Tom Hanika

Visiting Scientist (level W2) at University of Hildesheim
Research Associate (on leave) at University of Kassel


My research is concerned with the foundations and applications of explainable artificial intelligence. I am particularly interested in the (interactive) extraction of knowledge from explicit and implicit data by means of machine learning procedures. At the very heart of my work is the study of semantics arising from implicational theories in complex and large data. Of special interest in this context are lattice orders and ordinal-metric spaces. Problems in high dimensions are of special interest to me, as they represent a current challenge for the effectiveness of learning procedures. Apart from that I am very interested in unsupervised learning from large text corpora as well as applications of my methods in various research fields, such as Biology, Geography, Physics, Digital Humanities, and more.

Besides my work in research I am also a senior developer of the social publication sharing system BibSonomy as well as maintainer of the FCA-tool conexp-clj.

selected publications

  1. ordinal-motifs.png
    Johannes Hirth, Viktoria Horn, Gerd Stumme, Tom Hanika
    Ordinal motifs in lattices
    Information Sciences 659 (2024), 120009.
  2. divergent.png
    Katharina B. Budde, Christian Rellstab, Myriam Heuertz, Felix Gugerli, Tom Hanika, Miguel Verdú, Juli G. Pausas, Santiago C. González-Martínez
    Divergent selection in a Mediterranean pine on local spatial scales
    Journal of Ecology 112 (2024), no. 2, 278-290.
  3. tmlr.png
    Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider
    Intrinsic Dimension for Large-Scale Geometric Learning
    Transactions on Machine Learning Research (2023).
  4. topic-flow.png
    Bastian Schäfermeier, Johannes Hirth, Tom Hanika
    Research Topic Flows in Co-Authorship Networks
    Scientometrics 128 (2023), no. 9, 5051–5078.
  5. scaling-dim.png
    Bernhard Ganter, Tom Hanika, Johannes Hirth
    Scaling Dimension
    In: 17th ICFCA, Proceedings, D. Dürrschnabel, D. López-Rodríguez (eds.), 64–77. Springer (2023).
  6. tods.png
    Gerd Stumme, Dominik Dürrschnabel, Tom Hanika
    Towards Ordinal Data Science
    Transactions on Graph Data and Knowledge 1 (2023), no. 1, 6:1–6:39.
  7. id.png
    Tom Hanika, Friedrich Martin Schneider, Gerd Stumme
    Intrinsic dimension of geometric data sets
    Tohoku Mathematical Journal 74 (2022), no. 1, 23 – 52.
  8. concept-measurement.png
    Tom Hanika, Johannes Hirth
    On the lattice of conceptual measurements
    Information Sciences 613 (2022), 453-468.
  9. pacexp.png
    Daniel Borchmann, Tom Hanika, Sergei Obiedkov
    Probably approximately correct learning of Horn envelopes from queries
    Discrete Applied Mathematics 273 (2020), 30 - 42.