Hongzhi Yin

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Dr. Hongzhi Yin works as a senior lecturer and an ARC DECRA Fellow (Australia Discovery Early Career Researcher Award) with The University of Queensland, Australia. He received his doctoral degree from Peking University in July 2014, and his PhD Thesis won the highly competitive Distinguished Doctor Degree Thesis Award of Peking University. His current main research interests include recommender system, social media analytics and mining, network embedding and mining, time series data and sequence data mining and learning, chatbots, federated learning, topic models, deep learning and smart transportation. He has published 130+ papers, including 18 Highly Cited Papers (Top 1%), 27 Q1, 65+ CCF A and 40+ CCF B, 65 CORE A* and 45+ CORE A, such as KDD (x11), ICDE (x16), VLDB (x4), SIGIR (x6), ACM TOIS (x7), IEEE TKDE (x5), IJCAI (x4), AAAI (x3), VLDB Journal (x2), ICDM (x3), ACM Multimedia (x2), WWW (x3), CIKM (x6), SIGMOD and WSDM. He is the leading author (first author or corresponding author) for 80+ of them. He has won 5 Best Paper Awards such as ICDE'19 Best Paper Award and 21st ACM Annual Best of Computing Article as the first author. He has received ARC Discovery Early Career Researcher Award (DECRA) within his first year of obtaining his PhD, ARC Discovery Project 2019 (Sole CI) as an early-career researcher, UQ Foundation Research Excellence Award 2019 as the first winner of this award in School of ITEE since the establishment of this award 20 years ago. He is currently serving as Associate Editor for Springer Nature Computer Science, IEEE Journal of Social Computing, Academic Editor for Complexity, Editorial Board of Young Scientists for Journal of Computer Science and Technology (JCST), and Guest Editor of Information Systems, World Wide Web and JCST. He is currently directing the RSBDI (Responsible and Sustainable Big Data Intelligence) Lab. RSBDI Lab aims and strives to develop socially responsible and environmentally sustainable data mining and machine learning techniques with theoretical backbones to better discover actionable patterns and intelligence from large-scale, networked, dynamic and sparse data. The research of RSBDI Lab is directly motivated by, and contributes to, applications in E-commerce and marketing, social informatics, biomedical and health informatics, urban transportation and information security. His research has also been attracting media coverage, such as UQ News, Faculty News of EAIT, Computing Reviews, 360 News.