James is interested in the pragmatic application of machine learning to climate physics. Previously, he studied unsupervised ML for isolating the natural variability of our climate. More recently, James has been using cycleGANs to make our climate simulations match observations better. He is also interested in how ML models can be used to replace the nastier parts of weather models.