Deep Probabilistic Machine Learning

This project will develop scalable approaches to deep non-parametric probabilistic models that use approximate inference techniques to learn the structure of the model. The project requires that the development of practical, interpretable models, with latent variables that can be used by clinicians and non-academics in a meaningful way. We also aim to build distributed user-centric data models, in which the learning occurs across distributed devices, through the paradigm of differential privacy. The successful candidate will be able to demonstrate knowledge of a wide range of machine learning techniques (in particular probabilistic modelling) and practical experience handling data which is noisy, sparse and/or of high dimensionality.

This project is currently ongoing and I am employed as a postdoctoral research associate for a two years fixed-term, ending September 2019. More details will follow.