Resume

January 2019 - Present

Computational Research Engineer @ Arrival

As part of a team aiming to vastly improve how products are designed, I am developing the computational engineering and science tools required to achieve this goal.

Mar 2018 - Jan 2019

Postdoctoral Associate @ MIT

Funded by an AFOSR MURI, award number FA9550-15-1-0038

• Developed novel methods for general engineering design using machine learning and Bayesian optimization techniques.

• Applied optimization methods to design under turbulence-based uncertainty, collaborating with Stanford University

Oct 2014 - Mar 2018

PhD Candidate @ University of Cambridge

Funded by an early offer EPSRC DTA, grant number EP/L504920/1

• Developed novel methods for optimization under uncertainty that offer advantages over the state-of-the-art for computational aerospace design

• Extended multi-fidelity uncertainty quantification techniques to work with these methods.

• Applied these methods to aerofoil shape design, aero-structural multi-disciplinary wing design, civil aircraft conceptual design, and hypersonic air-breathing vehicle conceptual design.

• Software tools were developed primarily in python (available on this site here). Also conducted research in MATLAB and C++. 

• Thesis is available here: "Effective Formulations of Optimization Under Uncertainty for Aerospace Design"

Oct 2014 - Mar 2018

Supervisor @ Cambridge University

Taught over 70 undergraduate Engineers (in 1-3 person supervisions) third year mechanics, second year maths, second year mechanics, and first year maths.