Wallis Professor of Mathematics
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
System control and rough paths
ISBN-10: 0-19-850648-1 (2002)
High order recombination and an application to cubature on Wiener space
Annals of Applied Probability volume 22 page 1301-1327 (2012)
Backward stochastic dynamics on a filtered probability space
Annals of Probability issue 4 volume 39 page 1422-1448 (1 July 2011)
Expected signature of two dimensional Brownian Motion up to the first exit time of the domain
Integrability Estimates for Gaussian Rough Differential Equations
Uniqueness for the signature of a path of bounded variation and the reduced path group
Annals of Mathematics. Second Series volume 171 page 109-167 (2010)
Universal Approximation with Deep Narrow Networks
(6 July 2020)
An optimal polynomial approximation of Brownian motion
SIAM Journal on Numerical Analysis
The signature-based model for early detection of sepsis from electronic health records in the intensive care unit
2019 Computing in Cardiology (CinC) volume 46 (24 February 2020)
Numerical Method for Model-free Pricing of Exotic Derivatives in Discrete Time Using Rough Path Signatures
Applied Mathematical Finance (18 February 2020)
Deep signature transforms
Advances in Neural Information Processing Systems 32 volume 32 page 3082-3092 (10 December 2019)
I am the Wallis Professor of Mathematics; I was a founding member (2007) of, and then Director (2011-2015) of, the Oxford Man Institute of Quantitative Finance; I was the Director of the Wales Institute of Mathematical and Computational Sciences (WIMCS; 2008-2011). I came to Oxford in 2000 having previously been Professor of Mathematics at Imperial College London (1993-2000), and before that I held the Colin Maclaurin Chair at Edinburgh (1985-93).
My long-term research interests are all focused on Rough Paths, Stochastic Analysis, and applications - particularly to Finance and more generally to the summarsing of large complex data. That is to say I am interested in developing mathematical tools that can be used to effectively model and describe high dimensional systems that exhibit randomness. This involves me in a wide range of problems from pure mathematical ones to questions of efficient numerical calculation.
Stochastic analysis. This is the area of mathematics relating to the rigorous description of high-dimensional systems that have randomness. It is an area of wide-reaching importance. Virtually all areas of applied mathematics today involve considerations of randomness, and a mobile phone would not work without taking advantage of it. Those who provide fixed-rate mortgages have to take full account of it. My interests are in identifying the fundamental language and the basic results that are required to model the interaction between highly oscillatory systems where the usual calculus is inappropriate. If you google ‘Rough Paths’ and ‘Lyons’ you will find further information. My St Flour Lecture notes provide a straightforward technical introduction with all the details put as simply as possible. A more general introduction can be found in my talk/paper to the European Mathematical Society in Stockholm in 2002.
My approach is that of a pure mathematician, but my research has consequences for numerical methods, finance, sound compression and filtering. At the moment I am (speculatively) exploring their usefulness in understanding sudden shocks on dynamical systems, and also trying to understand the implications for geometric measure theory. The focus of my research directed to ‘Rough paths’ can be viewed as a successful approach to understanding certain types of non-rectifiable currents.
I actively look for applications in the mathematics I do, but my experience has led me to believe strongly in the importance of being rigorous in the development of the core mathematical ideas. For me, the word proof is synonymous with the more palatable ‘precise, convincing and detailed explanation’, and I believe it is important, even essential, to find rigorous proofs of the key mathematical intuitions so that mathematics can reliably grow and ideas can be passed on to the next generation.