I am an expert in statistical analysis, modelling and inference of coupling and causality in complex systems. I did my PhD in Space Physics at the University of Cologne with application in plasma turbulence of planetary magnetospheres. I then moved to the field of Neuroscience, where I worked on Deep Brain Stimulation data in Parkinson’s disease and dynamical resting state networks derived from M/EEG and massively parallel spike trains. As a scientific coordinator and data science consultant I am skilled in project management and in the translation of real-world problems into quantitative models.
Apart from my work as Data Scientist, I am an editor for the Scientists for Future Podcast and try to make the climate impact of products visible using augmented reality (see GreenScore). In my spare time I enjoy spending time with my wife and my two boys and occasionally play the saxophone.
Data Science in single-cell analytics (since 2019)
Data Science Consultant at Comma Soft AG, Bonn, Germany
Since 2019 I work as data science consultant in the Life Science team at Comma Soft in Bonn, Germany, with a focus on single-cell genomics in the industrial and academic sector. I am part of the team behind the single-cell analytics platform FASTGenomics that provides cloud-based data management and analytical software environments for the single-cell community.
Validation and modelling of cortical activity (2017-2018)
Postdoc in Computational Neuroscience at Research Centre Jülich, Jülich, Germany
Research assistant and scientific coordinator in the Human Brain Project, co-developed Python package NetworkUnit for statistical validation of spiking neuronal network models and developed an estimator of active motor cortical states based on dynamic functional connectivity of massively parallel spike trains.
- Dahmen et al. Long-range coordination patterns in cortex change with behavioral context, biorXiv, 2020
- Dabrowska, Voges, von Papen et al. On the complexity of resting state spiking activity in monkey motor cortex, biorXiv, 2020
- Gutzen, von Papen et al. Reproducible neural network simulations: statistical methods for model validation on the level of network activity data, Front.Neuroinf., 2018
Causal inference in electrophysiology (2014-2017)
Postdoc in Clinical Neuroscience at University Hospital Cologne & Heinrich-Heine University Düsseldorf, Germany
Developed wavelet-based methods for phase-dependent reconstruction of coupled time series and time- and frequency-resolved nonparametric Granger causality. Estimated causality between brain regions and muscle activity during tremor using transfer entropy.
- Weber, Florin, von Papen et al. Characterization of information processing in the subthalamic area of Parkinson’s patients, NeuroImage, 2020
- von Papen et al. Phase-coherence classification: a new wavelet-based method to separate local field potentials, J.Neuroscience Methods, 2017
- Weber, Florin, von Papen and Timmermann. The influence of filtering and downsampling on the estimation of transfer entropy, PLOS ONE, 2017
Turbulence in Saturn's magnetosphere (2011-2014)
PhD in Space Physics at University of Cologne, Germany
First to show that Saturn’s magnetosphere is in a turbulent state using wavelet analysis of magnetic field data. Detected temporal and spatial patterns in nine years of data and developed a parametric model to explain observed electron temperatures.
- von Papen & Saur. Longitudinal and local time asymmetries of magnetospheric turbulence in Saturn’s plasma sheet, JGR:Space Physics, 2016
- von Papen & Saur. Forward modeling of reduced power spectra from three-dimensional k-space, Astrophysical Journal, 2015
- von Papen et al. Turbulent magnetic field fluctuations in Saturn’s magnetosphere, JGR:Space Physics, 2014