Our group focuses on developing new AI and machine learning approaches tailored to complex and multi-modal biomedical data to elucidate cancer mechanisms, from the intra-cellular to the patient level. We aim to learn interpretable representations of the tumor microenvironment by integrating single-cell (spatial) omics and histopathology data, and understand how tumors respond to drug perturbations ex vivo, ultimately leading to AI-assisted precision oncology approaches.
Domain(s) of application
Medicine and health
Domain of activity
Machine learning and text mining