Kirsten Köbschall, M. Sc.


Johannes Gutenberg University Mainz
Institut für Informatik
Staudingerweg 9
55128 Mainz, Germany

E-Mail:
Room: 03-623

Research Interests

  • Online learning, data streams
  • Resource-awareness
  • Transparency of AI models
  • Stochastic methods
  • Deep neural networks

 

Short Scientific CV

since 07/2022         Research associate at Johannes Gutenberg University, Mainz.
09/2024-12/2024    Guest research associate at Lamarr Institute, TU Dortmund, Germany.
06/2019 - 11/2021  Computer Science (M. Sc.) at Johannes Gutenberg University Mainz, Germany.
01/2019 - 06/2019  Semester abroad at Universitat de València, Spain.
10/2015 - 03/2019  Computer Science (B. Sc.) at Johannes Gutenberg University Mainz, Germany.

 

Teaching

  • Supervision and organization in "Machine Learning Seminar: Real-Time Insights from Streaming Data, Time Series, and Forecasting Models" (WS 25/26)
  • Co-Supervision in Data Mining and Machine Learning seminar (WS 22/23, SS 23, WS 23/24, SS 24, WS24/25, SS 25)
  • Workshop "KI als Chance oder Risiko?" (SS 24)
  • Tutor in Computability and Formal Languages (SS 21)
  • Tutor in Complexity Theory (WS 19/20, WS 20/21)

Presentation and Poster

  • Paper "Transparent and Adaptive Pruning of Hoeffding Trees" presented (Talk) at 2nd TempXAI Workshop for Explainable AI in Time Series and Data Streams, ECML-PKDD 2025, Porto, Portugal, 15-19 September 2025.
  • Talk "Balancing Prediction Performance, Transparency and Energy Consumption in Machine Learning Models for Data Streams" at Fast Machine Learning for Science Conference 2025, ETH Zurich, Switzerland, 1-5 September 2025.
  • Poster "Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams" at On Boarding-Meeting Trading Off Non-Functional Properties of Machine Learning (TOPML), 29 October 2024.
  • Paper "Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams" presented (Talk) at Discovery Science 2024, Pisa, Italy, 14-16 October 2024
  • "Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams" presented (Talk and Poster) at 1st Mainz and Friends Artificial Intelligence Conference (MAInC), Mainz, Germany, 13-14 June 2023.
  • "Soft Hoeffding Tree: A Transparent and Differentiable Model for Data Streams" presented (Talk and Poster) at Trading Off Non-Functional Properties of Machine Learning (TOPML) Workshop, 3 February 2023.

Publications

2025

Kobschall, K., Hartung, L., and Kramer, S. (2025). Adaptive differentiable trees for transparent learning on data streams. MACHINE LEARNING, 114(11). DOI Author/Publisher URL
Koebschall, K., Hartung, L., and Kramer, S. (2025). Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams (Vols 15243, pp. 167-182). DOI Author/Publisher URL

 

Further Links

Link to my Github profile.

Link to my LinkedIn profile.

Link to my Google Scholar profile.