My research interests lie at the interface of mathematical sciences and imaging. I study non-smooth regularisation approaches, convex optimisation methods and deep learning techniques for image analysis and inverse problems, among them image reconstruction, for static and dynamic targets, image classification and segmentation. I have been working on both the rigorous theoretical and computational analysis of the problems considered as well as their practical implementation and their use for real-world applications. My research over the years included interdisciplinary collaborations with physicists working on nuclear weapons safeguards, biologist experts on plant physiology and clinicians for medical projects.

What I am currently excited most about is my work on data-driven inversion and statistical inverse learning problems. This is giving me the possibility to work on mathematical aspect of machine learning, a very active area of research where sound contributions are much needed.