We are a research group at the Heidelberg University and Heidelberg University Hospital focuses on problem driven development of AI/ML approaches to data exploration, hypothesis generation and computational scientific discovery to facilitate translational biomedicine from imaging, single-cell and spatial omics data.

OWe work at the intersection of AI and biomedicine to address a fundamental challenge in biology and medicine: understanding the principles governing tissue organization and plasticity. We develop interpretable, scalable machine learning and optimization approaches that turn the complexity of single-cell and spatial omics data into mechanistic insight and clinical prediction.

• We formalize spatial tissue organization by learning representations that integrate single-cell, spatial multiomics, and imaging data into a unified view of tissue architecture, establishing that the spatial arrangement of cells encodes disease state in ways that are structured, persistent, and clinically consequential.

• We model tissue plasticity as a structured dynamical process, reconstructing how spatial programs evolve across disease stages and under therapeutic pressure to generate mechanistic hypotheses about the principles of adaptation, immune evasion, and resistance.

• We link spatial organization to clinical outcomes by integrating molecular, spatial, and imaging layers into explainable frameworks that establish the molecular architecture of tissue as predictive of disease progression and therapeutic response, toward translation into clinical practice.

We value collaborations with clinical, experimental biology groups and groups working on the development of novel methods for the acquisition of single-cell spatial omics data. We welcome synergistic collaborations with computational groups towards the construction of more robust theoretical and computational frameworks for the analysis of all aspects of biomedical data and beyond.