Leading investigation into novel Bayesian Machine learning techniques. Extending Bayesian neural networks with principled Bayesian model comparisons. Rethinking approaches to neural networks and machine learning that focus on explainability and interpretability.Working in the Kavli Institute for Cosmology and partnered with a data science start up, taking the state of the art Bayesian inference tools for cosmology to a range of cutting edge industry challenges. Demonstrating application of scalable model selection to cutting edge problems in computer vision, particle physics and Cosmology.

Associated Researcher with a Cambridge University spin-out Data Science company. Building next generation data science tools and Bayesian machine learning solutions

Research Assistant in experimental and phenomenological High Energy Physics. Building public software releases, supervision of masters student projects and continuing research and development of simulation software with machine learning techniques.

PhD student in High Energy Physics specialising in calculations at the interface of experiment and theory. Writing open source research software for statistical data analysis of High Energy Physics experiments

Implementation and support consultant for investment management software. Developing SQL and SWIFT workflows.