Univ.-Prof. Dr. rer. nat. habil. Martin Atzmüller
Institute of Computer Science
Martin Atzmueller is Full Professor (W3, tenured) at the Institute of Computer Science at Osnabrück University (Germany), where he holds the ROSEN-Group-Endowed Chair of Semantic Information Systems and heads the Semantic Information Systems research group.
Professor Atzmueller is founding member of the Research Unit Data Science at Osnabrück University, and an Affiliated Professor at the German Research Center for Artificial Intelligence (DFKI). Previously, he also held appointments at Tilburg University (The Netherlands) as an Associate Professor, and at the Université Sorbonne Paris Nord (France) as a Visiting Professor.
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 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 Würzburg where he completed his MSc (Diplom-Informatiker Univ.) in Computer Science.
Martin Atzmueller's research interests include Artificial Intelligence, Knowledge Discovery, Machine Learning, Network Science and Pattern Mining, also with a human-centered data science and system design perspective. His work focuses on how to 'make sense' of complex information and knowledge processes - leveraging the massive amounts of data collected in science and industry by intelligent analytics and semantic interpretation. For instance, this includes the identification of interesting/exceptional patterns and structures, predictive modeling, analysis and exploration of complex heterogeneous and multi-modal data, as well as human-centered 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.
Lukas Eberhard, Christoph Trattner, and Martin Atzmueller. Predicting Trading Interactions in an Online Marketplace through Location-Based and Online Social Networks. Information Retrieval Journal, 22(2), 2019.
Martin Atzmueller, Benjamin Kloepper, Hassan Al Mawla, Benjamin Jäschke, Martin Hollender, Markus Graube, David Arnu, Andreas Schmidt, Sebastian Heinze, Lukas Schorer, Andreas Kroll, Gerd Stumme, and Leon Urbas. Big Data Analytics for Proactive Industrial Decision Support. atp edition, (58)9 2016.
Christoph Scholz, Martin Atzmueller, Mark Kibanov, and Gerd Stumme. Predictability of Evolving Contacts and Triadic Closure in Human Face-to-Face Proximity Networks. Journal of Social Network Analysis and Mining, (4)217, 2014.
Martin Atzmueller and Thomas Roth-Berghofer. The Mining and Analysis Continuum of Explaining Uncovered. In Research and Development in Intelligent Systems XXVII, Proc. International Conference on Artificial Intelligence (SGAI), 2010.
Martin Atzmueller, Frank Puppe, and Hans-Peter Buscher. Exploiting Background Knowledge for Knowledge-Intensive Subgroup Discovery. In Proc. International Joint Conference on Artificial Intelligence (IJCAI), pages 647–652, Edinburgh, Scotland, 2005.
WWW 2018: Mining Attributed Networks
DSAA 2017: Mining Attributed Networks
Web Science 2016: Community Detection: From Plain to Attributed Complex Networks
CSSWS 2015: Subgroup and Community Analytics
ODYN: Observing Team Dynamics and Communication using Sensor-Based Social Analytics (funded by NWO).
HIHAT: Building high performing and happy teams using sensor-based social analytics (funded by NWO).
🔗 Martin Atzmueller, Martin Becker, Mark Kibanov, Christoph Scholz, Stephan Doerfel, Andreas Hotho, Bjoern-Elmar Macek, Folke Mitzlaff, Juergen Mueller, and Gerd Stumme. Ubicon and its Applications for Ubiquitous Social Computing. New Review of Hypermedia and Multimedia, (20)1:53--77, 2014.
🔗Martin Atzmueller and Frank Puppe. Semi-Automatic Visual Subgroup Mining using VIKAMINE. Journal of Universal Computer Science (JUCS), Special Issue on Visual Data Mining, 11 (11), pp. 1752-1765, 2005