Fiona Katharina Ewald

About

Since October 2022, I have been working on my Ph.D. in Interpretable Machine Learning at the Ludwig-Maximilians-Universität in Munich.

Previously, I completed my Bachelor’s in Business Mathematics (B.Sc.) and my Master’s in Economics majoring in Statistics (M.Sc.) at the University of Duisburg-Essen.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

fiona.ewald [at] stat.uni-muenchen.de

Research Interests

• Interpretable Machine Learning

You can find me on

• Google Scholar
• GitHub
• LinkedIn

References

1. Ewald FK, Bothmann L, Wright MN, Bischl B, Casalicchio G, König G (2024) A Guide to Feature Importance Methods for Scientific Inference. In: In: Longo L , In: Lapuschkin C Sebastianand Seifert (eds) Explainable Artificial Intelligence, pp. 440–464. Springer Nature Switzerland, Cham.
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2. Herbinger J, Dandl S, Ewald FK, Loibl S, Casalicchio G (2024) Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation. In: In: Nowaczyk S , In: Biecek P , In: Chung NC , In: Vallati M , In: Skruch P , In: Jaworek-Korjakowska J , In: Parkinson S , In: Nikitas A , In: Atzmüller M , In: Kliegr T , In: Schmid U , In: Bobek S , In: Lavrac N , In: Peeters M , In: Dierendonck R van , In: Robben S , In: Mercier-Laurent E , In: Kayakutlu G , In: Owoc ML , In: Mason K , In: Wahid A , In: Bruno P , In: Calimeri F , In: Cauteruccio F , In: Terracina G , In: Wolter D , In: Leidner JL , In: Kohlhase M , In: Dimitrova V (eds) Artificial Intelligence. ECAI 2023 International Workshops, pp. 232–249. Springer Nature Switzerland.
   link|pdf.