My interests revolve around how energy systems can be better designed, implemented, and used to reduce humanity’s impact on the planet. This includes how energy is generated, what it’s used for, and who is using it. A key component of this is evaluating the role of digitalisation and automation to achieve sustainability goals. I therefore work across a range of disciplines – engineering, computer science, behavioural sciences, business management – and utilise a combination of quantitative and qualitative methodologies.

I am currently completing my PhD which looks to improve the quality of data generated from automotive testing facilities. I am approaching this by developing an unsupervised machine learning algorithm for online anomaly detection whilst also conducting an ethnography to identify the influence that human factors and business process have on the testing and development of automotive components. I am evaluating the success of these approaches based on the potential energy saved from minimising redundant testing, which can be approximated into both carbon dioxide emission and financial savings.