Sustainable Statistical Computing for Climate-Sensitive Science. This project aims to address the substantial carbon footprint of simulation-based statistical computations underpinning modern science.

Description

Sustainable Statistical Computing for Climate-Sensitive Science. This project aims to address the substantial carbon footprint of simulation-based statistical computations underpinning modern science. Current research focuses on reducing the time-to-result for computations at the expense of energy efficiency. Thus it is not currently possible to scale-up computations to address great environmental challenges without increased contribution to greenhouse gas emissions. Expected project outcomes are new simulation-based inference algorithms designed to be fast, accurate, and energy-efficient. Novel, readily available, low-power computer hardware will be used to demonstrate the future of low-energy statistical computing for climate-sensitive applications in health, environment and sustainability.. Scheme: Discovery Early Career Researcher Award. Field: 4905 - Statistics. Lead: Dr David Warne