University of Oxford
Professor Alison Noble OBE FREng FRS is the Technikos Professor of Biomedical Engineering in the Oxford University Department of Engineering Science, Associate Head of MPLS Division (Industry and Innovation), and a Fellow of St Hilda's College, Oxford. She is a former Director of the Institute of Biomedical Engineering (2012-16).
Professor Noble is a Fellow of the Royal Society, Fellow of the IET, a Fellow of the MICCAI Society, and a Fellow of the Royal Academy of Engineering. She served as the President of the MICCAI Society, the international society in her field, from 2013-16, and Past-President for 2016-17. Professor Noble regularly serves on national strategic and policy making committees and grant awarding panels related to research and innovation in healthcare technologies and more broadly engineering, and is the current Chair of the EPSRC Healthcare Technologies Strategic Advisory Team. Professor Noble is a Trustee of the Institute of Engineering Technology (IET) and of the Oxford Trust, which promotes enterprise as well as communication of SET to schools and the public, in the Oxfordshire region. She received an OBE for services to science and engineering in the Queen's Birthday Honours list in June 2013.
Professor Noble’s research interest is in biomedical image analysis, with a particular focus on raising the profile of ultrasound imaging as a first class data type by understanding the interplay of ultrasound device design (physics), clinical acquisition, and downstream image analysis and computer vision. Her most recent research has concerned applying machine learning to ultrasound to advance automatic 2D, 3D and video ultrasound analysis. Her research is motivated by unmet important clinical needs in developed world and developing world settings, and involves inter-disciplinary translational research collaborations with clinical groups from the UK and overseas. In 2016, she was the recipient of a 5-year ERC Advanced Grant entitled “Perception Ultrasound by Learning Sonographer Experience (PULSE)”, which aims to advance understanding of how to develop next generation ultrasound systems by utilizing machine learning.