I'm a PhD student in the Machine Learning Group at Cambridge, supervised by Rich Turner. I'm generally interested in probabilistic modelling and (approximate) inference, and how Gaussian processes should feature in probabilistic programming.
The above feeds directly into my work on probabilistic machine learning in climate science, which addresses combining the predictions of ensembles of GCMs in a sensible way, and the requirements that this task places on statistical weather modelling.
Gaussian process probabilistic programming (GPPP) is a term I've coined for the work I'm doing to re-design the way that we work with GPs in a practical sense. There will be a substantial technical report available here soon explaining what this is all about. In the mean time, I presented the high-level aspects of this work at ProbProg and JuliaCon.
GPAR is a multi-output Gaussian process-based regression model. It's simple, scalable by GP standards, and seems to work really well in practice. It has been accepted to AISTATS 2019, and you can find a Python implementation of the key results here.
Stheno.jl is a Julia implementation of my GPPP work. It's a WIP, but makes working with GPs in problems involving multiple related processes signficantly more straightforward than traditional GP packages.