Prof. Mike Giles | Mathematical Institute

Prof. Mike Giles

Head of Department

Status:

Academic Faculty

Professor of Scientific Computing

PA Sally Mullins: +44 (0)1865 273530
sally.mullins@maths.ox.ac.uk

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Personal website:

http://people.maths.ox.ac.uk/gilesm/

+44 1865 273534

ORCID iD:

https://orcid.org/0000-0002-5445-3721

Research groups:

Address

Mathematical Institute
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG

Highlighted Publications:

Adaptive Euler-Maruyama method for SDEs with non-globally Lipschitz drift

Adaptive Euler-Maruyama method for SDEs with non-globally Lipschitz drift
Giles, M; Fang, W
Annals of Applied Probability issue 2 volume 30 page 526-560 (8 June 2020)

Multilevel nested simulation for efficient risk estimation

Multilevel nested simulation for efficient risk estimation
Giles, M; Haji-Ali, A
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

Efficient white noise sampling and coupling for multilevel Monte Carlo with non-nested meshes
CROCI, M; GILES, M; Rognes, M; Farrell, P
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

Multilevel estimation of expected exit times and other functionals of stopped diffusions
Giles, M; Bernal, F
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

Algorithm 955: Approximation of the inverse Poisson cumulative distribution function
Giles, M
ACM Transactions on Mathematical Software issue 1 volume 42 (1 March 2016)

Multilevel Monte Carlo Approximation of Distribution Functions and Densities

Multilevel Monte Carlo Approximation of Distribution Functions and Densities
Giles, M; Nagapetyan, T; Ritter, K
SIAM/ASA Journal on Uncertainty Quantification issue 1 volume 3 page 267-295 (21 April 2015)

Multilevel Monte Carlo methods

Multilevel Monte Carlo methods
Giles, M
Acta Numerica volume 24 page 259-328 (1 January 2015)

ANTITHETIC MULTILEVEL MONTE CARLO ESTIMATION FOR MULTI-DIMENSIONAL SDES WITHOUT LEVY AREA SIMULATION

ANTITHETIC MULTILEVEL MONTE CARLO ESTIMATION FOR MULTI-DIMENSIONAL SDES WITHOUT LEVY AREA SIMULATION
Giles, M; Szpruch, L
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

Further analysis of multilevel Monte Carlo methods for elliptic PDEs with random coefficients
Teckentrup, A; Scheichl, R; Giles, M; Ullmann, E
Numerische Mathematik issue 3 volume 125 page 569-600 (1 November 2013)

Multilevel Monte Carlo methods and applications to elliptic PDEs with random coefficients

Multilevel Monte Carlo methods and applications to elliptic PDEs with random coefficients
Cliffe, K; Giles, M; Scheichl, R; Teckentrup, A
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

On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods
Lee, A; Yau, C; Giles, M; Doucet, A; Holmes, C
Journal of Computational and Graphical Statistics issue 19 volume 4 page 769-789 (December 2010)

Multilevel Monte Carlo path simulation

Multilevel Monte Carlo path simulation
Giles, M
Operations Research issue 3 volume 56 page 607-617 (1 May 2008)

Research interests:

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 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 15 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.

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