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 (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 80, 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, 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
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[5 December 2024] Our tutorial "Graph Condensation: Foundations, Methods and Prospects" has been accepted for presentation at The Web Conference 2025.
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[30 November 2024] I have been invited to serve as SPC for IJCAI 2025 and DASFAA 2025.
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[29 November 2024] I was honored with The Faculty Higher Degree Research Supervision Excellence Award.
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[19 November 2024] Congratulations to Dr. Liang Qu on being awarded his PhD degree by The University of Queensland.
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[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.
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[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!
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[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.
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[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.
- [26 September 2024] We have one research paper "Distribution-Aware Data Expansion with Diffusion Models" accepted by NeurIPS 2024 (CCF A, CORE A*).
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[23 September 2024] We have three journal papers recognized as ESI Hot and Highly Cited papers.
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[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.
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[24 August 2024] Two of my PhD graduates have been awarded the competitive ARC DECRA Fellowship. Congratulations to Weiqing and Junliang.
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[23 July 2024] Recently, we have released 3 comprehensive survey papers.
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[2 July 2024] I have been invited to serve as area chair at KDD 2025.
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[27 June 2024] Our ARC Linkage Project "Building an Aussie Information Recommendation System You Can Trust" has been granted and funded.
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[16 June 2024] I have been invited to co-chair the User modeling, personalization and recommendation track at The Web Conference 2025.
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[6 June 2024] Recently, we have released 2 comprehensive survey papers.
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[23 May 2024] Our project Personalized On-Device Large Language Models was shortlisted as a finalist for the 2024 iAwards.
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[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).
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[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
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Recommender System and User Modeling
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Graph Mining and Embedding
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Decentralized and Federated Learning
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Edge Machine Learning and Applications
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Trustworthy Machine Learning and Applications
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QA, Chatbot and Information Retrieval
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Time Series and Sequence Mining and Prediction
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Spatiotemporal Data Mining
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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
2025
Journal Article
Handling low homophily in recommender systems with partitioned graph transformer
Nguyen, Thanh Tam, Nguyen, Thanh Toan, Weidlich, Matthias, Jo, Jun, Nguyen, Quoc Viet Hung, Yin, Hongzhi and Liew, Alan Wee-Chung (2025). Handling low homophily in recommender systems with partitioned graph transformer. IEEE Transactions on Knowledge and Data Engineering, 37 (1), 334-350. doi: 10.1109/tkde.2024.3485880
2024
Journal Article
Reliable node similarity matrix guided contrastive graph clustering
Liu, Yunhui, Gao, Xinyi, He, Tieke, Zheng, Tao, Zhao, Jianhua and Yin, Hongzhi (2024). Reliable node similarity matrix guided contrastive graph clustering. IEEE Transactions on Knowledge and Data Engineering, 36 (12), 9123-9135. doi: 10.1109/tkde.2024.3435887
2024
Journal Article
Hyperbolic translation-based sequential recommendation
Yu, Yonghong, Zhang, Aoran, Zhang, Li, Gao, Rong, Gao, Shang and Yin, Hongzhi (2024). Hyperbolic translation-based sequential recommendation. IEEE Transactions on Computational Social Systems, 11 (6), 7467-7483. doi: 10.1109/tcss.2024.3409711
2024
Conference Publication
Physics-guided Active Sample Reweighting for Urban Flow Prediction
Jiang, Wei, Chen, Tong, Ye, Guanhua, Zhang, Wentao, Cui, Lizhen, Huang, Zi and Yin, Hongzhi (2024). Physics-guided Active Sample Reweighting for Urban Flow Prediction. New York, NY, USA: ACM. doi: 10.1145/3627673.3679738
2024
Conference Publication
Preference Prototype-Aware Learning for Universal Cross-Domain Recommendation
Zhang, Yuxi, Zhang, Ji, Xu, Feiyang, Chen, Lvying, Li, Bohan, Guo, Lei and Yin, Hongzhi (2024). Preference Prototype-Aware Learning for Universal Cross-Domain Recommendation. New York, NY, USA: ACM. doi: 10.1145/3627673.3679774
2024
Conference Publication
Efficient and Robust Regularized Federated Recommendation
Liu, Langming, Wang, Wanyu, Zhao, Xiangyu, Zhang, Zijian, Zhang, Chunxu, Lin, Shanru, Wang, Yiqi, Zou, Lixin, Liu, Zitao, Wei, Xuetao, Yin, Hongzhi and Li, Qing (2024). Efficient and Robust Regularized Federated Recommendation. New York, NY, USA: ACM. doi: 10.1145/3627673.3679682
2024
Conference Publication
Watermarking Recommender Systems
Zhang, Sixiao, Long, Cheng, Yuan, Wei, Chen, Hongxu and Yin, Hongzhi (2024). Watermarking Recommender Systems. New York, NY, USA: ACM. doi: 10.1145/3627673.3679617
2024
Conference Publication
Scalable Dynamic Embedding Size Search for Streaming Recommendation
Qu, Yunke, Qu, Liang, Chen, Tong, Zhao, Xiangyu, Nguyen, Quoc Viet Hung and Yin, Hongzhi (2024). Scalable Dynamic Embedding Size Search for Streaming Recommendation. New York, NY, USA: ACM. doi: 10.1145/3627673.3679638
2024
Conference Publication
DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems
Zhang, Sheng, Wang, Maolin, Zhao, Xiangyu, Guo, Ruocheng, Zhao, Yao, Zhuang, Chenyi, Gu, Jinjie, Zhang, Zijian and Yin, Hongzhi (2024). DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems. 18h ACM Conference on Recommender Systems (RecSys), Bari Italy, Oct 14-18, 2024. New York, NY, USA: ACM. doi: 10.1145/3640457.3688117
2024
Journal Article
Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention
Sakong, Darnbi, Vu, Viet Hung, Huynh, Thanh Trung, Nguyen, Phi Le, Yin, Hongzhi, Nguyen, Quoc Viet Hung and Nguyen, Thanh Tam (2024). Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention. Information Sciences, 680 121165, 121165. doi: 10.1016/j.ins.2024.121165
2024
Conference Publication
Graph condensation for open-world graph learning
Gao, Xinyi, Chen, Tong, Zhang, Wentao, Li, Yayong, Sun, Xiangguo and Yin, Hongzhi (2024). Graph condensation for open-world graph learning. KDD '24: 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25-29 August 2024. New York, NY, United States: ACM. doi: 10.1145/3637528.3671917
2024
Conference Publication
Hate speech detection with generalizable target-aware fairness
Chen, Tong, Wang, Danny, Liang, Xurong, Risius, Marten, Demartini, Gianluca and Yin, Hongzhi (2024). Hate speech detection with generalizable target-aware fairness. KDD '24: 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25-29 August 2024. New York, NY, United States: ACM. doi: 10.1145/3637528.3671821
2024
Conference Publication
Unveiling vulnerabilities of contrastive recommender systems to poisoning attacks
Wang, Zongwei, Yu, Junliang, Gao, Min, Yin, Hongzhi, Cui, Bin and Sadiq, Shazia (2024). Unveiling vulnerabilities of contrastive recommender systems to poisoning attacks. 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25-29 August 2024. New York, NY, United States: ACM. doi: 10.1145/3637528.3671795
2024
Conference Publication
Diffusion-based cloud-edge-device collaborative learning for next POI recommendations
Long, Jing, Ye, Guanhua, Chen, Tong, Wang, Yang, Wang, Meng and Yin, Hongzhi (2024). Diffusion-based cloud-edge-device collaborative learning for next POI recommendations. 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25-29 August 2024. New York, NY, United States: ACM. doi: 10.1145/3637528.3671743
2024
Journal Article
Explicit knowledge graph reasoning for conversational recommendation
Ren, Xuhui, Chen, Tong, Nguyen, Quoc Viet Hung, Cui, Lizhen, Huang, Zi and Yin, Hongzhi (2024). Explicit knowledge graph reasoning for conversational recommendation. ACM Transactions on Intelligent Systems and Technology, 15 (4) 86, 1-21. doi: 10.1145/3637216
2024
Journal Article
Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures
Nguyen, Thanh Toan, Quoc Viet Hung, Nguyen, Nguyen, Thanh Tam, Huynh, Thanh Trung, Nguyen, Thanh Thi, Weidlich, Matthias and Yin, Hongzhi (2024). Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures. ACM Computing Surveys, 57 (1) 3, 1-39. doi: 10.1145/3677328
2024
Conference Publication
Poisoning decentralized collaborative recommender system and its countermeasures
Zheng, Ruiqi, Qu, Liang, Chen, Tong, Zheng, Kai, Shi, Yuhui and Yin, Hongzhi (2024). Poisoning decentralized collaborative recommender system and its countermeasures. SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, United States: Association for Computing Machinery. doi: 10.1145/3626772.3657814
2024
Conference Publication
CaseLink: inductive graph learning for legal case retrieval
Tang, Yanran, Qiu, Ruihong, Yin, Hongzhi, Li, Xue and Huang, Zi (2024). CaseLink: inductive graph learning for legal case retrieval. 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657693
2024
Conference Publication
Lightweight embeddings for graph collaborative filtering
Liang, Xurong, Chen, Tong, Cui, Lizhen, Wang, Yang, Wang, Meng and Yin, Hongzhi (2024). Lightweight embeddings for graph collaborative filtering. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657820
2024
Journal Article
A dual benchmarking study of facial forgery and facial forensics
Pham, Minh Tam, Huynh, Thanh Trung, Nguyen, Thanh Tam, Nguyen, Thanh Toan, Nguyen, Thanh Thi, Jo, Jun, Yin, Hongzhi and Hung Nguyen, Quoc Viet (2024). A dual benchmarking study of facial forgery and facial forensics. CAAI Transactions on Intelligence Technology, 9 (6), 1377-1397. doi: 10.1049/cit2.12362
Funding
Current funding
Past funding
Supervision
Availability
- Professor Hongzhi Yin is:
- Available for supervision
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Available projects
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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.
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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
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Doctor Philosophy
Knowledge Graph-based Conversational Recommender Systems
Principal Advisor
Other advisors: Dr Miao Xu
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Doctor Philosophy
Decentralised Collaborative Predictive Analytics on Personal Smart Devices
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Image Generation from Texts
Principal Advisor
Other advisors: Dr Thomas Taimre, Dr Slava Vaisman
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Doctor Philosophy
Decentralised Collaborative Predictive Analytics on Personal Smart Devices
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Decentralized Point-Of-Interest (POI) Recommender Systems
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Meeting Challenges on Secure Recommender Systems
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Federated Graph Neural Network-based Recommender Systems
Principal Advisor
Other advisors: Dr Miao Xu
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Doctor Philosophy
Deep Learning for Univariate Time Series Anomaly Detection in Industrial IoT
Principal Advisor
Other advisors: Dr Thomas Taimre, Dr Slava Vaisman
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Doctor Philosophy
Decentralised Collaborative Predictive Analytics on Personal Smart Devices
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Joint Feature Learning for Recommender System
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Deep Learning for Graph Data Analysis
Principal Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Causal Analysis for Decision Support in Public Health
Associate Advisor
Other advisors: Professor Shazia Sadiq, Dr Rocky Chen
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Doctor Philosophy
Integrated high-throughput material synthesis and characterisation system
Associate Advisor
Other advisors: Associate Professor Jingwei Hou
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Doctor Philosophy
Scalable and Lightweight On-Device Recommender Systems
Associate Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Lightweight Graph Neural Networks for Recommendation
Associate Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Scalable and Lightweight On-Device Recommender Systems
Associate Advisor
Other advisors: Dr Rocky Chen
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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
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Doctor Philosophy
Sustainable On-Device Recommender Systems
Associate Advisor
Other advisors: Dr Rocky Chen
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Doctor Philosophy
Scalable and Generalizable Graph Neural Networks
Associate Advisor
Other advisors: Dr Rocky Chen
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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
Completed supervision
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2024
Doctor Philosophy
Federated Graph Neural Network-based Recommender Systems
Principal Advisor
Other advisors: Dr Miao Xu
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2023
Doctor Philosophy
From Cloud to Device: Transforming Recommender Systems for On-Device Deployment
Principal Advisor
Other advisors: Dr Miao Xu
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2023
Doctor Philosophy
Decentralized On-device Machine Learning and Unlearning for IoT Collaboration
Principal Advisor
Other advisors: Dr Miao Xu
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2023
Doctor Philosophy
Enhancing Recommender Systems wtih Self-Supervised Learning
Principal Advisor
Other advisors: Professor Helen Huang
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2022
Doctor Philosophy
Toward Deep Conversational Recommender Systems
Principal Advisor
Other advisors: Professor Helen Huang
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2022
Doctor Philosophy
Secure Recommender Systems
Principal Advisor
Other advisors: Professor Helen Huang
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2022
Doctor Philosophy
Decentralized Framework for Embedding Large-scale Networks
Principal Advisor
Other advisors: Professor Helen Huang
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2021
Doctor Philosophy
Lightweight and Secure Deep Learning-based Mobile Recommender Systems
Principal Advisor
Other advisors: Professor Helen Huang
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2020
Doctor Philosophy
Sequence Modelling for E-Commerce
Principal Advisor
Other advisors: Professor Xue Li
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2020
Doctor Philosophy
Graph Representation Learning with Attribute Information
Principal Advisor
Other advisors: Professor Xue Li
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2020
Master Philosophy
Advanced Machine Learning Algorithms for Discrete Datasets
Principal Advisor
Other advisors: Professor Shazia Sadiq
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2017
Doctor Philosophy
POINT OF INTERESTS RECOMMENDATION IN LOCATION-BASED SOCIAL NETWORKS
Principal Advisor
Other advisors: Professor Shazia Sadiq
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2023
Doctor Philosophy
Multi-modal Data Modeling with Awareness of Efficiency, Reliability, and Privacy
Associate Advisor
Other advisors: Professor Helen Huang
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2022
Doctor Philosophy
Neural Attentive Recommender Systems
Associate Advisor
Other advisors: Professor Helen Huang, Dr Rocky Chen
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2022
Master Philosophy
An exploration into the correlation between users' intentions and candidates for query- and non-query-based retrieval
Associate Advisor
Other advisors: Professor Helen Huang
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2021
Doctor Philosophy
Towards Efficient Similarity Search with Semantic Hashing Techniques
Associate Advisor
Other advisors: Professor Helen Huang
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2021
Doctor Philosophy
Multimedia Content Analytics with Modality Transition
Associate Advisor
Other advisors: Professor Helen Huang
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2018
Doctor Philosophy
Understand Video Event by Exploiting Semantic and Temporal Information for Classification and Retrieval
Associate Advisor
Other advisors: Professor Helen Huang
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Doctor Philosophy
Modelling Sequential Patterns of User Behaviour in Recommender Systems
Associate Advisor
Other advisors: Professor Helen Huang
Media
Enquiries
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