I live in London and work at the University of Cambridge and The Alan Turing Institute. I studied computational engineering at Cambridge and aerospace engineering previously at Maryland. During my doctorate I worked at Rolls-Royce plc in Derby and spent time at Stanford. My research focuses on uncertainty quantification, optimisation
and physics informed machine learning to address challenges in aerothermodynamics. I am the founder of Effective Quadratures.
My current research projects (and interests) are:
Bayesian models for improved aerothermal flow-physics in engines.
Subspace-based dimension reduction: algorithms and 3D blade design.
Polynomial approximations in uncertainty quantification, machine learning and optimization.
Empowering next-generation digital twins in virtual reality.
Machine learning techniques for extracting data-driven physical insight in flow simulations.
For all my publications, please check out Google Scholar and Researchgate.
For open-source codes, visit my Github profile. For my CV, click here.
If you are really interested in my research, check out the videos below. The first one is a high-level research talk, while the other two are initial results of my team's ongoing virtual reality work.
Ashley Scillitoe (Postdoctoral fellow): Physics informed machine learning in computational fluid dynamics.
Chun Yui Wong (PhD student): Embedded ridge approximations for improved computational aerodynamic inference.
James Gross (PhD student): Techniques in polynomial subspace projections for design optimisation.
Slawomir Konrad Tadeja (PhD student, co-advised by Per Ola Kristensson): Digital twinning an aeroengine in virtual reality.