讲座题目：Revolutionizing Anomaly Detection: Approaches and Guidelines
Dr Ye Zhu is a Senior Lecturer of Computer Science at the School of Information Technology at Deakin University. He is an IEEE senior member and ACM member. He obtained his PhD degree in Artificial Intelligence with a Mollie Holman Medal for the best doctoral thesis of the year from Monash University in 2017. Dr Zhu joined Deakin University as a post-doc research fellow in complex system data analytics in July 2017 and became a lecturer in Feb 2019. His research interests include clustering analysis, anomaly detection, similarity learning and their applications for pattern recognition. Dr Zhu has published about 40 papers in top-tier conferences and journals, such as SIGKDD, ICML, IJCAI, VLDB, AAAI, TKDE, AIJ, ISJ, PRJ, JAIR and MLJ. He has served as Program Chair and Program Committee for many prestigious international conferences. Moreover, he has secured multiple large research grants for interdisciplinary and industrial research. He recently received both an Early Career Researcher Award and a Teaching and Learning Award from the School of IT.
Anomaly detection is a crucial task of data mining and a hot research topic in various fields of artificial intelligence. It has many applications, such as detecting extreme climate events, mechanical faults, terrorists, frauds, malicious URLs, etc. In this talk, Dr Ye Zhu will present a comprehensive review of both shallow and deep-learning-based anomaly detection methods with explanation. He will explain the key intuitions, objective functions, underlying assumptions, and pros and cons of state-of-the-art anomaly detection techniques. He will also introduce several principled approaches to provide anomaly explanations for deep detection models. Furthermore, Dr Zhu will discuss the connections between classic shallow and novel deep methods and provide a practical guide on how to choose an appropriate outlier detector for different scenarios.