Prof. Carola-Bibiane Schönlieb: Inverse Problems in Imaging: From Differential Equations to Deep Learning
Abstract. In the last couple of years the processing and analysis of imaging data has undergone a significant paradigm shift, from knowledge driven approaches that derive imaging models from first principles to purely data driven approaches that derive models from data. In this talk I will discuss image processing methods that operate at the interface of these paradigms and feature both a knowledge driven (mathematical modelling) and a data driven (machine learning) component. Mathematical modelling is useful in the presence of prior information about the imaging data and relevant features of interest, for narrowing down the search space, for highly generalizable methods with solutions that come with theoretical solution guarantees. Machine learning on the other hand is a powerful tool for customising image processing methods to individual data sets. Their combination is the topic of this talk, furnished with examples for image classification under minimal supervision with an application to chest x-rays, tomographic image reconstruction with learned priors and fast spatio-temporal MRI.
Biography. Carola-Bibiane Schönlieb graduated from the Institute for Mathematics, University of Salzburg (Austria) in 2004. From 2004 to 2005 she held a teaching position in Salzburg. She received her PhD degree from the University of Cambridge (UK) in 2009. After one year of postdoctoral activity at the University of Göttingen (Germany), she became a Lecturer at Cambridge in 2010, promoted to Reader in 2015 and promoted to Professor in 2018. Since 2011 she is a fellow of Jesus College Cambridge and since 2016 a fellow of the Alan Turing Institute, London. She currently is Professor of Applied Mathematics at the University of Cambridge, where she is head of the Cambridge Image Analysis group and co-Director of the EPSRC Cambridge Mathematics of Information in Healthcare Hub. Her current research interests focus on variational methods, partial differential equations and machine learning for image analysis, image processing and inverse imaging problems.
Biography. Carola-Bibiane Schönlieb graduated from the Institute for Mathematics, University of Salzburg (Austria) in 2004. From 2004 to 2005 she held a teaching position in Salzburg. She received her PhD degree from the University of Cambridge (UK) in 2009. After one year of postdoctoral activity at the University of Göttingen (Germany), she became a Lecturer at Cambridge in 2010, promoted to Reader in 2015 and promoted to Professor in 2018. Since 2011 she is a fellow of Jesus College Cambridge and since 2016 a fellow of the Alan Turing Institute, London. She currently is Professor of Applied Mathematics at the University of Cambridge, where she is head of the Cambridge Image Analysis group and co-Director of the EPSRC Cambridge Mathematics of Information in Healthcare Hub. Her current research interests focus on variational methods, partial differential equations and machine learning for image analysis, image processing and inverse imaging problems.
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