Wilhelmshöher Allee 73
Tel.: +49 561 804-6298
Fax.: +49 561 804-6259
- 1.Stubbemann, M., Hanika, T., Stumme, G.: Orometric Methods in Bounded Metric Data. In: Berthold, M.R., Feelders, A., and Krempl, G. (eds.) Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27-29, 2020, Proceedings. pp. 496–508. Springer (2020).
- Subreviewer: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 19-23 2022
- Subreviewer: 20th International Semantic Web Conference, October 24-28 2021, Virtual Conference
- Subreviewer: 18th Extended Semantic Web Conference, June 6-10 2021, Hersonissos, Greece
- Subreviewer: 19th International Semantic Web Conference, November 1-6 2020, Virtual Conference
- Subreviewer: 25th International Conference on Conceptual Structures, September 18-21 2020, Bolzano, Italy
- Subreviewer: 24th European Conference on Artificial Intelligence, June 8-12 2020, Santiago de Compostela, Spain
- Subreviewer: 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 4 – 8, 2019, Anchorage, Alaska – USA
- Subreviewer: 15th International Conference on Formal Concept Analysis, June 25-28 2019 – Frankfurt, Germany
Programming in Clojure
Together with Johannes Hirth, I developed a Clojure programming curse which first took place in Summer 2019. The course is hold until today on a regular basis.
REGIO was a joint project between the University of Kassel, the L3S Research Center Hannover, the HU Berlin and the University of Würzburg. Together, we established new methodologies and data sources which helped to better understand the impact of geographic and thematic proximity on the genesis and the success of interaction in science and R&D. Our findings indicate, that social and thematic proximity are the key factors for cooperation.
This project is led by Dr. Tom Hanika within the Loewe Exploration program. In this project, we investigate to which extent machine learning is influenced by the intrinsic dimensionality of data. For this, we quantify the Curse of Dimensionality which is strongly connected to the phenomenon of measure concentration. Within the project, we develop methods that allow to efficiently compute the intrinsic dimension of modern large-scale datasets.