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Beyond Independence: Efficient Learning Techniques for Networks and Temporal Data

Prof. Dr. Stephan Günnemann, Technical University of Munich

Date: 28-Nov-2016 , 2pm

Location: DFKI Projektbüro Berlin, 4th Floor, Room: Weizenbaum, Alt-Moabit 91 C, 10559 Berlin

 

Abstract

Going beyond independence, most of the data gathered in today‘s applications show complex dependency structures: people, for example, interact with each other in social networks; similarly, sensors in a cyber-physical system continuously measure dependent signals over time. In general, networks and temporal data are the most frequently observed examples for such complex data. In this talk, I will focus on two data mining tasks that operate in these domains: (i) Classification in (partially) labeled networks, and (ii) anomaly detection for temporal rating data. For both tasks I will present the underlying modeling principles, I will sketch solutions how to derive efficient learning algorithms, and I will showcase their applications in different scenarios. The talk concludes with a summary of further research our group is working on.

Bio

Stephan Günnemann is a Professor at the Department of Informatics, Technical University of Munich. He acquired his doctoral degree in 2012 at RWTH Aachen University in the field of computer science. From 2012 to 2015 he was an associate of Carnegie Mellon University, USA; initially as a postdoctoral fellow and later as a senior researcher. Stephan Günnemann has been a visiting researcher at Simon Fraser University, Canada, and a research scientist at the Research & Technology Center of Siemens AG. His research interests include efficient data mining and machine learning techniques for high-dimensional, temporal, and network data.

 

Location: DFKI Projektbüro Berlin, 4th Floor, Room: Weizenbaum, Alt-Moabit 91 C, 10559 Berlin