July 26, 2020
Improving predictions by integrating Data Science and Domain Science in Resources and the Environment
Edward Cripps, deputy director of DARE centre (Data Analytics for Resources and Environments), is an Associate Professor in the Department of Mathematics and Statistics at the University of WA. His research interests are in Bayesian longitudinal analysis and spatio-temporal models, and the integration of statistical and physical models. His primary applications are in the statistical modelling of environmental, meteorological and oceanographic processes and their interaction with engineering decision making and asset management. Edward has extensive experience in industry collaboration and translating academic research output into commercially industrial applications. He is currently working in collaboration with Shell, Woodside, Lloyds and the Alan Turing Institute, the UK’s national data science institute.
Over the last decade there has been a rush to use Data Science applications in areas such as Geology, Ecology and Hydrology. However, in many cases this has either been a relatively limited application of Data Science tools within a complex Domain Science problem, or a more theoretical Data Science approach on a very limited Domain Science problem. In other words, in most cases the Domains simply borrow techniques or problems. This can be seen as a relatively weak integration of the two domains. This session seeks contributions of examples of strong integration, where problems of Data Science and Domain Science are co-developed or are working towards co-developed problems. Contributions highlighting issues and barriers related to such co-development are also encouraged.