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 (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.

Latest News

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

  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).

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

  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.

  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.

  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.

  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.

  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).

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

358 works between 2011 and 2025
All (358) Journal Article (138) Book Chapter (4) Conference Publication (208) Book (2) Edited Outputs (6)

281 - 300 of 358 works

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

2018

Conference Publication

Towards the Learning of Weighted Multi-label Associative Classifiers

Liu, Chunyang, Chen, Ling, Tsang, Ivor and Yin, Hongzhi (2018). Towards the Learning of Weighted Multi-label Associative Classifiers. 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8 - 13, 2018. Institute of Electrical and Electronics Engineers. doi: 10.1109/IJCNN.2018.8489398

Towards the Learning of Weighted Multi-label Associative Classifiers

2018

Conference Publication

TADA: trend alignment with dual-attention multi-task recurrent neural networks for sales prediction

Chen, Tong, Yin, Hongzhi, Chen, Hongxu, Wu, Lin, Wang, Hao, Zhou, Xiaofang and Li, Xue (2018). TADA: trend alignment with dual-attention multi-task recurrent neural networks for sales prediction. 18th IEEE International Conference on Data Mining, ICDM 2018, Singapore, 17-20 November 2018. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/ICDM.2018.00020

TADA: trend alignment with dual-attention multi-task recurrent neural networks for sales prediction

2018

Conference Publication

Exploiting reshaping subgraphs from bilateral propagation graphs

Hosseini, Saeid, Yin, Hongzhi, Cheung, Ngai-Man, Leng, Kan Pak, Elovici, Yuval and Zhou, Xiaofang (2018). Exploiting reshaping subgraphs from bilateral propagation graphs. 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, Gold Coast, QLD, Australia, 21-24 May 2018. Cham, Switzerland: Springer Verlag. doi: 10.1007/978-3-319-91452-7_23

Exploiting reshaping subgraphs from bilateral propagation graphs

2018

Conference Publication

Computing crowd consensus with partial agreement

Nguyen, Quoc Viet Hung, Huynh, Huu Viet, Nguyen, Thanh Tam, Weidlich, Matthias, Yin, Hongzhi and Zhou, Xiaofang (2018). Computing crowd consensus with partial agreement. 34th IEEE International Conference on Data Engineering (ICDE 2018), Paris, France, 16-19 April 2018. NEW YORK: IEEE. doi: 10.1109/ICDE.2018.00232

Computing crowd consensus with partial agreement

2018

Conference Publication

Look deeper see richer: Depth-aware image paragraph captioning

Wang, Ziwei, Luo, Yadan, Li, Yang, Huang, Zi and Yin, Hongzhi (2018). Look deeper see richer: Depth-aware image paragraph captioning. 26th ACM Multimedia conference, MM 2018, Seoul, South Korea, October 22 - 26, 2018. New York, NY, Untied States: Association for Computing Machinery, Inc. doi: 10.1145/3240508.3240583

Look deeper see richer: Depth-aware image paragraph captioning

2018

Book Chapter

Spatiotemporal recommendation in geo-social networks

Yin, Hongzhi, Cui, Bin and Zhou, Xiaofang (2018). Spatiotemporal recommendation in geo-social networks. Encyclopedia of Social Network Analysis and Mining. (pp. 2930-2948) edited by Reda Alhajj and Jon Rokne. New York, NY, United States: Springer New York. doi: 10.1007/978-1-4939-7131-2_110177

Spatiotemporal recommendation in geo-social networks

2018

Conference Publication

Publishing graph node strength histogram with edge differential privacy

Qian, Qing, Li, Zhixu, Zhao, Pengpeng, Chen, Wei, Yin, Hongzhi and Zhao, Lei (2018). Publishing graph node strength histogram with edge differential privacy. 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_5

Publishing graph node strength histogram with edge differential privacy

2018

Conference Publication

TSAUB: a temporal-sentiment-aware user behavior model for personalized recommendation

Wang, Qinyong, Yin, Hongzhi, Wang, Hao and Huang, Zi (2018). TSAUB: a temporal-sentiment-aware user behavior model for personalized recommendation. 29th Australasian Database Conference, ADC 2018, Gold Coast, QLD, Australia, 24-27 May 2018. Heidelberg, Germany: Springer Verlag. doi: 10.1007/978-3-319-92013-9_17

TSAUB: a temporal-sentiment-aware user behavior model for personalized recommendation

2018

Conference Publication

Unified user and item representation learning for joint recommendation in social network

Yang, Jiali, Li, Zhixu, Yin, Hongzhi, Zhao, Pengpeng, Liu, An, Chen, Zhigang and Zhao, Lei (2018). Unified user and item representation learning for joint recommendation in social network. 19th International Conference on Web Information Systems Engineering, WISE 2018, Dubai, United Arab Emirates, November 12 - 15, 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-02925-8_3

Unified user and item representation learning for joint recommendation in social network

2018

Conference Publication

Call attention to rumors: deep attention based recurrent neural networks for early rumor detection

Chen, Tong, Li, Xue, Yin, Hongzhi and Zhang, Jun (2018). Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018, Melbourne, VIC, Australia, 3 June 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-04503-6_4

Call attention to rumors: deep attention based recurrent neural networks for early rumor detection

2018

Conference Publication

Mining geo-social networks - spatial item recommendation

Yin, Hongzhi and Wang, Weiqing (2018). Mining geo-social networks - spatial item recommendation. 29th Australasian Database Conference (ADC), Gold Coast, Australia, 24-27 May 2018. Cham, Switzerland: Springer.

Mining geo-social networks - spatial item recommendation

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

Availability

Professor Hongzhi Yin is:
Available for supervision

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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.

  • 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

    Decentralized Point-Of-Interest (POI) Recommender Systems

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Meeting Challenges on Secure Recommender Systems

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Decentralized Point-Of-Interest (POI) Recommender Systems

    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

    Secure Cross-device Federated Recommender Systems

    Principal Advisor

    Other advisors: Dr Miao Xu

  • 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

    Deep Learning for Univariate Time Series Anomaly Detection in Industrial IoT

    Principal Advisor

    Other advisors: Dr Thomas Taimre, Dr Slava Vaisman

  • Doctor Philosophy

    Joint Feature Learning for Recommender System

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Secure Cross-device Federated 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

    Decentralized Learning for On-device Recommendation

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    LLM-enhanced Recommender System

    Principal Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Knowledge Graph-based Conversational Recommender Systems

    Principal Advisor

    Other advisors: Dr Miao Xu

  • Doctor Philosophy

    Secure Cross-device Federated Recommender Systems

    Principal Advisor

    Other advisors: Dr Miao Xu

  • Doctor Philosophy

    Deep Learning for Univariate Time Series Anomaly Detection in Industrial IoT

    Principal Advisor

    Other advisors: Dr Thomas Taimre, Dr Slava Vaisman

  • Doctor Philosophy

    Image Generation from Texts

    Principal Advisor

    Other advisors: Dr Thomas Taimre, Dr Slava Vaisman

  • Doctor Philosophy

    Decentralized Learning for On-device Recommendation

    Principal 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

    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 Generalizable Graph Neural Networks

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Understanding and mitigating greenhouse gas emissions from wastewater system in the data era

    Associate Advisor

    Other advisors: Dr Haoran Duan, Professor Liu Ye

  • Doctor Philosophy

    Scalable and Lightweight On-Device Recommender Systems

    Associate Advisor

    Other advisors: Dr Rocky Chen, Dr Junliang Yu

  • Doctor Philosophy

    Lightweight Graph Neural Networks for Recommendation

    Associate Advisor

    Other advisors: Dr Rocky Chen

  • Doctor Philosophy

    Understanding and mitigating greenhouse gas emissions from wastewater system in the data era

    Associate Advisor

    Other advisors: Dr Haoran Duan, Professor Liu Ye

  • 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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