Philipp Hennig holds the Chair for the Methods of Machine Learning, and is an adjunct scientist at the Max Planck Institute for Intelligent Systems. 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 a Starting Grant of the European Commission.
Hennig is co-speaker of the Cyber Valley Initiative (with Michael Black and Thomas Kropf); Co-Director of the ELLIS Program on Theory, Algorithms and Computations of Modern Learning Systems (with Francis Bach and Lorenzo Rosasco), and Member of the Center of Excellence for Machine Learning in Science. He is a Co-PI of the IMPRS for Intelligent Systems and the Competence Center for Machine Learning in Tübingen. In 2019, he received the annual Award for Excellence in Teaching of the Union of CS Students.
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.