ML/Google Distinguished Lecture Series-Machine Learning Department - Carnegie Mellon University

Machine Learning Special Seminars

Fall 2016 Seminar
Date: October 18, 2016
Time: 4:30 PM - 00:00 AM
Location: 6115 Gates and Hillman Centers
Speaker: Leman Akoglu Assistant Professor of Information Systems
Title: Tackling Anomaly Mining Problems in the Wild with Networks and Beyond
Abstract: Anomaly mining is critical for a 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 the real world, and addresses challenges arising from scale, data multimodality, dynamics, robustness and interpretability.  In this talk I will introduce representative work from our research that address real-world anomaly mining tasks. In particular I will talk about finding anomalous communities in social networks, detecting fake review(er)s in online review sites, and spotting suspicious host-level activity from system logs. I will argue that while it is the ultimate goal to build a unifying anomaly mining framework, different problems pose different challenges and have their own intricacies. I will introduce how we approach these problems with a goal to address all of the three aforementioned challenges, namely the three ‘D’s of anomaly mining.
Speaker Bio: Leman Akoglu is an assistant professor of Information Systems at the Heinz College of Carnegie Mellon University since Fall 2016. Between 2012-2016, she was an assistant professor at Stony Brook University, prior to which she received her Ph.D. from the Computer Science Department at Carnegie Mellon University. (and before that she was young) 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 5 publication awards; Best Paper Runner-up at SIAM SDM 2016, Best 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 3 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, a gift from Facebook, and project grants from PNC Center For Financial Services Innovation and PwC Risk and Regulatory Services Innovation Center. More details can be found at