Researcher biography

I develop methods to integrate multiple quantitative imaging modalities (magnetic susceptibility,  T1, T2*) to extract hidden tissue characteristics that have the potential to be used as early biomarkers for neurodegenerative diseases.

Exploiting the high signal levels of ultra-high field 7 Tesla MRI and combining this with image processing driven by artificial intelligence we investigate and quantify disease processes at an unprecedented level of detail. The construction of very high resolution digital MRI atlases ( allows us to better understand structural changes on a population level and to transfer this knowledge to analyse data in single individuals.

To foster collaboration, we release our source code on GitHub ( and and provide documentation for our developed workflows. I also contribute to the PhysIO toolbox, developed by Lars Kasper at the TNU Zurich, that enables the modeling and removal of physiological noise in fMRI time series.

Further material is available in my publically accessible figshare profile and I regularly post updates on my research at


I tutor the course "Classical Theory of Magnetic Resonance" (MRES7001). I am also responsible for organizing the CAI Data Analysis Seminar, where we deal with solving practical problems for imaging data analysis, including teaching practical skills in Python (supported by the DataCamp for the Classroom initiative), Linux shell scripting, high performance computing and a variety of scientific workflows (NiPy, FreeSurfer, FSL, DCM, ICE, BIDS, SPM, AFNI, Deep Learning). If you are interested in joining this seminar, please get in touch with me: