Simon pursues a thoroughly multi-disciplinary research agenda as a complexity and data scientist. In Economics, he is thematically interested in technology, innovation and non-equilibrium economics. Largely he uses computational and data science techniques to study socio-economic, biological, or physical phenomena. He has worked on diverse complex systems applications such as evolutionary models of joint-intentionality, finite-state automata models of perpetualy novelty, self-organisation in polymer films, complex neural regulation of endurance pacing, and cellular automata models of tumor progression. He also has a strong interest in networks and data science, including big data ananalysis and visualisation. Additionally, he has brought complexity thinking to sustainable development through his educational activities.

Simon is a founding member of SoDa Labs, an empirical research laboratory associated with Monash University's Department of Economics and Department of Econometrics in the Monash Business School. SoDa Labs applies new tools from data science, machine learning, and beyond to answer social science questions using alternative and big data.

Simon returned to Melbourne, where he grew up, to join the Department of Economics, Monash University, in 2008 after spending a decade at UNSW for undergraduate and postgraduate studies across Arts, Science and Economics. During this time he also benefitted from stints at the Santa Fe Institute (SFI) where his interest in the Science of Complexity was cultivated. Simon's interest in computational and algorithmic thinking started very early with much time spent on the family's first computer from the age of five, since then, he has continued to pursue the art of computational and data science, embracing a truly multi-disciplinary program of research and teaching, leveraging his diverse background. Outside of university, Simon: serves City on a Hill, an Anglican church in Melbourne, as a lay-pastor for strategy and analysis; enjoys time with his wife and their three kids; and pursues endurance trail-running competition.

Simon welcomes research supervision interest in any of his research areas. Students should normally expect to work on algorithmic, computational, or data science works within the complexity paradigm. Simon is open to applications across the science domains, though some aspect of the work should ordinarily have an economics flavour.