Simon completed his PhD in 2019, focusing on generating test cases that effectively stress-test optimisation algorithms and determine their relative strengths and weaknesses. He has a particular interest in optimisation algorithm development, empirical performance analysis, and automated algorithm configuration using machine learning methods.

Simon also has extensive experience applying optimisation and data analysis techniques to real-world problems, including optimisation for road and rail logistics, development of automated tools to analyse operational data from wind farms; and short-term energy production forecasting.