Professor of Scientific Computing
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Adaptive Euler-Maruyama method for SDEs with non-globally Lipschitz drift
Annals of Applied Probability issue 2 volume 30 page 526-560 (8 June 2020)
Multilevel nested simulation for efficient risk estimation
SIAM/ASA Journal on Uncertainty Quantification issue 2 volume 7 page 497-525 (2 May 2019)
Efficient white noise sampling and coupling for multilevel Monte Carlo
with non-nested meshes
SIAM/ASA Journal on Uncertainty Quantification issue 4 volume 6 page 1630-1655 (20 November 2018)
Multilevel estimation of expected exit times and other functionals of stopped diffusions
SIAM/ASA Journal on Uncertainty Quantification issue 4 volume 6 page 1454–1474- (18 October 2018)
Algorithm 955: Approximation of the inverse Poisson cumulative distribution function
ACM Transactions on Mathematical Software issue 1 volume 42 (1 March 2016)
Multilevel Monte Carlo methods
Acta Numerica volume 24 page 259-328 (1 January 2015)
Multi-level Monte Carlo approximation of distribution functions and densities
SIAM/ASA Journal on Uncertainty Quantification issue 1 volume 3 page 267-295 (1 January 2015)
ANTITHETIC MULTILEVEL MONTE CARLO ESTIMATION FOR MULTI-DIMENSIONAL SDES WITHOUT LEVY AREA SIMULATION
ANNALS OF APPLIED PROBABILITY issue 4 volume 24 page 1585-1620 (August 2014) Full text available
Further analysis of multilevel Monte Carlo methods for elliptic PDEs with random coefficients
Numerische Mathematik issue 3 volume 125 page 569-600 (1 November 2013)
Multilevel Monte Carlo methods and applications to elliptic PDEs with random coefficients
Computing and Visualization in Science issue 1 volume 14 page 3-15 (1 January 2011)
On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods
Journal of Computational and Graphical Statistics issue 19 volume 4 page 769-789 (December 2010)
Multilevel Monte Carlo path simulation
Operations Research issue 3 volume 56 page 607-617 (1 May 2008)
In my early career, I worked at MIT and in the Oxford University Computing Laboratory on computational fluid dynamics applied to the analysis and design of gas turbines, but more recently I have moved into computational finance and research on Monte Carlo methods for a variety of applications.
My research focus is on improving the accuracy, efficiency and analysis of Monte Carlo methods. A particular highlight has been the development and numerical analysis of multilevel Monte Carlo methods; this has been the basis of much of my research in the past 10 years and has stimulated a lot of research elsewhere.
I am also interested in various aspects of scientific computing, including high performance parallel computing, and I have worked extensively on the exploitation of many-core GPUs for a variety of applications.
For more details please see my website.