Andrew McDonald presenting his work on deep learning-based sea ice forecasting at UiT
Image:
Petter Bjørklund

Andrew McDonald presenting his work on deep learning-based sea ice forecasting at UiT

Insightful talks on deep learning-based sea ice forecasting at UiT

Andrew McDonald, a PhD student from University of Cambridge and the British Antarctic Survey, presented his work on sea ice forecasting with diffusion models, as well as the downstream applications of those forecasts‍, at Visual Intelligence in Tromsø.

Insightful talks on deep learning-based sea ice forecasting at UiT

Andrew McDonald, a PhD student from University of Cambridge and the British Antarctic Survey, presented his work on sea ice forecasting with diffusion models, as well as the downstream applications of those forecasts‍, at Visual Intelligence in Tromsø.

By Petter Bjørklund, Communications Advisor at SFI Visual Intelligence

McDonald's work is based deep learning-based sea ice forecasting within the IceNet project: a state-of-the-art sea ice prediction model.

McDonald giving his talk at UiT. Photo: Petter Bjørklund

SFI Visual Intelligence has worked on further developing the IceNet model in close collaboration with the University of Cambridge, BAS, and The Alan Turing Institute.

McDonald's talk was followed by a supplementary presentation by Visual Intelligence PhD student Lars Uebbing, who is a collaborator of McDonald and his colleagues.

Uebbing's talk focused on a recently-published study which sought to train versions of the IceNet with drastically reduced numbers of input features according to results of explainable AI, and investigate the effects on the sea ice predictions.

Uebbing presenting recently published work on the IceNet. Photo: Petter Bjørklund.

The audience got the opportunity to ask McDonald and Uebbing questions about their presented work. Thanks to both for organizing a very informative session!

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