CR
Cryospheric Sciences
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Kim Bente

Kim Bente is a PhD student in the School of Computer Science at The University of Sydney, where she develops probabilistic machine learning models for geoscience and climate applications. Her research focuses on uncertainty-aware methods, such as Gaussian Processes, to advance our understanding of what we know and what we don’t know about the Antarctic ice sheet. This includes inferring Antarctic ice thickness under physical constraints, spatio-temporal modelling of ice loss, efficient sensor placement, and probabilistic causal inference from ice core data. Kim is supervised by Fabio Ramos and Roman Marchant, and is part of the ARC Industrial Transformation Training Centre for Data Analytics for Resources and Environments (DARE; https://darecentre.org.au/).

Did you know? Machine learning can help us understand the cryosphere!

, and November 8, 2024
A bar chart showing in which years how many publications included machine learning.
Google Scholar search results show rapid growth of articles applying ML to the study of the cryosphere. Courtesy of Kim Bente and Julia Kaltenborn.

    Recently, Machine Learning (ML) has emerged as a powerful tool within cryospheric sciences, offering innovative and effective solutions for observing, modelling and understanding the frozen regions of the Earth. From learning snowfall patterns and predicting avalanche dynamics to speeding up the process of modelling ice sheets, ML has transformed cryospheric sciences and bears many o ...[Read More]