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Department of Applied Mathematics and Theoretical Physics

Dr Roberts is Senior Research Associate of Applied Mathematics at DAMTP and member of the Cambridge Image Analysis group (CIA) and leads the algorithm development team for the global COVID-19 AIX-COVNET collaboration, see https://covid19ai.maths.cam.ac.uk/.

Career

Positions:

since April 2021: Senior Research Associate at DAMTP, University of Cambridge, UK.

March 2020 to March 2021: Research Associate at DAMTP, University of Cambridge, UK.

since April 2019: Postdoctoral Fellow at AstraZeneca, Cambridge, UK

Education:

July 2019: Doctor of Philosophy, University of Liverpool, UK

June 2015: Master’s degree in Mathematics with Honors, Durham University, UK

Research

Dr Roberts' research interests focus on variational methods for image processing (in particular image segmentation and registration), machine learning for image and data analysis, image processing and data analysis. He has active interdisciplinary collaborations with other applied mathematicians, computer scientists and clinicians focussing on medical imaging problems. He has vast experience in studying medical imaging problems for lung diseases including (but not limited to) lung cancer, idiopathic lung fibrosis, mesothelioma and drug induced interstitial lung disease.

Publications

Author Correction: 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)
5,
943
Machine churning
M Roberts
– NEW SCIENTIST
(2021)
245,
23
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
– Nat Biomed Eng
(2021)
5,
509
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
– Radiology. Artificial intelligence
(2021)
3,
e210011
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)
3,
199
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
– Sci Rep
(2021)
11,
3499
On an effective multigrid solver for solving a class of variational problems with application to image segmentation
M Roberts, K Chen, J Li, KL Irion
– International Journal of Computer Mathematics
(2019)
97,
1
Chan-Vese Reformulation for Selective Image Segmentation.
M Roberts, J Spencer
– Journal of Mathematical Imaging and Vision
(2019)
61,
1173
A Convex Geodesic Selective Model for Image Segmentation
M Roberts, K Chen, KL Irion
– Journal of Mathematical Imaging and Vision
(2018)
61,
482
Multigrid algorithm based on hybrid smoothers for variational and selective segmentation models
M Roberts, K Chen, KL Irion
– International Journal of Computer Mathematics
(2018)
96,
1
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Research Group

Cambridge Image Analysis

Room

F1.13

Telephone

01223 760390