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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 associated with the Munich Center for Machine Learning (MCML), and I am a fellow of the Konrad Zuse School of Excellence in Reliable AI (relAI). I am further a member of the Young Center of the LMU Center for Advanced Studies (CAS^LMU), where I co-lead a research focus on Bayesian reasoning in science. I am also associate editor of the European Journal for Philosophy of Science.

My work is about 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.

For more on philosophy of machine learning at the MCMP, including our reading group and our teaching, see here.

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

Mar 2026. My commentary paper with David Watson on Floridi's conjecture has been accepted for publication in Philosophy & Technology.

Apr 2026. In the 2026 summer semester Timo Freiesleben and I teach a master seminar on Algorithmic Fairness, which is also part of the relAI professional development curriculum. I also organize, together with Timo and Ignacio Ojea Quintana, a new round of the MCMP reading group on philosophy of machine learning.

Mar 2026. The save-the-date is out for the PhilML 2026 conference, to be held in October in Munich.

Mar 2026. My paper on values in machine learning has been accepted for publication in AI and Ethics. Further, a first public version is available of my chapter on Solomonoff induction.

Formerly current affairs...

Previously

In my DFG-funded Eigene Stelle project The Epistemology of Statistical Learning Theory (2020-2023), I explored epistemological aspects 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 (supervisor: Peter Grünwald) and the Faculty of Philosophy of the University of Groningen (supervisor: Jan-Willem Romeijn), 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.