Before I joined the mlcolab in July 2021, my path has taken me from physics over international policy making and environmental sciences to probabilistic machine learning.

My educational background is in physics, which I studied at LMU in Munich and McGill University in Montréal. I concluded my MSc with a project on simulating core-collapse supernovae at the Max Planck Institute for Astrophysics. After intermediate projects in the environmental sector, I moved to Tübingen to pursue my PhD in the (at the time) probabilistic numerics group lead by Philipp Hennig at the MPI for Intelligent Systems— nowadays known as the Methods for Machine Learning group at the University of Tübingen. I got excited about probabilistic inference and uncertainty quantification due to their wide applicability across scientific applications and am happy to join the interdisciplinary efforts of the ML Cluster to make machine learning techniques accessible to scientists across a wide range of disciplines.

If not at the office, you are likely to encounter me on a bike: I enjoy cycling—the further the better. Also I have been teaching maths camps in Togo with SAMI.