Press Release

01.05.2021

Julia Schnabel to lead new Institute of Machine Learning in Biomedical Imaging

Julia Schnabel has joined the Center as the head of the newly formed Institute of Machine Learning in Biomedical Imaging (IML). The new institute will complement and link into the biomedical imaging and computational health research program at the Center.

©Julia Schnabel

Julia Schnabel's appointment means Helmholtz Zentrum München has gained an outstanding internationally renowned talent in this interdisciplinary research field.

Before joining the Center, Julia held a Chair in Computational Imaging at King’s College London and was full Professor of Engineering Science at the University of Oxford. In 2019 she was awarded a Helmholtz Distinguished Professorship, to form a new Institute for Machine Learning in Biomedical Imaging at Helmholtz Zentrum München together with a Liesel Beckmann Distinguished Professorship at Technical University of Munich (TUM). Both programs are designed to attract top female scientists to leadership positions.

With the new institute, Julia aims to foster a strongly collaborative, diverse and inclusive interdisciplinary research environment that attracts early career researchers. She is a Fellow of IEEE, of the MICCAI Society, the European Laboratory for Learning and Intelligent Systems (ELLIS) and a founding member of ELLIS Munich, a joint endeavor of TUM and Helmholtz Zentrum München. She is also a founding and executive editor of the new free open-access journal Machine Learning in Biomedical Imaging (MELBA).

The mission of the Institute for Machine Learning in Biomedical Imaging is to leverage machine learning for the grand challenges in biomedical imaging in areas of unmet clinical need. Its goal is to fundamentally transform the use of imaging for diagnostics and prognostics. Novel and affordable solutions should empower clinics to make more accurate, fast and reliable decisions for early detection, treatment planning and improved patient outcome. Julia’s research focus is on applications in cancer, cardiovascular diseases, and maternal/perinatal health.