Prof. Can Ergen

Can Ergen is a physician-scientist and computational biologist whose research combines artificial intelligence, systems immunology, and clinical medicine to understand the molecular basis of human disease. After studying medicine and completing his residency in internal medicine, he specialized in computational biology, developing machine learning methods for the analysis of single-cell and multimodal omics data. During his postdoctoral research at the University of California, Berkeley, in the lab of Nir Yosef, he developed probabilistic models for large-scale single-cell genomics and contributed to the scverse ecosystem, creating widely adopted open-source methods for scalable data integration, representation learning, and statistical modeling.

His research has been supported through competitive funding from the German Research Foundation, and the Chan Zuckerberg Initiative, and has resulted in software used by hundreds of researchers worldwide. His work focuses on developing generative and multimodal AI methods that integrate molecular, spatial, and clinical data to uncover disease mechanisms, define molecular endotypes, and advance precision medicine.

In 2026, Can Ergen was appointed Assistant Professor of Systems Immunology at the University of Würzburg and Principal Investigator at Helmholtz Munich. His laboratory develops computational foundations for multiscale biomedical AI, bridging single-cell and spatial omics with clinical data to build AI models that connect cells, tissues, and patients. By combining methodological innovation with open-source software and close collaboration between computational scientists, clinicians, and experimental researchers, his goal is to establish a new generation of AI methods for precision medicine.