# Axel Flinth

Guest lecturer, Mathematical Sciences

My research is dealing with theoretical aspects of mathematical signal processing, with focus on the application of optimization for signal reconstruction from linear measurements. A lot of my research operates within the field of “compressed sensing”, where assumptions about the structure of the signals are used for enabling, or enhancing, methods for reconstructing them. With the help of regularized optimization problems, and/or tailor-made algorithms, it is possible to reconstruct (extrinsically) n-dimensional objects from far fewer than n measurements.

My special interest is infinite-dimensional versions of the mentioned optimization problems. Especially appealing to me is that the field combines tools from many areas within mathematics: convex geometry, optimization, mathematical statistics and functional analysis.

My special interest is infinite-dimensional versions of the mentioned optimization problems. Especially appealing to me is that the field combines tools from many areas within mathematics: convex geometry, optimization, mathematical statistics and functional analysis.