Anomaly Mining: Detection, Explanation, Interaction
Anomaly mining is a key unsupervised learning task, with numerous applications in finance, security, surveillance, etc. Despite its importance and extensive work on the topic, anomaly mining remains a challenging subject in part due to the tremendous variety of both the forms that anomalies can take and the settings in which they are to be identified. One of the main thrusts of my research has been in tackling these challenges in anomaly mining by building models that are suitable for different practically-relevant settings.
In this talk, I will highlight some vignettes from my recent work on streaming, contextual, and relational anomaly detection with concrete applications to intrusion, ad fraud, tax and credit card fraud detection. I will also discuss how to improve detection quality by bringing human-in-the-loop, with a focus on auditing systems. Finally I will move beyond detection and introduce new approaches for explaining anomalies toward verification and sense-making.
Leman Akoglu joined the Heinz College faculty as an Assistant Professor in Fall 2016. She also holds a courtesy appointment in the Computer Science Department (CSD) and the Machine Learning Department (MLD) of School of Computer Science (SCS). Prior to this she was an Assistant Professor in the Department of Computer Science at Stony Brook University since receiving her Ph.D. from CSD/SCS of Carnegie Mellon University in 2012.
Dr. Akoglu’s research interests span a wide range of data mining and machine learning topics with a focus on algorithmic problems arising in graph mining, pattern discovery, social and information networks, and especially anomaly mining; outlier, fraud, and event detection. At Heinz, Dr. Akoglu directs the Data Analytics Techniques Algorithms (DATA) Lab.