Professor Carola Bibiane Schönlieb

Professor Schönlieb is Professor of Applied Mathematics at DAMTP and head of the Cambridge Image Analysis group (CIA). Moreover, she is 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).

Career

Positions:

since October 2018: Professor at DAMTP, University of Cambridge, UK.
October 2015 to September 2018: Reader at DAMTP, University of Cambridge, UK.
since October 2011: Fellow of Jesus College, Cambridge, UK.
September 2010 to September 2015: Lecturer at DAMTP, University of Cambridge, UK.
September 2009 to September 2010: Postdoc at NAM (Institute of Numerical and Applied Mathematics), Georg-August University Goettingen, Germany.
October 2008 to September 2009: Research Assistant at DAMTP, University of Cambridge.
October 2005 to October 2008: Research Assistant at the Faculty of Mathematics, University of Vienna, Austria.
September 2002 to June 2004: Research Assistant at the Department of Mathematics, University of Salzburg, Austria.

Education:

July 18, 2009: Admission to the degree Doctor of Philosophy, University of Cambridge (UK)
January 30, 2004: Master’s degree in Mathematics with Honors, University of Salzburg (Austria)

Honors and Awards:

2020: Wolfson Fellowship, Royal Society UK.
2019: Calderón Prize, Inverse Problems International Association.
2017: Philip Leverhulme Prize.
2016: Whitehead Prize, London Mathematical Society.
2013: EPSRC Science Photo Award, 1st Prize in the Category People.
2008: Mary Bradburn Award from the BFWG.
2004: Scholarship from the University of Salzburg (Austria) for exceptional achievements as a student
2002: Hans-Stegbuchner-Award from the Department of Mathematics, University of Salzburg (Austria).

Research

Professor Schönlieb's research interests focus on variational methods, partial differential equations and machine learning for image analysis, image processing and inverse imaging problems. She has 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.. More details the website of her research group, Cambridge Image Analysis (CIA).

Publications

Deep learning as optimal control problems ⁎ ⁎ ⁎ MB acknowledges support from the Leverhulme Trust Early Career Fellowship ECF-2016-611 ‘Learning from mistakes: a supervised feedback-loop for imaging applications’. CBS acknowledges support from the Leverhulme Trust project on Breaking the non-convexity barrier, the Philip Leverhulme Prize, the EPSRC grant No. EP/M00483X/1, the EPSRC Centre No. EP/N014588/1, the European Union Horizon 2020 research and innovation programmes under the Marie Skodowska-Curie grant agreement No. 777826 No-MADS and No. 691070 CHiPS, the Cantab Capital Institute for the Mathematics of Information and the Alan Turing Institute. We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Quadro P6000 and a Titan Xp GPU used for this research. EC and BO thank the SPIRIT project (No. 231632) under the Research Council of Norway FRIPRO funding scheme. This work was supported by EPSRC grant No. EP/K032208/1.

M Benning, E Celledoni, MJ Ehrhardt, B Owren, CB Schonlieb

– IFAC-PapersOnLine

(2021)

54,

620

(doi: 10.1016/j.ifacol.2021.06.124)

Structure-preserving deep learning

E Celledoni, MJ Ehrhardt, C Etmann, RI Mclachlan, B Owren, CB Schonlieb, F Sherry

– European Journal of Applied Mathematics

(2021)

32,

888

(doi: 10.1017/s0956792521000139)

Adversarially Learned Iterative Reconstruction for Imaging Inverse Problems

S Mukherjee, O Öktem, CB Schönlieb

– Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

(2021)

12679,

540

(doi: 10.1007/978-3-030-75549-2_43)

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images

G Wang, X Liu, J Shen, C Wang, Z Li, L Ye, X Wu, T Chen, K Wang, X Zhang, Z Zhou, J Yang, Y Sang, R Deng, W Liang, T Yu, M Gao, J Wang, Z Yang, H Cai, G Lu, L Zhang, L Yang, W Xu, W Wang, A Olvera, I Ziyar, C Zhang, O Li, W Liao, J Liu, W Chen, W Chen, J Shi, L Zheng, L Zhang, Z Yan, X Zou, G Lin, G Cao, LL Lau, L Mo, Y Liang, M Roberts, E Sala, C-B Schönlieb, M Fok, JY-N Lau, T Xu, J He, K Zhang, W Li, T Lin

– Nature Biomedical Engineering

(2021)

509

(doi: 10.1038/s41551-021-00704-1)

Dynamic spectral residual superpixels

J Zhang, AI Aviles-Rivero, D Heydecker, X Zhuang, R Chan, CB Schönlieb

– Pattern Recognition

(2021)

112,

107705

(doi: 10.1016/j.patcog.2020.107705)

Mechanisms Underlying Vascular Endothelial Growth Factor Receptor Inhibition–Induced Hypertension

KM Mäki-Petäjä, A McGeoch, LL Yang, A Hubsch, CM McEniery, PAR Meyer, F Mir, P Gajendragadkar, N Ramenatte, G Anandappa, S Santos Franco, SJ Bond, C-B Schönlieb, Y Boink, C Brune, IB Wilkinson, DI Jodrell, J Cheriyan

– Hypertension (Dallas, Tex. : 1979)

(2021)

77,

1591

(doi: 10.1161/hypertensionaha.120.16454)

Multi-Task Deep Learning for Image Segmentation Using Recursive Approximation Tasks.

R Ke, A Bugeau, N Papadakis, M Kirkland, P Schuetz, C-B Schonlieb

– IEEE Trans Image Process

(2021)

30,

3555

(doi: 10.1109/tip.2021.3062726)

Machine Learning for COVID-19 Diagnosis and Prognostication: Lessons for Amplifying the Signal While Reducing the Noise

D Driggs, I Selby, M Roberts, E Gkrania-Klotsas, J Rudd, G Yang, J Babar, E Sala, C Schoenlieb

– Radiol Artif Intell

(2021)

e210011

(doi: 10.1148/ryai.2021210011)

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, AI Aviles-Rivero, C Etmann, C McCague, L Beer, JR Weir-McCall, Z Teng, E Gkrania-Klotsas, A Ruggiero, A Korhonen, E Jefferson, E Ako, G Langs, G Gozaliasl, G Yang, H Prosch, J Preller, J Stanczuk, J Tang, J Hofmanninger, J Babar, LE Sánchez, M Thillai, PM Gonzalez, P Teare, X Zhu, M Patel, C Cafolla, H Azadbakht, J Jacob, J Lowe, K Zhang, K Bradley, M Wassin, M Holzer, K Ji, MD Ortet, T Ai, N Walton, P Lio, S Stranks, T Shadbahr, W Lin, Y Zha, Z Niu, JHF Rudd, E Sala, CB Schönlieb

– Nature Machine Intelligence

(2021)

199

(doi: 10.1038/s42256-021-00307-0)

Assessing Robustness of Carotid Artery CT Angiography Radiomics in the Identification of Culprit lesions in Cerebrovascular Events

EPV Le, L Rundo, JM Tarkin, NR Evans, MM Chowdhury, PA Coughlin, H Pavey, C Wall, F Zaccagna, FA Gallagher, Y Huang, R Sriranjan, A Le, JR Weir-McCall, M Roberts, FJ Gilbert, EA Warburton, C-B Schönlieb, E Sala, JHF Rudd

– Nature Scientific Reports

(2021)

11,

3499

(doi: 10.1038/s41598-021-82760-w)

Department of Applied Mathematics and Theoretical Physics

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