Published multiple high-impact papers including 'Mitigating Clever Hans Strategies in Image Classifiers through Generating Counterexamples' and 'Neural interaction explainable AI predicts drug response across cancers'
Background
Professor at Charité – Universitätsmedizin Berlin
Research Group Lead at the Berlin Institute for the Foundations of Learning and Data (BIFOLD)
Research focuses on Explainable AI (XAI) methods and their applications in medical diagnosis and research
Aims to develop explanation approaches that integrate well with state-of-the-art machine learning models for verification and insight discovery
Research interests: Explainable AI, Machine Learning, Data Science