Talk by Martin Atzmueller
Martin Atzmueller, Associate Professor at the Department of Cognitive Science and Artificial Intelligence at Tilburg University, will give a talk at DIKU.
Onto Model-Based Exceptional Pattern Mining on Attributed Interaction Networks
The detection of anomalies and exceptional patterns in attributed interaction networks is a prominent research direction in data mining and network science. Typically two questions need to be addressed: (1) What is an exceptional/anomalous pattern ? (2) How do we identify that
This talk presents model-based approaches and methods for addressing and formalizing these issues in the context of attributed interaction networks, and exemplifies promising directions for its implementation.
Martin Atzmueller is Associate Professor at the Department of Cognitive Science and Artificial Intelligence at Tilburg University, where he heads the Computational Sensemaking Lab. Furthermore, he is 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). Further, he received his PhD (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 Würzburg where he completed his MSc in Computer Science.
Martin Atzmueller's research interests include Data Science, Artificial Intelligence, Social Sensing, Human Computing and Network Science. His work focuses on how to 'make sense' of complex data and information processes in science and industry by designing and developing approaches, methods and tools for interactive data science and intelligent analytics, leading to computational sensemaking. For instance, this includes the identification of interesting local patterns (e.g., complex structures, exceptional subgroups, and anomalies), the modeling, analysis and exploration of complex heterogeneous and multi-modal data, as well as human-machine learning and decision support. By connecting computational approaches with the human cognitive, behavioral, and social contextual perspectives - thus linking technologies with their users - the goal is to augment human intelligence and to assist human actors in all their purposes, both online and in the physical world.