Research group: Mathematics of Data Science

The Research group "Mathematics of Data Science" works at the interface of data science, machine learning, inverse problems and image processing. Within these fields, our research is characterized by a close connection of the development and analysis of mathematical models in function space with concrete, interdisciplinary applications.

Our main directions of research are variational methods for dynamic and multi-channel inverse problems as well as generative models in machine learning. The contributions of our work are both of mathematical nature, comprising novel mathematical models in function space, and strongly application-driven, comprising the implementation and publication of parallelized algorithms and software tools for dealing with real-world data.

Examples of our practical contributions so far are mathematical models and corresponding algorithms for dynamic magnetic-resonance imaging, positron emission tomography, image- and video decompression and electron tomography.

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