Further Information

I am Emmy Noether junior group leader of the project From Bias to Knowledge: The Epistemology of Machine Learning. This project builds on my earlier German Science Foundation-funded project on The Epistemology of Statistical Learning Theory. Before coming to the MCMP, I did my PhD at the Department of Theoretical Philosophy of the University of Groningen and the Dutch national research center for mathematics and computer science (CWI Amsterdam). My PhD thesis on universal prediction received the Wolfgang-Stegmüller-Award of the German Society for Analytic Philosophy.

Research Interests

My research is in the philosophy of induction and the epistemological foundations of machine learning. I am in particular interested in applying the mathematical field of machine learning theory to philosophical questions around machine learning and artificial intelligence.

Selected Publications

  • On explaining the success of induction, The British Journal for the Philosophy of Science, forthcoming.
  • Peirce, pedigree, Probability. Transactions of the Charles S. Peirce Society, 2022. With Rush Stewart.
  • On characterization of learnability with computable learners. Conference on Learning Theory (COLT), 2022.
  • On the truth-convergence of open-minded Bayesianism, The Review of Symbolic Logic, 2022. With Rianne de Heide.
  • The no-free-lunch theorems of supervised learning, Synthese, 2021. With Peter Grünwald.
  • The meta-inductive justification of induction, Episteme, 2020.
  • The meta-inductive justification of induction: The pool of strategies, Philosophy of Science, 2019.
  • Putnam’s diagonal argument and the impossibility of a universal learning machine, Erkenntnis, 2019.
  • Solomonoff prediction and Occam’s razor, Philosophy of Science, 2016.