Hannah Laus

Hannah Laus is a Researcher at the Department of Mathematics within the School of Computation, Information and Technology at the Technical University of Munich. Her work focuses on advanced data science methodologies, particularly in uncertainty quantification, high-dimensional learning, and optimization algorithms. She contributes to research groups including Data Science, Probability Theory, and Numerical Analysis.

Her research interests revolve around addressing challenges in inverse problems, machine learning, and statistical learning theory. Notable contributions include studies on uncertainty quantification in undersampled medical imaging and convergence guarantees for deep learning algorithms.

Recent publications emphasize interdisciplinary applications of mathematical techniques in high-dimensional settings and iterative thresholding algorithms. No scientific awards are explicitly listed, but her involvement in collaborative projects like the TUM-ICL Mathematical Sciences Hub and the Cluster of Excellence MCQST highlights her active role in academic partnerships.

As part of the academic staff, Laus is affiliated with the TUM’s outreach initiatives such as TUM-Entdeckerinnen and the Data Innovation Lab, promoting mathematical education and innovation. No doctoral advisees are listed in the provided information.