@johanneskoester – he/him

Johannes Köster is professor for Bioinformatics and Computational Oncology (Bioinformatische Algorithmen in der Onkologie) at the Institute for AI in Medicine (IKIM), University Medicine Essen, University of Duisburg-Essen. He has a focus on algorithm engineering and data analysis. Johannes studied computer science at the University of Dortmund (diploma thesis 2010, with Max Planck Institute of Molecular Physiology Dortmund). Then, he did his PhD in the group of Prof. Sven Rahmann (TU Dortmund, 2015). Afterwards, Johannes was a postdoc in the groups of Prof. Shirley Liu and Prof. Myles Brown at Dana Farber Cancer Institute and Harvard University (2015-2016). In 2016, Johannes moved to the lab of Prof. Alexander Schönhuth for a brief second postdoc at Centrum Wiskunde & Informatica Amsterdam, Netherlands (CWI), where he quickly received a VENI grant for an independent position. In 2017, Johannes became the head of the group “Algorithms for reproducible bioinformatics” at the University of Duisburg-Essen in Germany. Johannes was appointed full professor at the University of Duisbug-Essen in 2023.

Johannes’ research is focused on reproducibility in three ways. First, he is the author of the popular workflow management system Snakemake and the founder of the Bioconda project for sustainably distributing bioinformatics software as easily installable packages. Together, these projects form the base for a large fraction of currently performed scalable and reproducible data analysis in bioinformatics. Second, Johannes is the author of the Rust-Bio library, enabling the use of the Rust programming language for bioinformatics by providing standard bioinformatics algorithms and data structures. Using Rust promotes reproducibilty by guaranteeing thread and memory safety at compile-time. Third, Johannes is working in the field of Bayesian statistics (e.g., for variant calling and single cell transcriptomics) in order to provide algorithms for analysis of high-throughput data while capturing and quantifying all known sources of uncertainty, thereby providing more reproducible predictions.