I am a Research Engineer in the Data Science team at Seldon Technologies, a company shaping the future of Machine Learning Operations (MLOps). I work on:

• Designing, maintaining, and developing the open-source machine learning libraries Alibi:Explain and Alibi:Detect.

• Supporting research projects in model explainability, outlier and drift detection, model monitoring, and more.

Previously, I was a Research Associate in the Data-Centric Engineering for Aeronautics group at The Alan Turing Institute. My research here explored the use of machine learning and statistical methods for tackling aerospace engineering problems, with a focus on developing approaches with uncertainty quantification and interpretability in mind.

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.