Rate My Professor Jia Wu

Professor Jia Wu is a Full Professor in the School of Computing at Macquarie University, where he also holds the position of ARC Future Fellow (Level 3, 2026-2030) for the project 'Next-Generation Graph-Level Mining for High-Complexity Data Environments' funded by $1,173,569, Research Director for the Centre for Applied Artificial Intelligence, and Director of Higher Degree Research in the School of Computing. He earned his PhD in Computer Science from the University of Technology Sydney. Previously, Wu was an ARC DECRA Fellow in the School of Computing. His research specializes in data mining and artificial intelligence, encompassing graph mining, graph neural networks, knowledge graphs, recommendation systems, social networks, time series analysis, deep learning, graph search, multi-objective optimisation, anomaly detection, and brain graph analysis. Since 2009, he has published over 200 refereed journal and conference papers in leading venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Multimedia, IEEE Transactions on Industrial Informatics, IEEE Transactions on Neural Networks and Learning Systems, International Joint Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, IEEE International Conference on Data Mining, The Web Conference, and Neural Information Processing Systems.

Professor Wu has garnered significant recognition for his contributions, including being named Australia’s Top Researcher in Databases and Information Systems by The 2025 Australian Research Magazine, selected as a Clarivate Highly Cited Researcher in the top 0.1% globally (2024), recipient of the 2025 CORE Outstanding Research Contribution Award—the sole annual award for one computer science researcher across Australia and New Zealand—and the Heidelberg Laureate Forum Fellowship from the Australian Academy of Science (2019). He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and ACM Transactions on Knowledge Discovery from Data, and as a Senior Member of the IEEE. Additionally, he has chaired numerous roles in prestigious conferences such as KDD, ICDM, WSDM, IJCAI, AAAI, WWW, NeurIPS, and CIKM for over a decade. His research team has secured awards including the ADMA'24 Best Paper Award, CIKM'22 Best Paper Runner-Up and Best Paper Honorable Mention, ICDM'21 Best Student Paper Award, SDM'18 Best Paper in Data Science Track, and IJCNN'17 Best Student Paper Award. Key publications feature 'A comprehensive survey on graph anomaly detection with deep learning' (2021, 1139 citations), 'A comprehensive survey on community detection with deep learning' (2022, 681 citations), and 'Training deep neural networks on imbalanced data sets' (2016, 685 citations), underscoring his substantial impact on data mining and AI fields.