User:Hans Ulrich Schneider/Stefan Wrobel

Stefan Wrobel is a German computer scientist, professor at the University of Bonn and director of the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS.

Biografie
Wrobel studied computer science in Bonn and Atlanta, Georgia, USA (M.S., Georgia Institute of Technology) with a focus on artificial intelligence and received his doctorate from the University of Dortmund. After holding positions in Berlin and Sankt Augustin, he became Professor of Computer Science at the University of Magdeburg before accepting the position of Professor of Computer Science at the Rheinische Friedrich-Wilhelms-Universität Bonn in 2002.

Since 2007, Wrobel has also been Managing Director of the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS. He is also Managing Director of the Bonn-Aachen International Center for Information Technology (b-it) and one of the two spokespersons for the Rhine-Ruhr Machine Learning Competence Center (ML2R).

Wrobel deals with aspects of digitalization, in particular with intelligent algorithms and systems for the analysis of large amounts of data and the influence of big data/smart data on the use of information in companies and society. He is the author of publications in the fields of data mining and machine learning, a member of the editorial board of several specialist journals and a founding member of the International Machine Learning Society.

As spokesman for the "Fraunhofer Big Data and Artificial Intelligence Alliance", Director of the "Fraunhofer Research Center for Machine Learning", Deputy Chairman of the "Fraunhofer Group for Information and Communication Technology IUK" and spokesman for the "Knowledge Discovery, Data Mining and Machine Learning" section of the German Informatics Society, he is committed to the topics of digitalization, intelligent use of big data and artificial intelligence.

Awards
In 2019, Stefan Wrobel was honored by the German Informatics Society as one of ten influential personalities in the history of German AI.

Publications (selection)

 * Pascal Welke, Tamas Horvath, Stefan Wrobel: Probabilistic Frequent Subtree Kernels. In: M. Ceci u. a. (Hrsg.): Post-Proceedings of the International Workshop on New Frontiers in Mining Complex Patterns. Lecture Notes in Artificial Intelligence, Springer Verlag, 2016.
 * Pascal Welke, Tamas Horvath, Stefan Wrobel: Min-Hashing for Probabilistic Frequent Subtree Feature Spaces. In: Discovery Science. 2016, S. 67–82.
 * Rajkumar Ramamurthy, Christian Bauckhage, Krisztian Buza, Stefan Wrobel: Using Echo State Networks for Cryptography. In: Proc. 26th International Conference on Artificial Neural Networks. Lecture Notes in Computer Science 10614, Springer Verlag, 2017, S. 663–671.
 * Katrin Ullrich, Michael Kamp, Thomas Gärtner, Martin Vogt, Stefan Wrobel: Co-Regularised Support Vector Regression. In: Machine Learning and Knowledge Discovery in Databases, Proc. ECML-PKD 2017. Part II, Lecture Notes in Artificial Intelligence Series. Springer-Verlag, Berlin, 2017, S. 338–354.
 * Daniel Trabold, Tamas Horvath, Stefan Wrobel: Mining Strongly Closed Itemsets from Data Streams. In: Special Issue on Discovery Science of Machine Learning Journal. 2017, S. 251–266.
 * Pascal Welke, Tamas Horvath, Stefan Wrobel: Probabilistic Frequent Subtrees for Efficient Graph Classification and Retrieval. In: Machine Learning. Volume 107, Issue 11, 2018, S. 1847–1873.
 * Till Hendrik Schulz, Tamas Horvath, Pascal Welke, Stefan Wrobel: Mining Tree Patterns with Partially Injective Homomorphisms. In: Proceedings ECML-PKDD 2018. S. 585–601.
 * Pascal Welke, Tamas Horvath, Stefan Wrobel: Probabilistic and Exact Frequent Subtree Mining in Graphs Beyond Forests. In: Machine Learning. Volume 108, Issue 7, 2019, S. 1137–1164.
 * Florian Seiffarth, Tamas Horvath, Stefan Wrobel: Maximal Closed Set and Half-Space Separations in Finite Closure Systems. In: Proceedings ECML-PKDD 2019. S. 21–37.
 * Natalia Andrienko, Gennady Andrienko, Silvia Miksch, Heidrun Schumann, Stefan Wrobel: A theoretical model for pattern discovery in visual analytics. In: Visual Informatics. 2020, in press (available online).