Our research group is interested in the development of methods for extracting relevant information from complex data. This data often arise from collaborations with lab scientists. My research in mathematical statistics is concerned with developing such methods and tools to equip these with statistical error control. The challenge is to incorporate computational aspects at the same hand, an important issue in modern data science.
Statistical inverse problems, nonparametric regression, statistical imaging and signal recovery, multiscale testing and estimation, qualitative inference, shape analysis, optimal transport.
fast segmentation algorithms, motion estimation, resampling
Statistical methods for single molecule experiments, ion channel recordings, nanoscale fluorescence microscopy. My work on Nanostatistics has been highlighted in the Research Features Magazine.
- Other areas of application I have been involved include
Clinical trials: Bioequivalence and noninferiority trials.
Econometrics: microstructure noise models.
Pattern recognition: Analysis of fingerprints and biometric identification security.
In the recently founded Felix-Bernstein Institute for Mathematical Statistics in the Biosciences (FBMS)
we foster interaction of Statistics with Bio- and Lifescience.
Since 2010 I am Max-Planck-fellow heading the Statistical Inverse Problems in Biophysics group
at the Max Planck Institute for Biophysical Chemistry.