Many machine learning and data mining approaches focus on discovering predictive patterns in data. An equally important task is to look for weird but meaningful data instances that do not fit the dominant patterns. This area of machine is often referred to as anomaly detection, but this term is a wide umbrella that includes many different versions of this task. Anomaly detection plays a critical role in many real-world applications such as biosurveillance, fraud detection, computer security and safety monitoring. This talk will include a high-level overview of anomaly detection and a discussion of recent research developments in this area.
Weng-Keen Wong is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University. He received his Ph.D. (2004) and M.S. (2001) in Computer Science at Carnegie Mellon University, and his B.Sc. (1997) from the University of British Columbia. From 2016-2018, he served as a Program Director at the National Science Foundation under the Robust Intelligence Program in the Division of Information and Intelligent Systems. His research areas are in data mining and machine learning, with specific interests in anomaly detection, probabilistic graphical models, computational sustainability and human-in-the-loop learning.