I am a PhD students in the Emmy-Noether-Group on Probabilistic Numerics at the Empirical Inference department. Together with my supervisor Philipp Hennig and my colleagues I try to develop algorithms which return information about the uncertainty of the result. This uncertainty is mathematically captured by probability measures.
I am especially interested in solving ordinary differential equations (ODEs). My main goal for my PhD is to bridge the trade-off between computational cost and probabilistic calibration of the estimated uncertainty. Therefore I aim at developing solvers that return meaningful probabilistic information while staying fast. Since ODEs are solved in many fields of science and engineering, I am always interested in interdisciplinary cooperations. Additionally, I would like to learn as much as possible about algorithms based on Gaussian processes or Bayesian statistics. In machine learning these algorithms are often connected in a series and I believe that there are still many open questions about the interplay between the different computational steps (e.g. about the propagation of error estimates). I hope to add insights about these overarching questions from my ODE perspective.
Before joining this group, I studied mathematics at LMU Munich with a focus on stochastic processes, stochastic calculus, PDEs and functional analysis.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems