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Carola-Bibiane Schönlieb currently holds the title of Professor in Applied and Computational Analysis in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. She is also a Turing Fellow of the Alan Turing Institute, Director of the EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, and Director of the Cantab Capital Institute for the Mathematics of Information. Schönlieb is Austrian, and completed her MA in mathematics at the University of Salzburg in 2004. She earned her PhD in 2009 at Cambridge, and completed postdoctoral studies at the University of Göttingen.
Schönlieb ’s work is primarily focused in image processing and partial differential equations. In particular, Schönlieb has made significant progress in applying partial differential equations in image analysis and inverse imaging problems, and problems in 3D and 4D imaging. As an interdisciplinarian, Schönlieb’s work has significant implications for a wide range of fields that employ video imaging, including chemical engineering, biomedical sciences, and art.
In addition to her research, Schönlieb is active in encouraging and advocating for women in mathematics, and is active with the European Women in Mathematics Association, and the Committee for Women in Mathematics.
For her work, Schönlieb has received awards and honors including the Whitehead Prize of the London Mathematical Society, the Philip Leverhulme Prize, and was named the Mary Cartwright Lecturer of the London Mathematical Society.
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According to Wikipedia,
Carola-Bibiane Schönlieb is an Austrian mathematician who works in image processing and partial differential equations. She is a Fellow of Jesus College, Cambridge and a Professor in Applied and Computational Analysis in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. She is the author of the book Partial Differential Equation Methods for Image Inpainting , on methods for using the solutions to partial differential equations to fill in gaps in digital images.
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