I undertake research in data-centric aeronautics: applying existing, and developing novel, data-driven algorithms in turbomachinery aerothermodynamics and machine learning for better aerodynamic inference and decision-making. My research interests vary from statistical theory and numerical linear algebra to turbomachinery applications. Explore my current projects below.


Bayesian spatial models from engine sensor measurements

Understanding aeroengine aerothermodynamics by generating spatial fields of temperature and pressure based on sensor measurements.

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Subspace-based dimension reduction

Dimension reduction in blade aerodynamics

Finding lower-dimensional representations of high-dimensional design spaces for the aerodynamic design and manufacturing of aeroengine blades.

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Polynomial approximations for machine learning

Leveraging polynomial approximations via least squares, compressive sensing and numerical integration rules for learning.

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