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, the Australian Research Council Future Fellowship 2021, the Discovery Early Career Researcher Award 2016, the UQ Foundation Research Excellence Award 2019, the 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 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 77, 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, and 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
-
[23 September 2024] We have three journal papers recognized as ESI Hot and Highly Cited papers.
-
[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.
-
[24 August 2024] Two of my PhD graduates have been awarded the competitive ARC DECRA Fellowship. Congratulations to Weiqing and Junliang.
-
[23 July 2024] Recently, we have released 3 comprehensive survey papers.
-
[2 July 2024] I have been invited to serve as area chair at KDD 2025.
-
[27 June 2024] Our ARC Linkage Project "Building an Aussie Information Recommendation System You Can Trust" has been granted and funded.
-
[16 June 2024] I have been invited to co-chair the User modeling, personalization and recommendation track at The Web Conference 2025.
-
[6 June 2024] Recently, we have released 2 comprehensive survey papers.
-
[23 May 2024] Our project Personalized On-Device Large Language Models was shortlisted as a finalist for the 2024 iAwards.
-
[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).
-
[17 May 2024] We have 4 full research research papers accepted by the prestigious conference KDD 2024 (CORE A*, CCF A).
-
[24 April 2024] I have been recognized with 2024 Computer Science in Australia Leader Award in Research.com.
-
[26 March 2024] We have three research papers accepted by the top conference SIGIR 2024 (CORE A*, CCF A).
-
[14 March 2024] I have again been recognized as the 2024 AI 2000 Most Influential Scholar Honorable Mention in Data Mining.
-
[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.
-
Hide Your Model: A Parameter Transmission-free Federated Recommender System
-
Unraveling the 'Anomaly' in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution
-
BIM: Improving Graph Neural Networks with Balanced Influence Maximization
-
Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation
-
Graph Condensation for Inductive Node Representation Learning
-
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection
-
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
-
-
[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.
-
[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.
-
Hide Your Model: A Parameter Transmission-free Federated Recommender System
-
Open-World Semi-Supervised Learning for Node Classification
-
-
[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.
-
[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).
-
[24 January 2024] We have five research papers and one tutorial accepted by The Web Conference 2024 (CORE A*, CCF A).
-
On-Device Recommender Systems: A Tutorial on The New-Generation Recommendation Paradigm
-
Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation
-
Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation
-
Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation
-
Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation
-
-
[23 January 2024] We have released three timely surveys:
-
[19 January 2024] I have been invited to serve as Official Nominator for VinFuture Prize (US$3,000,000). The nomination is open!
-
[13 January 2024] I have been invited to serve as Area Chair in the Research Track of KDD 2024.
-
[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).
-
[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
2021
Journal Article
Secure your ride: real-time matching success rate prediction for passenger-driver pairs
Wang, Yuandong, Yin, Hongzhi, Wu, Lian, Chen, Tong and Liu, Chunyang (2021). Secure your ride: real-time matching success rate prediction for passenger-driver pairs. IEEE Transactions on Knowledge and Data Engineering, PP (99), 1-14. doi: 10.1109/tkde.2021.3112739
2021
Conference Publication
Self-supervised hypergraph convolutional networks for session-based recommendation
Xia, Xin, Yin, Hongzhi, Yu, Junliang, Wang, Qinyong, Cui, Lizhen and Zhang, Xiangliang (2021). Self-supervised hypergraph convolutional networks for session-based recommendation. 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence, Virtual, 2-9 February 2021. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence Press.
2021
Conference Publication
Memory augmented multi-instance contrastive predictive coding for sequential recommendation
Qiu, Ruihong, Huang, Zi and Yin, Hongzhi (2021). Memory augmented multi-instance contrastive predictive coding for sequential recommendation. IEEE International Conference on Data Mining, Auckland, New Zealand, 7-10 December 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICDM51629.2021.00063
2021
Journal Article
FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection
Ye, Guanhua, Yin, Hongzhi, Chen, Tong, Chen, Hongxu, Cui, Lizhen and Zhang, Xiangliang (2021). FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection. IEEE Journal of Biomedical and Health Informatics, 25 (8) 9320528, 2848-2856. doi: 10.1109/JBHI.2021.3050113
Featured
2021
Journal Article
Multifractal characterization of distribution synchrophasors for cybersecurity defense of smart grids
Cui, Yi, Bai, Feifei, Yan, Ruifeng, Saha, Tapan, Mosadeghy, Mehdi, Yin, Hongzhi, Ko, Ryan K. L. and Liu, Yilu (2021). Multifractal characterization of distribution synchrophasors for cybersecurity defense of smart grids. IEEE Transactions on Smart Grid, 13 (2), 1658-1661. doi: 10.1109/tsg.2021.3132536
2021
Conference Publication
Subgraph convolutional network for recommendation
Zhao, Yan, Zhou, Lianming, Deng, Liwei, Zheng, Vincent W., Yin, Hongzhi and Zheng, Kai (2021). Subgraph convolutional network for recommendation. 2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS), Xi'an, China, 7-8 November 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/CCIS53392.2021.9754683
2020
Journal Article
Enhanced factorization machine via neural pairwise ranking and attention networks
Yu, Yonghong, Jiao, Lihong, Zhou, Ningning, Zhang, Li and Yin, Hongzhi (2020). Enhanced factorization machine via neural pairwise ranking and attention networks. Pattern Recognition Letters, 140, 348-357. doi: 10.1016/j.patrec.2020.11.010
2020
Journal Article
Entity alignment for knowledge graphs with multi-order convolutional networks
Nguyen, Tam Thanh, Huynh, Thanh Trung, Yin, Hongzhi, Tong, Vinh Van, Sakong, Darnbi, Zheng, Bolong and Nguyen, Quoc Viet Hung (2020). Entity alignment for knowledge graphs with multi-order convolutional networks. IEEE Transactions on Knowledge and Data Engineering, 34 (9), 1-1. doi: 10.1109/TKDE.2020.3038654
2020
Journal Article
Enhance social recommendation with adversarial graph convolutional networks
Yu, Junliang, Yin, Hongzhi, Li, Jundong, Gao, Min, Huang, Zi and Cui, Lizhen (2020). Enhance social recommendation with adversarial graph convolutional networks. IEEE Transactions on Knowledge and Data Engineering, 34 (8), 1-1. doi: 10.1109/tkde.2020.3033673
2020
Journal Article
Semantic trajectory representation and retrieval via hierarchical embedding
Gao, Chongming, Zhang, Zhong, Huang, Chen, Yin, Hongzhi, Yang, Qinli and Shao, Junming (2020). Semantic trajectory representation and retrieval via hierarchical embedding. Information Sciences, 538, 176-192. doi: 10.1016/j.ins.2020.05.107
2020
Journal Article
CRSAL: conversational recommender systems with adversarial learning
Ren, Xuhui, Yin, Hongzhi, Chen, Tong, Wang, Hao, Hung, Nguyen Quoc Viet, Huang, Zi and Zhang, Xiangliang (2020). CRSAL: conversational recommender systems with adversarial learning. ACM Transactions on Information Systems, 38 (4) 3394592, 1-40. doi: 10.1145/3394592
2020
Journal Article
Deep pairwise hashing for cold-start recommendation
Zhang, Yan, Tsang, Ivor, Yin, Hongzhi, Yang, Guowu, Lian, Defu and Li, Jingjing (2020). Deep pairwise hashing for cold-start recommendation. IEEE Transactions on Knowledge and Data Engineering, 34 (7), 1-1. doi: 10.1109/tkde.2020.3024022
2020
Journal Article
Overcoming data sparsity in group recommendation
Yin, Hongzhi, Wang, Qinyong, Zheng, Kai, Li, Zhixu and Zhou, Xiaofang (2020). Overcoming data sparsity in group recommendation. IEEE Transactions on Knowledge and Data Engineering, 34 (7), 1-1. doi: 10.1109/tkde.2020.3023787
2020
Conference Publication
Multi-level graph convolutional networks for cross-platform Anchor Link Prediction
Chen, Hongxu, Yin, Hongzhi, Sun, Xiangguo, Chen, Tong, Gabrys, Bogdan and Musial, Katarzyna (2020). Multi-level graph convolutional networks for cross-platform Anchor Link Prediction. ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, CA, United States, 23-27 August 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3394486.3403201
2020
Conference Publication
FactCatch: incremental pay-as-you-go fact checking with minimal user effort
Nguyen, Thanh Tam, Weidlich, Matthias, Yin, Hongzhi, Zheng, Bolong, Nguyen, Quang Huy and Nguyen, Quoc Viet Hung (2020). FactCatch: incremental pay-as-you-go fact checking with minimal user effort. International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event China, 25-30 July 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3397271.3401408
2020
Conference Publication
GAG: global attributed graph neural network for streaming session-based recommendation
Qiu, Ruihong, Yin, Hongzhi, Huang, Zi and Chen, Tong (2020). GAG: global attributed graph neural network for streaming session-based recommendation. International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event China , 25-30 July 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3397271.3401109
2020
Conference Publication
Try this instead: personalized and interpretable substitute recommendation
Chen, Tong, Yin, Hongzhi, Ye, Guanhua, Huang, Zi, Wang, Yang and Wang, Meng (2020). Try this instead: personalized and interpretable substitute recommendation. International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event China, 25-30 July 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3397271.3401042
2020
Journal Article
User account linkage across multiple platforms with location data
Chen, Wei, Wang, Weiqing, Yin, Hongzhi, Fang, Jun-Hua and Zhao, Lei (2020). User account linkage across multiple platforms with location data. Journal of Computer Science and Technology, 35 (4), 751-768. doi: 10.1007/s11390-020-0250-7
2020
Conference Publication
Discovering subsequence patterns for next POI recommendation
Zhao, Kangzhi, Zhang, Yong, Yin, Hongzhi, Wang, Jin, Zheng, Kai, Zhou, Xiaofang and Xing, Chunxiao (2020). Discovering subsequence patterns for next POI recommendation. Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), Yokohama, Japan, 11-17 July, 2020. California, United States: International Joint Conferences on Artificial Intelligence Organization. doi: 10.24963/ijcai.2020/445
2020
Journal Article
TEAGS: time-aware text embedding approach to generate subgraphs
Hosseini, Saeid, Najafipour, Saeed, Cheung, Ngai-Man, Yin, Hongzhi, Kangavari, Mohammad Reza and Zhou, Xiaofang (2020). TEAGS: time-aware text embedding approach to generate subgraphs. Data Mining and Knowledge Discovery, 34 (4), 1136-1174. doi: 10.1007/s10618-020-00688-7
Funding
Current funding
Past funding
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
Meeting Challenges on Secure Recommender Systems
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
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
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
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
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
-
2023
Doctor Philosophy
From Cloud to Device: Transforming Recommender Systems for On-Device Deployment
Principal Advisor
Other advisors: Dr Miao Xu
-
2023
Doctor Philosophy
Decentralized On-device Machine Learning and Unlearning for IoT Collaboration
Principal Advisor
Other advisors: Dr Miao Xu
-
2023
Doctor Philosophy
Enhancing Recommender Systems wtih Self-Supervised Learning
Principal Advisor
Other advisors: Professor Helen Huang
-
2022
Doctor Philosophy
Secure Recommender Systems
Principal Advisor
Other advisors: Professor Helen Huang
-
2022
Doctor Philosophy
Decentralized Framework for Embedding Large-scale Networks
Principal Advisor
Other advisors: Professor Helen Huang
-
2022
Doctor Philosophy
Toward Deep Conversational Recommender Systems
Principal Advisor
Other advisors: Professor Helen Huang
-
2021
Doctor Philosophy
Lightweight and Secure Deep Learning-based Mobile Recommender Systems
Principal Advisor
Other advisors: Professor Helen Huang
-
2020
Doctor Philosophy
Sequence Modelling for E-Commerce
Principal Advisor
Other advisors: Professor Xue Li
-
2020
Doctor Philosophy
Graph Representation Learning with Attribute Information
Principal Advisor
Other advisors: Professor Xue Li
-
2020
Master Philosophy
Advanced Machine Learning Algorithms for Discrete Datasets
Principal Advisor
Other advisors: Professor Shazia Sadiq
-
2017
Doctor Philosophy
POINT OF INTERESTS RECOMMENDATION IN LOCATION-BASED SOCIAL NETWORKS
Principal Advisor
Other advisors: Professor Shazia Sadiq
-
2023
Doctor Philosophy
Multi-modal Data Modeling with Awareness of Efficiency, Reliability, and Privacy
Associate Advisor
Other advisors: Professor Helen Huang
-
2022
Doctor Philosophy
Neural Attentive Recommender Systems
Associate Advisor
Other advisors: Professor Helen Huang, Dr Rocky Chen
-
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
-
2021
Doctor Philosophy
Towards Efficient Similarity Search with Semantic Hashing Techniques
Associate Advisor
Other advisors: Professor Helen Huang
-
2021
Doctor Philosophy
Multimedia Content Analytics with Modality Transition
Associate Advisor
Other advisors: Professor Helen Huang
-
2018
Doctor Philosophy
Understand Video Event by Exploiting Semantic and Temporal Information for Classification and Retrieval
Associate Advisor
Other advisors: Professor Helen Huang
-
Doctor Philosophy
Modelling Sequential Patterns of User Behaviour in Recommender Systems
Associate Advisor
Other advisors: Professor Helen Huang
Media
Enquiries
For media enquiries about Professor Hongzhi Yin's areas of expertise, story ideas and help finding experts, contact our Media team: