Biography
Matthew Smith works in the Computational Science Lab at Microsoft Research, and is committed to improving societies (people, businesses, governments) abilities to predict geotemporal phenomena (properties and processes that can be associated with geographical space and time). He has worked in both theoretical and applied ecological science since he left high-school and has come to realise the enormous untapped value in predictive models of ecological and environmental systems and aims to unleash that potential on the world. In recent years he has also discovered so many other geotemporal phenomena that we can predict, anticipate and make decisions about much better than we have done to date, especially in the domains of agriculture, utilities and energy, to name some major business sectors.
Like the rest of his group, he believes that in order to advance our ability to do ecological prediction and forecasting we need new models, new ways to make those models, improved understanding of the systems we're trying to predict and of how much information we need to be able to predict them. In order to achieve these improvements we need to ALSO make fundamental advances in computational science: in how we formulate and constrain complex nonlinear stochastic models, in how we share models and data, in using high performance computing and masses of data, in how we make models and their predictions useful as tools and services. Microsoft Research is a brilliant place to investigate these problems because not only does working here enable us to make those computational breakthroughs to allow us to make the ecological breakthroughs, but those breakthroughs can feedback to benefit the company and human society.
He is currently working on some research projects with UK companies to investigate the value of predictive models of geotemporal phenomena to their businesses. While doing that, he maintains research interests in predicting crop dynamics, carbon and vegetation, human responses to climate change, and ecosystem structure and function.
