Philipp Hennig holds the Chair for the Methods of Machine Learning. He studied Physics in Heidelberg, Germany and at Imperial College, London, before moving to the University of Cambridge, UK, where he attained a PhD in the group of Sir David JC MacKay with research on machine learning. Since this time, he is interested in connections between computation and inference. With international collaborators, he helped establish the field of probabilistic numerics. In 2022, Cambridge University Press published his textbook on the subject, Probabilistic Numerics — Computation as Machine Learning.
Hennig's research was supported, among others, by the Emmy Noether Programme of the German Research Union (DFG), an independent Research Group of the Max Planck Society, and Starting and Consolidator grants of the European Commission.
Since July 2025, Hennig directs the Tübingen AI Center (with Matthias Bethge). He is a founding Fellow and Co-Director of the ELLIS Program on Theory, Algorithms and Computations of Modern Learning Systems (with Francis Bach and Lorenzo Rosasco). He is also a member of the Steering Committees of the Cluster of Excellence for Machine Learning in Science (more information on the Cluster's Blog). From October 2022 to October 2025, he served as the Dean of Studies for the Department of Computer Science.
An overview of research and teaching activities as well as a list of publications can be found on the corresponding webpages of the Chair, and at Google Scholar.
