Simulations of, and computational models for complex stochastic processes, in particular for engineering magnetic nanoparticles, modelling of complex systems in physics / engineering, general probability and stochastic process theory, development of computational models and software in engineering.
I completed my MEng in Aerospace Engineering at The University of Sheffield and worked on collaborative projects with QinetiQ and Rolls Royce on gas turbine engine prognostics – making predictions of useful service life from in-flight data in noisy environments.
My current research focuses on numerical approaches to modelling the dynamics of thermally fluctuating magnetic nanoparticles in order to better understand the magnetic relaxation process of interacting ensembles of particles. Due to the broad distribution of characteristic time scales observable in these systems, direct computation of non-equilibrium dynamics at long time scales is computationally prohibitive.
Therefore, one must fall back on approximation methods to determine the long time behaviour of the stochastic processes. We are investigating the validity of these approximation methods and under what assumptions they may be used. We hope that by developing efficient computational methods for simulating magnetic nanoparticle systems, we can extend the horizon beyond which we can make predictions of nanoparticle behaviour.
A key application of this research is in magnetic drive storage, where manufacturers hope to determine the reliability of their hard drives on the time scale of years. Engineering magnetic nanoparticles also has applications to bio-sensing, where they could offer improved contrast in medical imaging, compared to MRI.
In relation to this, I also have a continued interest in high performance computing and developing algorithms to handle large parallel simulations in computational engineering.