I am a PhD student in machine learning at the University of Tübingen and the International Max Planck Research School for Intelligent Systems (IMPRS-IS), supervised by Prof. Dr. Philipp Hennig. I'm interested in probabilistic machine learning for and with dynamical systems, with a focus on probabilistic numerics: by treating numerical simulation as a probabilistic inference problem, we develop new methods that efficiently quantify their numerical error and enable new ways to do data-driven inference in dynamical systems.
I also make my research widely accessible in the form of open-source software: ProbNumDiffEq.jl provides efficient probabilistic numerical differential equation solvers in Julia that are compatible with the broader DifferentialEquations.jl ecosystem - and it contains nearly all the methods that I published in my PhD. I also contributed to the probnum probabilistic numerics toolkit and maintain a range of smaller Julia packages. Check out the code section!