William Gregory: Improving Arctic Sea Ice Predictability with Gaussian Processes

Bio

Will recently completed his PhD within the Centre for Polar Observation and Modelling (CPOM) group at UCL, where he explored using various machine learning tools as ways to improve the predictability of Arctic sea ice on seasonal time scales. Prior to this, his background was in Geophysics, where he spent 3 years within the energy sector exposing him to various aspects of time-series analysis, data processing, and tomographic inversion methods for sub-surface imaging & modeling. Will is due to begin a postdoctoral research position within the Geophysical Fluid Dynamics Lab at Princeton University, where he will continue in the theme of machine learning, although now working towards improving the representation of sea ice in global climate models.