Environmental science and neuroscience may seem poles apart as research endeavors, but both are underpinned by the need to analyze and interpret enormous datasets capturing complex spatio-temporal processes. Statistically, looking for patterns and relationships in such datasets is very similar, whether it’s measurements of temperature across the globe or electrical activity throughout the brain. This common purpose has brought together Ying Sun and Hernando Ombao—two of KAUST’s leading researchers in big data statistics.
It all starts with the weather
In environmental monitoring data, each meteorological parameter—temperature, wind speed or precipitation—and each measurement station represents a dimension of the consolidated dataset. The result is a very large dataset with a complexity that defies conventional analytical approaches.
“We focus on developing new statistical methods for analyzing the complex high-dimensional data typically encountered in environmental science,” says Sun.
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