My interests revolve around how energy systems can be better designed, implemented and used to reduce humanity's impact on the climate. This incorporates a wide span of topics - including engineering, economics and politics, behavioural science and psychology, and environmental sciences - with digitalisation playing an important role across all of these.

My PhD is approaching the challenge by working with an automotive testbed manufacturer - AVL - to develop autonomous anomaly detection. This aims to reduce the number of redundant tests that are run due to both incorrect set up and calibration and, equipment or test unit failure or malfunction. Outputs of my project will include an algorithm informed by machine learning and statistics, a commentary on the impact of human factors on data quality and, an estimation of the cost savings (monetary, energy and carbon) possible by implementing anomaly detection.