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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

Jul 2025. I speak at IACAP/AISB-25 and the MCMP workshop Formal Epistemology Meets Philosophy of AI.

Jun 2025. I moderate the evening lecture by Leah Henderson (Groningen), part of our CAS^LMU Research Focus on Bayesian Reasoning.

Jun 2025. Heather Champion (Rotman Institute, Western) visits us from Tübingen. Welcome, Heather!

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

May 2025. I give an invited talk in the Philosophisches Kolloquium at the University of Regensburg.

May 2025. Our CAS^LMU Research Focus on Bayesian Reasoning officially starts, with a workshop on How to Choose a Good Prior.

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.