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My current research focuses on deep Gaussian processes and how to apply them in Bayesian inverse problems. Such inverse problems arise, e. g., in image reconstruction. I am mostly concerned with the computational side, exploring ways to sample a large number of GPs that depend on each other in an efficient manner. For my work I need Python with SciPy and FEniCS, Ubuntu via the WSL, and a lot of tea.

Master’s Thesis and First Paper

My master’s thesis at TU Munich was supervised by Dr Brendan Keith and Prof Barbara Wohlmuth. During that time, I worked on an algorithm that minimises the strain energy in a given shape under a load and uncertainty. Working on my thesis exposed me to a large number of topics: stochastic optimisation, adaptive sampling, risk measures, shape optimisation, finite elements, linear elasticity, sequential quadratic programming, …
The main question it all boils down to is how many samples one needs at each iteration in an SGD-type algorithm (hence “adaptive sampling”). I handed in my master’s thesis in 2020 and stayed on for a few more months to contribute to a paper on adaptive sampling under constraints. The paper has now been published (open access!) and can be found here: