Aspelmeier, PD Dr. Timo (statistical imaging)
Munk, Prof. Dr. Axel (mathematical statistics)
Werner, Dr. Frank (statistical inverse problems)
Ta, H., Keller, J., Haltmeier, M. Saka, S.K., Schmied, J., Opazo, F., Tinnefeld, P., Munk, A., Hell, S.W. (2015). Mapping molecules in scanning far-field fluorescence nanoscopy. Nature Communications, 6, 1-7, DOI: 10.1038/ncomms8977.
Aspelmeier, T., Egner, A., Munk, A. (2015).
Modern Statistical Challenges in High Resolution Fluorescence Microscopy. Annual Review of Statistics and its Application, 2, 163-202.
Frick, K., Munk, A., Sieling, H. (2014).
Multiscale Change-Point Inference (software "stepR" for multiscale change point analysis "SMUCE") With discussion and rejoinder by the authors. Journ. Royal Statist. Society, Ser. B, , 76, 495-580. arXiv:1301.7212 long version with full proofs.
Li, H., Haltmeier, M., Zhang, S., Frahm, J., Munk, A. (2013). Aggregated Motion Estimation for Image Reconstruction in Real-Time MRI (Supplements). Magnetic Resonance in Medicine, DOI: 10.1002/mrm.25020.
Our group is involved in the development of statistical methodology for biophysical applications. Modern biophysical data analysis is located at the cutting edge between biophysical modelling and statistical evaluation techniques. Therefore we aim to combine expertises from these fields in our group.
We devolop sophisticated data analysis tools which are in addition hampered by the huge amount of experimental measurements. These arise from a variety of currently emerging new technologies. This includes high resolution fluorescence microscopy in living cells, patch clamping for ion channel recordings of cell membranes, high throughput technologies in genetics and real time magnetic resonance imaging.