Dan joined the UCL Statistical Science department in 2021 after obtaining his PhD in Applied and Computational Mathematics (University College Dublin, Ireland). His research to date has focused on the mathematical and computational modelling of tsunamis, with the goal of mitigating the damage posed by these geophysical events. He has experience with developing highly efficient open source numerical approaches which utilise high performance computing frameworks and machine/statistical learning methods.
numerical solution of partial differential equations, data assimilation, statistical emulation, uncertainty quantification, high performance computing.
- Reguly IZ, Giles D, Gopinathan D, Quivy L, Beck JH, Giles MB, Guillas S & Dias F. (2018) The VOLNA-OP2 tsunami code (version 1.5), Geoscientific Model Development, 11, 4621-4635. https://doi.org/10.5194/gmd-11-4621-2018
- Giles D, Kashdan E, Salmanidou DM, Guillas S & Dias F. (2020) Performance analysis of Volna-OP2 – massively parallel code for tsunami modelling, Computers and Fluids, 209, 104649. https://doi.org/10.1016/j.compfluid.2020.104649
- Giles D, Gopinathan D, Guillas S & Dias F. (2021) Faster than real time tsunami warning with associated hazard uncertainties, Frontiers of Earth Science, 8, 560. https://doi.org/10.3389/feart.2020.597865