Jan Baumbach is director of the Institute for Computational Systems Biology (cosy.bio) in Hamburg. He studied computer science at Bielefeld University in Germany. His research career started at Rothamsted Research in Harpenden (UK) where he worked on computational methods for the integration of molecular biology data. He returned to the Center for Biotechnology in Bielefeld for his PhD studies where he developed models for the reconstruction of corynebacterial gene-regulatory networks. Afterwards, at the University of California at Berkeley, he worked in the Algorithms group of Richard Karp on weighted transitive graph projection problems for clustering of large-scale biomedical data sets. From 2010, Jan was head of the Computational Systems Biology group at the Max Planck Institute for Informatics in Saarbrücken, Germany. In 2012, he moved to the University of Southern Denmark as Associate Professor and head of the Computational BioMedicine group. His research concentrated on systems and network biomedicine. He was study program coordinator of the Computational BioMedicine program until 2017. In 2018 he moved to the Technical University of Munich (TUM) as chair of Experimental Bioinformatics (ExBio), where he developed federated AI approaches enabling privacy-preserving, multi-center systems and precision medicine. In January 2021, the lab relocated to the University of Hamburg where Jan Baumbach became director of cosy.bio. He is an active entrepreneur, co-founder of several startups, and advisor of pharmaceutical companies and EQT firms. In 2023, he was appointed a Humboldt Scout through the Henriette Herz-Scouting-Program of the Humboldt Foundation.
This panel will explore how a collaborative, cross-sector approach, uniting industry leaders and academic institutions, can unlock the full potential of integrated data and federated learning to transform R&D in complex disease areas. Focusing on osteoarthritis (OA) as a case study, the discussion will highlight how these technologies can overcome long-standing barriers in clinical research, particularly in diseases with high prevalence and socio-economic burden. By embedding disease progression modeling within next-generation analytics such as digital twins, this approach offers a path toward more predictive, efficient, and outcome-driven therapeutic development – bringing new hope to millions of patients worldwide.