Maximilian Stubbemann

Raum 0445A
Universität Kassel
Fachbereich Elektrotechnik/Informatik
Fachgebiet Wissensverarbeitung
Wilhelmshöher Allee 73
34121 Kassel
Tel.: +49 561 804-6298
Fax.: +49 561 804-6259


  • Stubbemann, M., Hille, T., Hanika, T.: Selecting Features by their Resilience to the Curse of Dimensionality (2023).
  • Stubbemann, M., Hanika, T., Schneider, F.M.: Intrinsic Dimension for Large-Scale Geometric Learning Transactions on Machine Learning Research. (2023).
  • Stubbemann, M., Stumme, G.: LG4AV: Combining Language Models and Graph Neural Networks for Author Verification In: Bouadi, T., Fromont, E., and H{ü}llermeier, E. (eds.) Advances in Intelligent Data Analysis XX. pp. 315–326. Springer International Publishing, Cham (2022).
  • Koopmann, T., Stubbemann, M., Kapa, M., Paris, M., Buenstorf, G., Hanika, T., Hotho, A., Jäschke, R., Stumme, G.: Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research Scientometrics. (2021).
  • Stubbemann, M., Stumme, G.: The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks arXiv preprint arXiv:2110.13774. (2021).
  • 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).
  • Dürrschnabel, D., Hanika, T., Stubbemann, M.: FCA2VEC: Embedding Techniques for Formal Concept Analysis,, (2019).


  • January 2023: Topological Data Analysis and Neural Networks, Dagstuhl Workshop on Concept Lattice Based Topological Data Analysis and Reasoning
  • April 2022: LG4AV: Combining Language Models and Graph Neural Networks for Author Verification, Symposium on Intelligent Data Analysis 2022
  • July 2021: Dimensionen von Nähe und ihr Einfluss auf die Entstehung von Kollaboration, ITeG Brown Bag Seminar
  • April 2020: Orometric Methods in Bounded Metric Data, Symposium on Intelligent Data Analysis 2020

Extracurricular Activities

  • Podcast of the University of Kassel: How does ChatGpt work? (In German)
  • Participation in a radio report on ChatGPT on the Hessian radio station HR4, broadcast on 23.03.2023 at 11:15 a.m.
  • 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.


  • 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 


REGIO (2018-2021)

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.

Dimension Curse Detector (2022-)

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.


Wintersemester 2022

Sommersemester 2021

Wintersemester 2021

Sommersemester 2020

Winter 2019/20

Summer 2019

Winter 2018/2019