Will Tebbutt
I am a postdoc at the Alan Turing Institute in London, having previously been a postdoc and PhD student in the Machine Learning Group in Cambridge. I am interested in probabilistic programming, Gaussian processes, algorithmic differentiation, and machine learning for weather forecasting.
I have been a Julia user for a while. I have worked on the algorithmic differentiation ecosystem (Zygote.jl, ChainRules.jl, and Mooncake.jl), the various packages in the JuliaGaussianProcesses organisation. I have also been working closely with the Turing.jl team.
Intervention
Mooncake.jl is an algorithmic differentiation (AD) tool written in Julia. It is characterised by support for a wider range of Julia language features than existing tools, and performance which is typically better than comparable tools written in Julia. It has extensive documentation, simple-to-use tools for correctness testing supported by a precise type system, and is best used via DifferentiationInterface.jl. In this talk I will attempt to both justify and qualify these claims, and will conclude with an opinionated outlook on the future of AD in Julia.