new doctors, Dr. Wei Yuan, Dr. Jing Long and Dr. Yuting Sun, who were awarded their PhD by The University of Queensland.

3. [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).

4. [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.

5. [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.

6. [26 January 2025] Our survey paper "Graph Condensation: A Survey" has been accepted by TKDE 2025 (CORE A*, CCF A).

7. [20 January 2025] We have three full research papers and one demo paper accepted by the top conference WWW 2025 (CORE A*, CCF A).

   • Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition

   • On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective

   • Epidemiology-informed Network for Robust Rumor Detection

   • BiasNavi: LLM-Empowered Data Bias Management

8. [18 January 2025] We have two research papers accepted by AAAI 2025 (CCF A, CORE A*) for Oral Presentation.

   • Efficient Traffic Prediction through Spatio-Temporal Distillation

   • Rethinking Cancer Gene Identification through Graph Anomaly Analysis

9. [5 December 2024] Our tutorial "Graph Condensation: Foundations, Methods and Prospects" has been accepted for presentation at The Web Conference 2025.

10. [30 November 2024] I have been invited to serve as SPC for IJCAI 2025 and DASFAA 2025.

11. [29 November 2024] I was honored with The Faculty Higher Degree Research Supervision Excellence Award.

12. [19 November 2024] Congratulations to Dr. Liang Qu on being awarded his PhD degree by The University of Queensland.

13. [17 November 2024] Our research paper "Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation" was accepted by the top conference KDD 2025 (CCF A, CORE A*). Congratulations to Hechuan.

14. [24 October 2024] Our research paper "Physics-guided Active Sample Reweighting for Urban Flow Prediction" won the Best Student Full Paper Award at the top conference CIKM 2024. Congratulations to Wei!

15. [18 October 2024] We have published two survey papers in top-tier journals: ACM Computing Surveys and Science China Information Sciences. Additionally, we have recently released two new survey papers on arXiv.

    • Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures (ACM Computing Surveys 2024)

    • A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures (SCIENCE CHINA - Information Science 2024)

    • Graph Condensation: A Survey (arXiv)

    • A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security (arXiv)

16. [17 October 2024] We have two research papers "PUMA: Efficient Continual Graph Learning with Graph Condensation" and "Handling Low Homophily in Recommender Systems with Partitioned Graph Transformer" accepted by the top journal TKDE.

17. [26 September 2024] We have one research paper "Distribution-Aware Data Expansion with Diffusion Models" accepted by NeurIPS 2024 (CCF A, CORE A*).

18. [23 September 2024] We have three journal papers recognized as ESI Hot and Highly Cited papers.

    • Self-Supervised Learning for Recommender Systems: A Survey (Hot and Highly Cited)

    • XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation (Highly Cited)

    • Enhancing Social Recommendation With Adversarial Graph Convolutional Networks (Highly Cited)

19. [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.

20. [24 August 2024] Two of my PhD graduates have been awarded the competitive ARC DECRA Fellowship. Congratulations to Weiqing and Junliang.

21. [23 July 2024] Recently, we have released 3 comprehensive survey papers.

    • Graph Condensation: A Survey

    • Poisoning Attacks and Defenses in Recommender Systems: A Survey

    • A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures

22. [2 July 2024] I have been invited to serve as area chair at KDD 2025.

23. [27 June 2024] Our ARC Linkage Project "Building an Aussie Information Recommendation System You Can Trust" has been granted and funded.

24. [16 June 2024] I have been invited to co-chair the User modeling, personalization and recommendation track at The Web Conference 2025.

25. [6 June 2024] Recently, we have released 2 comprehensive survey papers.

    • Poisoning Attacks and Defenses in Recommender Systems: A Survey

    • A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures

26. [23 May 2024] Our project Personalized On-Device Large Language Models was shortlisted as a finalist for the 2024 iAwards.

27. [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).

28. [17 May 2024] We have 4 full research research papers accepted by the prestigious conference KDD 2024 (CORE A*, CCF A).

    • Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations

    • Graph Condensation for Open-World Graph Learning

    • Hate Speech Detection with Generalizable Topic-aware Fairness

    • Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks