Will is a PhD student in the Machine Learning Group at the University of Cambridge, supervised by Rich Turner. He's generally interested in probabilistic modelling and (approximate) inference, and is particularly fond of Gaussian processes (GPs). His work on GPs includes approximate inference for scaling to large problems, their use in both multi-output regression and the ensembling of climate models, and most recently on how best to exploit their unique properties in a probabilistic programming framework.
Gaussian Process Probabilistic Programming with Stheno.jl
Stheno.jl is a probabilistic programming framework specifically designed for constructing probabilistic models based around Gaussian processes. Come to this talk to find out what that means, why you should care, and how you can use it with Flux.jl and Turing.jl to do cool things.