What an event!

The Probabilistic Numerics Spring School 2023 officially over. A huge thank you to all participants and speakers who made this event such a success.


The lectures have been recorded and uploaded to youtube (link). The solutions discussed in the tutorials as well as the slides from the lectures and talks have been uploaded here.


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 closed.


The school and the workshop will be held at the "Audimax" Lecture Hall at "Neue Aula":

Audimax - Universität Tübingen
1. OG, Neue Aula
72074 Tübingen

The dinner will take place on Tuesday evening (18:30) at Brauwerk Freistil Tübingen.

Practical sessions

The ProbNum Spring School includes practical sessions in which we implement some probabilistic numerical algorithms (in Python). You can find all instructions for the practical sessions here. Please have a careful look at the instructions and make sure that you have everything set up and ready to go before the first session on Monday afternoon (for example, that you have successfully installed all dependencies).

Food, drinks and activities in Tübingen

If you are wondering what to do on Monday evening: PhD students from Philipp Hennig's group have kindly compiled a list of suggestions for where to get food and/or drinks in Tübingen.

You can find the recommendations here.


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

Technical University of Denmark ➔ 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.

Franziska Weiler

University of Tübingen

Franzi studied multilingual management and started to work at the University of Tübingen in March 2017. Her first two jobs were at the Central Administration. She has been working part time for the Hennig lab since July 2018 and is responsible for all kinds of things, including the admin part of the ProbNum Spring School 2023.

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

Funded by the ''Cluster of Excellence - Machine Learning for Science'' and the Tübingen AI Center