I am a postdoctoral researcher at the Physikalisch-Technische Bundesanstalt (PTB) in Berlin, which I joined in August 2024. In the working group Numerical Methods (AG 8.43) of the department Mathematical Modelling and Data Analysis (8.4), I work on uncertainty quantification for high-dimensional problems arising in metrology.

My research focuses on the numerical solution of high-dimensional problems (functions depending on a very large number of variables) by means of low-rank tensor methods such as tensor trains. These problems arise naturally in physics, stochastics and machine learning, and prominent examples include parametric partial differential equations that arise in uncertainty quantification. Since evaluations of the sought functions are often expensive, I develop and analyse sample-efficient regression algorithms, combining techniques from statistical learning theory, optimal sampling and compressed sensing.

Before joining PTB, I was a postdoctoral researcher at Nantes Université / Centrale Nantes in the Laboratoire de Mathématiques Jean Leray, working with Anthony Nouy on optimal sampling for nonlinear approximation. I obtained my Ph.D. in Mathematics from the Technische Universität Berlin in 2021 under the supervision of Reinhold Schneider, funded by the BIMoS graduate school.

My research interests include

• Approximation theory
• High-dimensional approximation and tensor networks
• Sparse and weighted least-squares regression, compressed sensing
• Optimal and adaptive sampling, active learning
• Sample complexity and statistical learning theory
• Uncertainty quantification and Bayesian inversion

You can find my publications here and more details in my CV.