I am a postdoctoral researcher in the Mathematics for Materials Modelling group at EPFL where I work on Bayesian optimization for inverse materials design, supervised by Prof. Michael F. Herbst. Before that, I completed my PhD in machine learning at the University of Tübingen and the IMPRS-IS under Prof. Philipp Hennig on probabilistic numerical methods for ordinary differential equations; see my PhD thesis "A Flexible and Efficient Framework for Probabilistic Numerical Simulation and Inference". I'm broadly interested in the intersection of probabilistic machine learning and scientific computing, including topics such as probabilistic numerics, Bayesian optimization, state-space models, differential equations, and Gaussian processes.
I like to 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 — it contains nearly all the methods that I developed during my PhD. I also contributed to the probnum probabilistic numerics toolkit and maintain a range of smaller Julia packages, such as TuePlots.jl for making publication-ready plots with Makie. Check out the code section!