[Colloq] REMINDER (TODAY): Title: 3 D's of Anomaly Mining in Complex Graphs: Definition, Detection, and Description | Leman Akoglu, Stony Brook University | 2/17/16 2-3pm 366 WVH

[Walker, Lashauna]

Title: 3 D's of Anomaly Mining in Complex Graphs: Definition, Detection, and Description
Speaker: Leman Akoglu, Stony Brook University
Date: 2/17/16
Time:  2-3pm
Location: 366 WVH


Title:  3 D's of Anomaly Mining in Complex Graphs: Definition, Detection, and Description
Abstract:
Anomaly mining is critical for a large variety of real-world tasks in security, finance, medicine, and so on. Despite its immense popularity however, the problem is under-specified for many practical applications, such as insider threat detection, as the true goals are often difficult to specify. 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: in providing precise 'D'efinitions of what an anomaly is, in effectively 'D'etecting anomalies, and finally in providing practitioners with actionable 'D'escriptions of the detected anomalies.
My 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 I will first focus on a new model of neighborhoods in graphs with node attributes. The model utilizes both the structure and the attributes to characterize and quantify normality, and can be used for spotting anomalies. I will next shift focus to a new formalization for detecting suspicious nodes in large heterogeneous graphs, motivated by but generalizes from its application to bank fraud. I will then present a new model to summarize individual node anomalies through the groups that they form in the graph. This work constitutes representative steps on all three fronts of the aforementioned challenges, namely the three 'D's of anomaly mining.

Speaker 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 arising in graph mining, pattern discovery, social and information networks, and especially anomaly mining; outlier, fraud, and event detection. Dr. Akoglu's research has won 4 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. 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 US Army Research Office, NSF, 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



Thank You.

LaShauna Walker
Events and Administrative Specialist
College of Computer and Information Science
Northeastern University
617-373-2763
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