My research explores the use of machine learning and statistical methods for tackling aerospace engineering problems, with data primarily coming from computational simulations. To encourage industrial uptake, much of my work is focused on developing approaches with uncertainty quantification and interpretability in mind. I am funded by the EPSRC and the Lloyd’s Register Foundation, with previous projects including:
Uncertainty quantification for data-driven turbulence modelling, using Mondrian forests.
Rapid flowfield estimation via embedded ridge functions and deep neural networks.
Using dimension reduction techniques to tackle the curse of dimensionality in aerospace design tasks.
In 2017 I completed a PhD at the University of Cambridge, supervised by Prof. Paul Tucker. This involved working with Rolls-Royce to develop high fidelity turbulence resolving capability in their in-house CFD code. The resulting code was used to run Large Eddy Simulations of the three-dimensional and transitional flows present in gas-turbine compressors. Physical insights gained from these simulations informed compressor-specific turbulence modelling strategies.
I am also a member of the Effective Quadratures organisation. We run workshops on statistics and machine learning for engineers at organisations such as the Culham Centre for Fusion Energy, Rolls-Royce, McLaren Automotive and Siemens. Underpinning this is equadratures, an open-source python library using polynomials for uncertainty quantification, machine learning, optimisation, numerical integration and dimension reduction.