Azade Farshad

Azade Farshad is a Lecturer and researcher at the Technical University of Munich (TUM), affiliated with the Chair of Medical Informatics Applications under Prof. Nassir Navab. Her work focuses on advanced computer vision techniques applied to medical imaging and surgical robotics, with expertise in generative models, meta-learning, and scene graph-based systems. She actively contributes to research projects like MIGS (Meta Image Generation from Scene Graphs) and FedAP (Federated Learning for Non-IID Data), and leads initiatives in medical augmented reality and surgical data science.

Her teaching portfolio includes courses such as Computer Aided Medical Procedures, Medical Augmented Reality, and Deep Learning for Medical Applications. She supervises master’s theses on topics like meta-learning, semantic image manipulation, and robust machine learning models, with notable students including Caghan Koksal and Sraddha Das.

Azade is professionally active in the computer vision and medical imaging communities, serving on the executive committees of the British Machine Vision Association (BMVA) and Women in MICCAI (WiM). She co-organized workshops at CVPR and ICCV, and reviews for top conferences like CVPR, NeurIPS, and MICCAI.

Her research labs include the DHM (Deutsches Herzzentrum München), IFL Lab, and NARVIS Lab, where she explores applications in generative AI, surgical data science, and vision-language systems. Key contributions include Y-Net (a medical image segmentation framework) and DisPositioNet (for semantic image manipulation).