Complex Network Mining on Digital and Physical Information Artefacts

In the world of today, a variety of interaction data of humans, services and systems is generated, e.g., utilizing sensors and social media. This enables the observation and capture of digital and physical information artefacts at various levels in offline and online scenarios.
Then, data science provides for the means of sophisticated analysis of the collected information artefacts and emerging structures.
Targeting that, this talk focuses on data mining on complex networks and graph structures and presents exemplary methods and results in the context of real-world systems. Specifically, we focus on the grounding and analysis of behavior, interactions and complex structures emerging from heterogeneous data, and according modeling approaches.

Biography

Martin Atzmueller is assistant professor at Tilburg University as well as visiting professor at the Université Sorbonne Paris Cité.
He earned his habilitation (Dr. habil.) in 2013 at the University of Kassel, where he also was appointed as adjunct professor (Privatdozent).
He received his Ph.D. (Dr. rer. nat.) in Computer Science from the University of Würzburg in 2006. He studied Computer Science at the University of Texas at Austin (USA) and at the University of Wuerzburg where he completed his MSc in Computer Science.

Martin Atzmueller conducts fundamental and applied research at the nexus of Data Science, Network Analysis, Ubiquitous Social Media, the Internet of Things, and Big Data. In particular, his research focuses on how to successfully analyze and design information and knowledge processes in complex ubiquitous and social environments. This is implemented by developing according methods and approaches for augmenting human intelligence and to assist the involved actors in all their purposes, both online and in the physical world.