The graft-versus-host problem…

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Ofer Shamir just submitted a paper on the use of machine learning to represent atmospheric gravity wave momentum transport to QJRMS. He developed an idealized, one-dimensional model of the Quasi-Biennial Oscillation, where we could systematically compare different data-driven methods. In particular, how can one calibrate a data-driven scheme to work in a biased model (the graft-host problem), and how well can schemes generalize to new conditions, as in a climate change scenario?

This was a true team effort, involving Dave Connelly (our regression tree expert), Zihan Shao (our support vector regression guru), and Minah Yang (encoder-decoder extraordinaire) at NYU, with support from Steve Hardimann at the UK MetOffice (our dilated convolutional neural network tsar).