Aasa Feragen

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About

Aasa Feragen is a leading researcher in geometric machine learning, with a particular focus on medical image analysis and computational anatomy. Her major contributions lie in developing mathematically rigorous methods for analyzing complex, non-Euclidean data, such as shapes, trees, and graphs, which are fundamental in medical imaging. Feragen is best known for her work on statistical analysis of tree-structured data, including airway and vascular trees, where she introduced novel frameworks for comparing and modeling these intricate structures. Her research has had a profound impact, with her most-cited papers collectively garnering thousands of citations, reflecting the field's reliance on her foundational algorithms. Notably, she has pioneered methods for understanding variability in biological shapes, enabling more precise diagnostics and disease progression tracking. Feragen’s recent work explores the intersection of AI and clinical collaboration, emphasizing the need for human-centered design in medical AI systems. She has also been recognized for her contributions to the MICCAI community, serving as a program chair and advocating for reproducibility and open science. Her work continues to bridge the gap between theoretical machine learning and practical medical applications, inspiring a new generation of researchers.