Overview:

Anomaly mining is critical for a large variety of real-world tasks in security, finance, medicine, and so on. Despite its immense popularity, the problem is under-specified for many practical applications including insider threat detection, as the true goals are often difficult to specify. The research community has long focused on a few simple formulations that do not meet the needs of modern anomaly mining tasks in complex systems. The problem of anomaly mining presents pressing challenges along three main dimensions or three "D's" of anomaly mining: In providing precise Definitions of what an anomaly is, in effectively Detecting anomalies, and finally in providing practitioners with actionable Descriptions of the detected anomalies.

Leman Akoglu research focuses broadly on building new descriptive models and methods for anomaly mining in large complex graphs, and addresses challenges arising from scale, heterogeneity, dynamics, robustness, and interpretability.

In this talk, Akoglu will first focus on a new model of neighborhoods in graphs with node attributes. The model uses both the structure and the attributes to characterize and quantify normality, and can be used for spotting anomalies. From there, Akoglu will shift his focus to a new formalization for detecting suspicious nodes in heterogeneous graphs, motivated by, but generalizing, from its application to bank fraud. He will then present a new model to summarize individual node anomalies through the groups that they form in the graph. These work constitute representative steps on all three fronts of the aforementioned challenges.

Bio:

Leman Akoglu is an assistant professor in the Department of Computer Science at Stony Brook University. She received her Ph.D. from the Computer Science Department at Carnegie Mellon University in 2012. She also spent summers at IBM T. J. Watson Research Labs and Microsoft Research at Redmond. Her research interests span a wide range of data mining and machine learning topics with a focus on algorithmic problems in graph mining, pattern discovery, social and information networks, and especially anomaly mining; outlier, fraud, and event detection.

Dr. Akoglu's research has won four publication awards; Best Research Paper at SIAM SDM 2015, Best Paper at ADC 2014, Best Paper at PAKDD 2010, and Best Knowledge Discovery Paper at ECML/PKDD 2009. She also holds three U.S. patents filed by IBM T. J. Watson Research Labs. Dr. Akoglu is a recipient of the NSF CAREER award (2015) and Army Research Office Young Investigator award (2013). Her research is currently supported by the National Science Foundation, the US Army Research Office, DARPA, a gift from Northrop Grumman Aerospace Systems, and a gift from Facebook.

More details can be found at: http://www.cs.stonybrook.edu/~leman