I'm a PhD student in the Machine Learning Group at Cambridge, supervised by Richard E. Turner. I'm generally interested in probabilistic modelling and (approximate) inference, how Gaussian processes should feature in probabilistic programming, and how to scale GPs for large time series and spatio-temporal problems.
Julia Gaussian Processes (Julia GPs) is home to an ecosystem of packages whose aim is to enable research and modelling using GPs in Julia. It specifies a variety of interfaces, code which implements these interfaces in standard settings, and code built on top of these interfaces (e.g. plotting). The composability and modularity of these interfaces distinguishes it from other GP software. This talk will explore the things that you can currently do with the ecosystem, and where it’s heading.