Research output per year
Research output per year
Personal profile
Research interests
Research
Sebastian is interested in:
- Solving linear ill-posed inverse problems
- Numerical linear algebra
- Large scale optimisation
- Machine learning approaches to inverse problems
Education
Sebastian graduated from the University of Bath with a first class honors Masters of Mathematics. His masters dissertation, titled "efficient priorconditioning for edge enhancement in imaging", involved the regularisation of discrete ill-posed linear inverse problems and employing Krylov subspace methods that adaptively define edge-enhancing encodings between iterates.
He is currently enrolled on the Integrated PhD Statistical Applied Mathematics, as part of the Statistical Applied Mathematics at Bath (SAMBa) EPSRC CDT. The topic of his PhD concerns how one may use machine learning approaches to solve inverse problems.
Education/Academic qualification
Master of Mathematics, Efficient priorconditioning for edge enhacement in imaging, University of Bath
Oct 2016 → Jul 2020
Award Date: 23 Jul 2020
Keywords
- Inverse Problems
- Optimisation
- Machine Learning
- Numerical Linear Algebra
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Collaborations and top research areas from the last five years
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Flexible Krylov Methods for Edge Enhancement in Imaging
Gazzola, S., Scott, S. & Spence, A., 18 Oct 2021, In: Journal of Imaging. 7, 10, 216.Research output: Contribution to journal › Article › peer-review
Open Access2 Citations (SciVal) -
Efficient gradient-based methods for bilevel learning via recycling Krylov subspaces
Ehrhardt, M. J., Gazzola, S. & Scott, S. J., 11 Dec 2024, arXiv.Research output: Working paper / Preprint › Preprint
File9 Downloads (Pure) -
Majoration-Minimization for Sparse SVMs
Benfenati, A., Chouzenoux, E., Pesquet, J.-C., Franchini, G., Latva-Aijo, S., Narnhofer, D., Scott, S. & Yousefi, M., 3 Oct 2024, Advanced Techniques in Optimization for Machine Learning and Imaging. Springer, Singapore: Springer, Singapore, Vol. 61. p. 31-54 23 p. (Springer INdAM Series; vol. 61).Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding
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On Optimal Regularization Parameters via Bilevel Learning
Ehrhardt, M. J., Gazzola, S. & Scott, S. J., 9 Jan 2024, (Acceptance date) Data-driven models in inverse problems. De Gruyter, 40 p. (Radon Series on Computational and Applied Mathematics - RICAM).Research output: Chapter or section in a book/report/conference proceeding › Book chapter
Open AccessFile34 Downloads (Pure)