Grant is a PhD student in the Department of Geography at the University of Nottingham, with an interest in using Earth Observation to understand ecological trends. His research is based in the field of macroecology, exploring the relationship between environmental drivers and functional biodiversity. Specifically, his research characterises variability within the geosphere – geological, hydrological, geomorphological and pedological drivers – to understand whether these drivers can be used to predict biodiversity patterns. To do this, Grant uses remote sensing, geospatial analytics and machine learning combined with large geospatial datasets to explore these relationships across broad spatial extents.
Grant’s Turing related research involves using multivariate geospatial modelling and machine learning to determine whether environmental indicators can train Artificial Intelligence (AI) to predict patterns of biodiversity. Biodiversity can be challenging to measure directly, and therefore Grant’s research aims to understand whether machine learning can provide insight into identifying biodiverse systems and creating priorities for conservation planning.