prof. dr. J.W.A. (John) Rossen
Prof. Dr. John W.A. Rossen is Endowed Professor at the Department of Medical Microbiology and Infection Control at the University of Groningen and at Isala Hospital in Zwolle, the Netherlands. He also serves as Medical Head of Innovation and Science at Isala Hospital, fostering partnerships between academia, healthcare, and industry to advance translational research and regional innovation.
With more than three decades of experience in molecular biology, virology, and microbiology, Prof. Rossen has authored over 235 peer-reviewed publications. He obtained a BSc in Mathematics and Physics, an MSc in Biology, and a PhD in Molecular Virology and Cell Biology from Utrecht University (1996), where his doctoral research focused on coronavirus–host cell interactions.
His professional career includes leadership positions such as Chair of multiple scientific study groups, Faculty Council Member, Director of R&D and Head of Microbiology & Product Strategy at IDbyDNA (USA), and Scientific Lead of the Horizon Europe project DRAIGON (draigon.eu). Between 2020 and 2021 he deepened his expertise in metagenomics during a sabbatical at IDbyDNA and currently holds an Adjunct Professorship at the University of Utah School of Medicine.
Prof. Rossen has supervised 24 PhD candidates, advancing interdisciplinary research in microbiology, bioinformatics, epidemiology, and public health. He contributes to specialist training through the Concilium Medical Molecular Microbiology of the Dutch Society for Medical Microbiology (NVMM) and serves on the NVMM Working Group for International Medical Microbiology, promoting international collaboration.
Research Group: Antimicrobial Resistance, Genomics, and Epidemiology (AGE)
The AGE research group adopts a One Health framework, integrating human, animal, and environmental domains to study the dynamics of antimicrobial resistance (AMR) and emerging pathogens. The group develops and implements advanced molecular tools for surveillance and diagnostics, combined with artificial intelligence (AI) and machine learning (ML) for the analysis of complex genomic and epidemiological datasets.
These approaches provide deeper insight into resistance and virulence mechanisms and the pathways of pathogen transmission. By linking molecular findings with clinical and epidemiological contexts, the group aims to strengthen predictive models of resistance emergence, disease outcomes, and outbreak potential. Ultimately, this work supports improved antimicrobial stewardship, more effective infection prevention and control strategies, and evidence-based public health policies.