Martin Atzmueller is Full Professor (W3, tenured) at Osnabrück University (Germany), where he heads the Semantic Information Systems research group, as well as Scientific Director at the German Research Center for Artificial Intelligence (DFKI), heading the research department Cooperative and Autonomous Systems.
In addition, Professor Atzmueller is founding spokesperson of the Joint Lab on Artificial Intelligence and Data Science and member of the Research Center Data Science at Osnabrück University. 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.

Research

Martin Atzmueller's research interests include Artificial Intelligence (AI), Data Science and Integrative AI Systems, where his research covers, in particular, complex data, explainable AI, interpretability, machine perception, as well as semantic modeling. A major focus lies on machine learning and analysis on complex (sensor) data such as images, graphs, networks, and temporal data, often encountered in complex systems, as well as the respective system view and design. This also relates to applications in complex integrative AI system domains, for example, to robot control and integrative sensor-based AI systems. Here, the goal is to develop according semantic perception-based information systems, which can act both interactively and autonomously, in particular also enabling explainable methods for trusted AI system design.

Selected Publications

• Stefan Bloemheuvel, Jurgen van den Hoogen, Dario Jozinovic, Alberto Michelini, and Martin Atzmueller. Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data. International Journal of Data Science and Analytics, 2022.

• Stefan Bloemheuvel, Jurgen van den Hoogen and Martin Atzmueller. A Computational Framework for Modeling Complex Sensor Network Data using Graph Signal Processing and Graph Neural Networks in Structural Health Monitoring. Applied Network Science, 6(1), 2021.

• Martin Atzmueller, Henry Soldano, Guillaume Santini, and Dominique Bouthinon. MinerLSD: Efficient Mining of Local Patterns on Attributed Networks. Applied Network Science, 4(43), 2019.

• 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, Stephan Doerfel, and Folke Mitzlaff. Description-Oriented Community Detection using Exhaustive Subgroup Discovery. Information Sciences, (329):965-984, 2016.

• 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.

• Florian Lemmerich, Martin Atzmueller, and Frank Puppe. Fast Exhaustive Subgroup Discovery with Numerical Target Concepts. Data Mining and Knowledge Discovery, (30):711-762, 2016.

• Martin Atzmueller. Data Mining on Social Interaction Networks. Journal of Data Mining and Digital Humanities, (1)June 2014.
  

• Folke Mitzlaff, Martin Atzmueller, Andreas Hotho, and Gerd Stumme. The Social Distributional Hypothesis. Journal of Social Network Analysis and Mining, (4)216, 2014.

• Martin Atzmueller, Joachim Baumeister, and Frank Puppe. Semi-Automatic Learning of Simple Diagnostic Scores utilizing Complexity Measures. Artificial Intelligence in Medicine, 37(1):19–30, 2006.

• 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.

Slides of Recent Tutorials

• WWW 2019: Modeling and Mining Feature-Rich Networks

• WWW 2018: Mining Attributed Networks

• DSAA 2017: Mining Attributed Networks

• Web Science 2016: Community Detection: From Plain to Attributed Complex Networks

• ASONAM 2015: Subgroup Discovery and Community Detection on Attributed Graphs

• CSSWS 2015: Subgroup and Community Analytics

Recent Projects/Activities

• FRED: Artificial Intelligence and Trusted Edge Computing for freshness data management - from Farm to Fork on the Edge (funded by BMWK: Federal Ministry for Economic Affairs and Climate Action).

• Resilient Athletes using Sensor-Based Social Analytics (funded by ZonMW).

• Di-Plast: Digital Circular Economy for the Plastics Industry using Data Analytics and Internet of Things (funded by Interreg NWE).

• MODUS: Model-based Anomaly Pattern Detection and Analysis in Ubiquitous and Social Interaction Networks (funded by DFG).

• 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).

• FEE: Early detection and decision support in critical situations in production environments. (funded by BMBF).

• Conferator: A system platform for enhancing smart ubiquitous and social interactions.

• EveryAware (FP7, completed, but research continues in the Ubicon initiative)

Frameworks and Tools

• SD4Py Python package for subgroup discovery.

• rsubgroup R package for subgroup discovery.

• VIKAMINE
  

  • 🔗Martin Atzmueller and Florian Lemmerich. VIKAMINE - Open-Source Subgroup Discovery, Pattern Mining, and Analytics. Proc. ECML PKDD 2012, Springer, 2012

  • 🔗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