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Department of Applied Mathematics and Theoretical Physics

Mike Roberts is Principal Research Associate (Reader / Professor Grade 11) at DAMTP and also at the Department of Medicine. He is a member of the Cambridge Image Analysis group (CIA), leads the BloodCounts! consortium ( and also leads the algorithm development team for the global COVID-19 AIX-COVNET collaboration (



October 2023 onwards: Principal Research Associate at DAMTP and Department of Medicine, University of Cambridge, UK.

April 2021 to September 2023: Senior Research Associate at DAMTP, University of Cambridge, UK.

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

April 2019 to July 2022: Postdoctoral Fellow at AstraZeneca, Cambridge, UK


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

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


Mike's 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. More recently, he has been focussing on best practice and scientific integrity in machine learning and data science, in particular for understanding the crisis of reproducibility affecting these fields. He has active interdisciplinary collaborations with other applied mathematicians, computer scientists and clinicians focussing on medical imaging problems. He has vast experience in studying high-dimensional data and medical imaging problems for lung diseases including (but not limited to) lung cancer, idiopathic lung fibrosis, mesothelioma and drug induced interstitial lung disease.


Publisher Correction: The curious case of the test set AUROC
M Roberts, A Hazan, S Dittmer, JHF Rudd, C-B Schönlieb
– Nature Machine Intelligence
Non-contact, portable and stand-off infrared thermal imager for security scanning applications
W Khor, YK Chen, M Roberts, F Ciampa
– AIP Advances
The curious case of the test set AUROC
M Roberts, A Hazan, S Dittmer, JHF Rudd, CB Schönlieb
– Nature Machine Intelligence
The curious case of the test set AUROC
M Roberts, A Hazan, S Dittmer, JHF Rudd, C-B Schönlieb
The impact of imputation quality on machine learning classifiers for datasets with missing values.
T Shadbahr, M Roberts, J Stanczuk, J Gilbey, P Teare, S Dittmer, M Thorpe, RV Torné, E Sala, P Lió, M Patel, J Preller, AIX-COVNET Collaboration, JHF Rudd, T Mirtti, AS Rannikko, JAD Aston, J Tang, C-B Schönlieb
– Communications medicine
Common methodological pitfalls in ICI pneumonitis risk prediction studies
YK Chen, S Welsh, AM Pillay, B Tannenwald, K Bliznashki, E Hutchison, JAD Aston, C-B Schönlieb, JHF Rudd, J Jones, M Roberts
– Frontiers in Immunology
Shortcut Learning: Reduced But Not Resolved
IA Selby, M Roberts, A Breger, JHF Rudd, JR Weir-McCall
– Radiology
Reinterpreting survival analysis in the universal approximator age
S Dittmer, M Roberts, J Preller, AIX COVNET, JHF Rudd, JAD Aston, C-B Schönlieb
Dis-AE: Multi-domain & Multi-task Generalisation on Real-World Clinical Data
D Kreuter, S Tull, J Gilbey, J Preller, B Consortium, JAD Aston, JHF Rudd, S Sivapalaratnam, C-B Schönlieb, N Gleadall, M Roberts
A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data
A Breger, I Selby, M Roberts, J Babar, E Gkrania-Klotsas, J Preller, L Escudero Sánchez, AIX-COVNET Collaboration, JHF Rudd, JAD Aston, JR Weir-McCall, E Sala, C-B Schönlieb
– Scientific data
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Research Group

Cambridge Image Analysis




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