I am a researcher specialising in Applied Mathematics with a particular focus on Machine Learning Artificial Intelligence and Numerical Analysis.
I am currently working in a variety of areas of Machine Learning Artificial Intelligence and Numerical Analysis. A key interest of mine is the quest for reliable and trustworthy Artificial Intelligence an area which asks important questions regarding whether we can know what the machine has learned and whether we can be mathematically sure even if only on a probabilistic level that such a system will make correct decisions.
Other ongoing areas of interest include the numerical simulation of radiotherapy to optimise patient outcomes and research into new classes of numerical methods for solving systems of partial differential equations using general adaptive polytopic meshes.
(0) For an up to date list of my publications, please see my Google Scholar page at https://scholar.google.co.uk/citations?hl=en&pli=1&user=23pAfUcAAAAJ
Selected recent publications:
Z. Dong, L. Mascotto, O. J. Sutton (2021) "Residual-based a posteriori error estimates for hp-discontinuous Galerkin discretisations of the biharmonic problem" SIAM Journal on Numerical Analysis 59 (3), 1273-1298
A. Cangiani, E. H. Georgoulis, O. J. Sutton (2020) "Adaptive non-hierarchical Galerkin methods for parabolic problems with application to moving mesh and virtual element methods" Mathematical Models and Methods in Applied Sciences 31 (4), 711-751
O. J. Sutton (2020) "Long-time Lâˆž(L2) a posteriori error estimates for fully discrete parabolic problems" IMA Journal of Numerical Analysis 40 (1), 498-521
A. Cangiani, E. H. Georgoulis, A. Y. Morozov, O. J. Sutton (2018) "Revelaing new dynamical patterns in a reaction-diffusion model with cyclic competition via a novel computational framework" Proceedings of the Royal Society A 474(2213) 20170608.
Machine Learning Artificial Intelligence and Numerical Analysis.
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Machine Learning Artificial Intelligence and Numerical Analysis