I am a postdoctoral fellow at the Munich Center for Mathematical Philosophy (MCMP, LMU Munich). Starting April 2020 I will be working on my Deutsche Forschungsgemeinschaft-funded project on "The Epistemology of Statistical Learning Theory".
Contact me at tom.sterkenburglmu.de.
Research interestsPhilosophy of induction, inductive logic, Bayesian epistemology;
foundations of statistics and machine learning, philosophy of probability and randomness;
algorithmic information theory, algorithmic randomness, computability theory.
My PhD project was on the foundations of machine learning. More specifically, I investigated the computability-theoretic approach to probabilistic "universal prediction" (initiated by Solomonoff) as a link between Carnap's project of inductive logic and modern approaches in machine learning.
Picture by Kasper Vogelzang.
I did my PhD at both the Centrum Wiskunde & Informatica (CWI, the national research institute for mathematics and computer science in the Netherlands) and the Faculty of Philosophy of the University of Groningen, under supervision of Peter Grünwald and Jan-Willem Romeijn, respectively. This work resulted in my PhD dissertation on Universal prediction, that won me the Wolfgang Stegmüller Award.
From February till May 2017 I visited the Center for Formal Epistemology at Carnegie Mellon University, Pittsburgh, which was facilitated by the Leverhulme Trust-funded network The Scientific Approach to Epistemology.
I hold an MA and PhD in Philosophy (University of Groningen, cum laude), 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 intend to finally obtain my driver's license soon.
In February 2014 I talked about my PhD project on Swammerdam, the science show of the local radio station Amsterdam FM (in Dutch).