Carola-Bibiane Schönlieb
Professor of Applied Mathematics, University of Cambridge
I am a Professor of Applied Mathematics at DAMTP and head of the Cambridge Image Analysis group (CIA). Moreover, I am the Director of the Cantab Capital Institute for the Mathematics of Information (CCIMI) and Director of the EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging (CMIH), a Fellow of Jesus College, Cambridge and co-Chair of the Cambridge Centre for Data Driven Discovery (C2D3). Currently I am also chairing the SIAM activity group on Imaging Sciences and the Applied Mathematics Committee of the European Mathematical Society (EMS). I am a 2017 Philip Leverhulme Prize winner. My research interests focus on variational methods, partial differential equations and machine learning for image analysis, image processing and inverse imaging problems. I have active interdisciplinary collaborations with clinicians, biologists and physicists on biomedical imaging topics, chemical engineers and plant scientists on image sensing, as well as collaborations with artists and art conservators on digital art restoration.
EXPs by Carola-Bibiane Schönlieb
Image processing is a group of techniques that manipulate images. There are many examples where images appear. They appear in healthcare; they appear in cultural heritage; they appear in environmental monitoring and in material sciences. A lot of research is dependent on automated interpretation of imaging data.
Mathematical image processing has been hit tremendously by the emergence and success of deep learning. The philosophical implications of deep learning and the things we should worry about, or sometimes not worry about, are always triggered by the fact that we often use imaging as a tool to look at things that we otherwise cannot look at.
A lot of times, if I look at a particular historical piece, the artwork has gone through a process. Where we come in as mathematical imaging specialists is, we try to go back and discover what has been lost — or maybe not completely lost, but what is no longer visible to the human eye.