Dr Martin Benning
Senior Lecturer in Inverse Problems and Machine Learning
Email: email@example.com Telephone: +44 (0)20 7882 5370Room Number: Mathematical Sciences Building, Room: MB-G27Office Hours: On request
Dr Martin Benning is a Senior Lecturer in Inverse Problems and Machine Learning and is a member of Statistics and Data Science group.
Dr Benning's area of mathematical expertise is the theoretical and computational handling of inverse & ill-posed problems. In inverse problems, an unknown quantity – such as the image of the interior of a human body – is only accessible indirectly through the inversion of a mathematical operator. In nearly all relevant applications, this inversion process is highly unstable with respect to measurement errors. A remedy is the approximation of the inverses via families of continuous operators, also known as regularisation operators.
The particular focus of Dr Benning's research is the analysis and numerical realisation of regularisation operators arising from the minimisation of non-smooth functionals. His research covers topics such as non-linear (numerical) analysis, (convex and non-convex) optimisation, functional analysis, machine learning, imaging and image processing, compressed sensing and (big) data analysis.
- Inverse problems
- Machine learning
- Numerical analysis
- Variational calculus
- Regularisation theory
- Compressed sensing
Examples of research funding:
Leverhulme Trust Early Career Fellowship "Learning from mistakes: a supervised feedback-loop for imaging applications" (September 2016 - August 2019)
- M. Benning, and E. S. Riis. “Bregman methods for large-scale optimisation with applications in imaging.” Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision (2021): 1-42
- F. Sherry, M. Benning, J. C. De los Reyes, M. J. Graves, G. Maierhofer, G. Williams, C.-B. Schönlieb and M. J. Ehrhardt. “Learning the sampling pattern for MRI”. IEEE Transactions on Medical Imaging, 2020, 39(12): 4310-4321.
- M. Benning, E. Celledoni, M. J. Ehrhardt, B. Owren, and C.-B. Schönlieb. “Deep learning as optimal control problems: Models and numerical methods.” Journal of Computational Dynamics, 2019, 6(2):171-198.
- M. Benning, M. Burger. “Modern regularization methods for inverse problems”. Acta Numerica, 27, 1-111.
- M. Benning, M. Möller, R. Nossek, M. Burger, D. Cremers, G. Gilboa, C. Schönlieb. “Nonlinear spectral image fusion”,SSVM 2017 proceedings, pages 41-53, volume 10302
- M. Möller, M. Benning, C. Schönlieb, D. Cremers. „Variational depth from focus reconstruction. In: IEEE Transactions on Image Processing”, pages 5369–5378, volume 24(12)
- M. Benning, L. Gladden, D. Holland, C. Schönlieb, T. Valkonen, “Phase reconstruction from velocity-encoded MRI measurements – a survey of sparsity-promoting variational approaches”,Journal of Magnetic Resonance, pages 26-43, volume 238
- M. Benning, M. Burger, "Ground States and Singular Vectors of Convex Variational Regularization Methods", Methods and Applications of Analysis, pages 295-334, volume 20(4)
- M. Benning, M. Burger, C. Brune, J. Müller, "Higher-Order TV Methods: Enhancement via Bregman Iteration", Journal of Scientific Computing, pages 1-42, volume 54(2-3)
- M. Burger, M. Möller, M. Benning, S. Osher, "An Adaptive Inverse Scale Space Method for Compressed Sensing",Mathematics of Computation, pages 269-299, volume 82