I am a Machine Learning Researcher at Invenia Labs, where my role is to study the dynamics of electricity grids using Bayesian statistics.
My research interests include probabilistic modelling of spatiotemporal data, Gaussian processes, approximate inference and model-based reinforcement learning.
I hold an MSc in Computational Statistics and Machine Learning from the University College London, where my advisor was Professor John Shawe-Taylor.
MSc Computational Statistics and Machine Learning, 2018 - 2019
University College London
BA Computer Science, 2013 - 2016
University of Cambridge
A simple and widely adopted approach to extend Gaussian processes (GPs) to multiple outputs is to model each output as a linear combination of a collection of shared, unobserved latent GPs. An issue with this approach is choosing the number of latent processes and their kernels. These choices are typically done manually, which can be time consuming and prone to human biases. We propose Gaussian Process Automatic Latent Process Selection (GP-ALPS), which automatically chooses the latent processes by turning off those that do not meaningfully contribute to explaining the data. We develop a variational inference scheme, assess the quality of the variational posterior by comparing it against the gold standard MCMC, and demonstrate the suitability of GP-ALPS in a set of preliminary experiments.