Tim Schäfer -- rcmd.org/ts/

current work || projects || publications || contact

This is the professional website of Dr. Tim Schäfer. I am a bioinformatician, neuroscientist and software developer.

I received my PhD in bioinformatics at the Molecular Bioinformatics group of Prof. Dr. Ina Koch at Goethe-University Frankfurt in 2016. I have implemented research software during my PhD at the Koch lab, in the industry at Molecular Health, and at the Ecker Lab at University Hospital Frankfurt. I currently work as a research software engineer in the Fries Lab at ESI.

I have contributed to a range of open source projects and I am the author and maintainer of some neuroimaging software tools, including the freesurferformats and fsbrain packages for R, and libraries providing access to neuroimaging data for C++, Julia, Rust and Python. I am especially interested in spatial geometry, (3D) imaging, artificial intelligence and high-performance computing (HPC). You can find the source code for some of my projects on my github profile.

Current work

I am a scientific software developer in the Fries Lab at ESI, working on the syncopy Python package for electrophysiology data analysis (MEG, EEG, ECoG). Syncopy aims to be easy-to-use, with an interface similar to Fieldtrip, the standard Matlab package for the analysis of electrophysiology data. Syncopy is designed for large-scale data anaylysis and parallel computations, and can utilize multi-core machines and high-performance computing (HPC) systems like clusters running the Slurm job scheduler.

Brain signal visualizations created with Syncopy

Visualization of electrophysiological data.

Previous projects

Ecker Lab

I was a postdoc in Computational Neuroimaging at University Hospital Frankfurt, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy in the group of Prof Christine Ecker from 03/2018 - 04/2022. We used structural magnetic resonance imaging (sMRI) in combination with genetic data to uncover the mechanisms of autism spectrum disorders. We applied statistical and machine learning methods implemented in R, Python and Matlab to surface-based cortical reconstructions generated with Freesurfer. See the website of the Laboratory of Neuroimaging and the list of publications below for more details.

Brain mesh visualizations

Visualization of sMRI neuroimaging data, created with fsbrain. A Visualization of raw morphometry data (cortical thickness) from native space on the white surface of a subject. The view shows the data in tiles from 8 different angles. B Arbitrary data (p-values in this case) visualized on the regions of the Desikan atlas, using the surface of the fsaverage (standard space template) subject from FreeSurfer. The view shows the data in tiles from 4 different angles. C The regions of the Desikan atlas on the white surface of a subject. The colors were loaded from the respective annotation file.

Koch Lab

I finished my doctorate in 2016 and mainly worked on the following two projects in the fields of digital pathology and structural biology:

Digital pathology: The spatial distribution of immune cells in Hodgkin lymphoma

In this project, I worked on the analysis of Hodgkin lymphoma, a cancer of the lymphatic system, based on high-resolution images. We were interested in better understanding the way tumour cells interact with their environment, communicate and spread through the lymphatic system. We implemented a digital image analysis pipeline to perform cell detection, description and classification. We used graphs to model and compare spatial cell distributions in different Hodgkin lymphoma subtypes as well as lymphadenitis. This is a collaboration with the Senckenberg Institute of Pathology at University Hospital Frankfurt.

Hodgkin lymphoma cell graph

Part of a whole slide image from a Hodgkin lymphoma case. Cell nuclei are stained in blue, and CD30+ cells in red. A cell graph is displayed as an overlay. Each vertex represents a cell detected by our imaging pipeline. Edges are added between cells which are close to each other. The graphs can be used to quantify clustering and to compare cell distributions.

Structural biology: The new Protein Topology Graph Library (PTGL) webserver

My diploma thesis dealt with modeling protein structure topologies by graph-theoretical methods. A part of the thesis was the development of the Visualization of Protein Ligand Graphs (VPLG) software. VPLG computes and visualizes protein ligand graphs. It works on the super-secondary structure level and uses the atom coordinates from PDB files and the SSE assignments of the DSSP algorithm. The graphs can be saved to a database or exported in standard graph formats for further analysis. VPLG is free software and available from the project websites at Sourceforge and GitHub. It powers the PTGL protein topology database, a web server which also supports motif detection and other advanced queries based on the graphs computed for all proteins of the RCSB Protein Data Bank.

Protein structure

From 3D atom data to protein graph. The 3D atom coordinates and the secondary structure assignments are used to compute contacts between secondary structure elements (SSEs). In the final cell graph, each vertex represents an SSE, and edges model spatial contacts and relative spatial orientations between SSEs.


Here are my records on ORCID and Google Scholar, which may be more up-to-date than this list.

Journal Articles (Peer reviewed)

Preprints and Other Articles (Non-Peer Reviewed)

Conference Talks

Posters and Conferences


My email address is:


You can also contact me on Research Gate or find my office phone number and address at KGU on the website of the Laboratory of Neuroimaging.

current work || projects || publications || contact