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Department of Applied Mathematics and Theoretical Physics

I am a Postdoc at the Cambridge Image Analysis group. My research interests include

  • inverse problems
  • neural networks with mathematical guarantees
  • medical imaging (in particular high-dimensional 3D data)
  • adversarial examples
  • structure preserving deep learning

Publications

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
iUNets: Learnable Invertible Up- and Downsampling for Large-Scale Inverse Problems
C Etmann, R Ke, CB Schonlieb
– PROCEEDINGS OF THE 2020 IEEE 30TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP)
(2020)
2020-September,
1
On the connection between adversarial robustness and saliency map interpretability
C Etmann, S Lunz, P Maass, CB Schönlieb
– 36th International Conference on Machine Learning, ICML 2019
(2019)
2019-June,
3255
Structure preserving deep learning
E Celledoni, MJ Ehrhardt, C Etmann, RI McLachlan, B Owren, C-B Schönlieb, F Sherry
Equivariant neural networks for inverse problems
E Celledoni, MJ Ehrhardt, C Etmann, B Owren, C-B Schönlieb, F Sherry
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)
J Stanczuk, C Etmann, LM Kreusser, C-B Schönlieb

Research Group

Cambridge Image Analysis

Room

F2.05

Telephone

01223 337917