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I am Emmy Noether junior group leader at the Munich Center for Mathematical Philosophy (MCMP, LMU Munich). Me and my group are also associated with the Munich Center for Machine Leanring (MCML). I am further a fellow of the Konrad Zuse School of Excellence in Reliable AI (relAI) and a member of the Young Center of the LMU Center for Advanced Studies (CAS^LMU).

I am interested in applying the mathematical field of machine learning theory to philosophical questions around machine learning and artificial intelligence. My Emmy Noether project From Bias to Knowledge: The Epistemology of Machine Learning is concerned with the fundamental notion of inductive bias—the assumptions that allow a learning algorithm to learn. This project builds on my earlier German Science Foundation-funded project on the The Epistemology of Statistical Learning Theory.

I think climate change is a thing, and I don't see why we all couldn't try to make an effort. So I avoid flying, and aim to only travel to places I can reach by train.

Contact me at tom.sterkenburglmu.de.

Current affairs

May 2025. My group organizes the second Epistemology and Theory of Machine Learning workshop.

May 2025. I help organize and speak at a workshop on How to Choose a Good Prior within our CAS^LMU Research Focus "Bayesian Methods in Science."

Apr 2025. In the 2025 summer semester I teach my master seminar on Philosophy of Statistics, which is also part of the relAI professional development curriculum. I further organize, together with Timo Freiesleben, a new round of the MCMP reading group on philosophy of machine learning.

Formerly current affairs...

Previously

In my DFG-funded Eigene Stelle project The Epistemology of Statistical Learning Theory (2020-2023), I explored the epistemological import of statistical learning theory, the standard theoretical framework for modern machine learning methods. I also proved new results in the recently proposed setting of computable PAC learning.

As a postdoctoral fellow at the MCMP (2017-2020), I investigated the meta-inductive justification of induction that is based on the machine learning theory of online prediction. I further worked on a Bayesian confirmation theory that can deal with newly formulated hypotheses, that also drew from results in online prediction.

In my PhD project (2013-2018, cum laude), at the CWI and the Faculty of Philosophy of the University of Groningen, I investigated the theory of universal prediction stemming from algorithmic information theory (Kolmogorov complexity). My PhD dissertation on Universal Prediction won me the Wolfgang Stegmüller Award.

I hold a MSc in Logic (Institute for Logic, Language and Computation, University of Amsterdam, cum laude), a MSc in History and Philosophy of Science (Descartes Centre, Utrecht University, cum laude), and a BSc in Artificial Intelligence (VU University Amsterdam, cum laude). I have never managed to obtain my driver's license, but I hope to attain Deutsche Bahn Statuslevel Gold Platin soon.