Research student (DPhil/PhD) studying industrially focused mathematical modelling (InFoMM).
+44 7793 054 704
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
My DPhil topic is
sponsored by ARM and NAG, supervised by Prof. Mike Giles. Previous research topics have included:
- Bayesian optimisation.
- Machine learning methods.
- Numerical simulations of mortgage products.
- Computational methods for high energy astrophysics.
Some areas of interest include:
- High performance computing.
- Scientific computing.
- Financial mathematics.
- Condensed matter physics and theoretical physics.
- Sports modelling.
- Machine learning and artificial intelligence.
- Data analysis.
However, this list is not exhaustive and there are many other areas I am interested in. Please get in contact if you have a related area of study or work.
Mansfield College, Oxford, OX1 3TF.
- Introduction to markdown languages.
- Fourier series and partial differential equations.
- Special relativity.
- Financial computing with C++.
- Mathematical modelling of financial derivatives.
- Statistics and financial data analysis.
- Exotic derivatives.
- Monte Carlo based numerical methods.
Prizes, awards, and scholarships:
EPSRC InFoMM CDT scholarship (2016).
Oxford university scholarship (2012).
Man Group scholarship (2012).
Major / recent publications:
- February 2021, Analysis of nested multilevel Monte Carlo using approximate Normal random variables, Mike Giles and Oliver Sheridan-Methven, arXiv:2102.08164.
- December 2020, Approximating inverse cumulative distribution functions to produce approximate random variables, Michael Giles and Oliver Sheridan-Methven, arXiv:2012.09715.
- December 2020, Rounding error using low precision approximate random variables, Michael Giles and Oliver Sheridan-Methven, arXiv:2012.09739.
- December 2018, Escaping local minima with derivative-free methods: a numerical investigation, Coralia Cartis, Lindon Roberts, and Oliver Sheridan-Methven, arXiv:1812.11343. (Accepted to the Taylor & Francis journal Optimization).