The first ever Probabilistic Numerics Spring School and Research Workshop will take place in Tübingen from 27th to 29th of March, 2023.

The school consists of two days of (in-person) lectures, keynotes, and tutorial sessions, from the 27th to the 28th of March. It will be held in English, and is aimed at graduate students, researchers, and professionals interested in probabilistic numerical methods. Prior experience with probabilistic numerical methods is not a prerequisite. The school features lectures and keynotes by leading experts, and hands-on code tutorials. This makes the school an opportunity to quickly get up to speed with these methods, including concrete practical experience.

The school will be followed by a workshop on the 29th of March, 2023. The workshop offers a stage for advances in the probabilistic computation, by researchers working in the field. Both events (school and workshop) will be held in conjunction. There is no need to register for either of them separately, and participants are invited to participate in both parts.

Q: Am I interested in probabilistic numerical methods?
A: You might be interested in probabilistic numerics if you are also keen to (learn about) topics such as: numerical analysis and scientific computing, uncertainty quantification, Bayesian inference, machine learning, Gaussian processes, or probabilistic programming. In particular, you may also be interested if you are a scientist working with simulation methods, or a machine learning researcher interested in scientific applications of AI, or in large-scale Bayesian inference. Find out more about probabilistic numerics here, on Wikipedia, or in the (very recent) book about the topic.

Q: What are the prerequisites?
A: You should be able to follow the tutorials with basic knowledge in linear algebra, probability theory, and numerical analysis. If you are interested, but feel you do not have the required background yet, take a look at Chapter I ("Mathematical Background") in the probabilistic numerics book. The tutorials will involve programming exercises, largely in the python-based ML stack.


Registration for the school is now open. Please register here.


The school and the workshop will be held in Tübingen. The exact location will be announced soon.


Philipp Hennig @PhilippHennig5

University of Tübingen ➔ Website

Philipp holds the Chair for the Methods of Machine Learning at the University of Tübingen. 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.

Nicholas Krämer @pnkraemer

University of Tübingen ➔ Website

Nicholas is an incoming Postdoc at Denmark's Technical University. He has been a PhD student at the University of Tübingen since September 2019. Prior to this, he was a student research assistant at the Institute for Numerical Simulation at the University of Bonn. He holds an MSc in Mathematics from the University of Bonn and a BSc in Mathematics in Business and Economics from the University of Mannheim. His research interests lie in probabilistic numerics, differential equations, and physics-informed machine learning.

For questions, please contact Nicholas Krämer ( nicholas.kraemer(at-symbol)uni-tuebingen.de ).