Danielle studied Computational Life Science at the University of Lübeck. Her main area of interest are functional lifting approaches which allow to reformulate non-convex variational problems as convex problems in another space. Furthermore, she is interested in non-linear scale space methods, non-smooth imaging problems and coordinate-based neural networks.
At the Eighth International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) she won the Best Student Paper Prize for the paper ”Inverse Scale Space Iterations for Non-Convex Variational Problems Using Functional Lifting”.
|Research assistant at DESY
|PhD student at University of Lübeck
|Master’s degree (with honours), Computational Life Science, University of Lübeck
|Bachelor’s degree, Computational Life Science, University of Lübeck