I am a postgraduate research student at the Centre for Doctoral Training in Data Science, part of the School of Informatics at the University of Edinburgh.
I am interested in Bayesian approaches to machine learning and statistical inference. For my PhD I am working on using Bayesian inference for scientific applications that have traditionally presented challenges for probabilistic modelling, as part of Iain Murray's research group. Applications I have worked on include physiological modelling and density estimation for noisy astronomical datasets.
Before moving to Edinburgh I worked as a data scientist and software engineer in London. For my undergraduate degree I studied engineering at the University of Cambridge.
Publications
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Density Deconvolution with Normalizing Flows
*Equal Contribution
Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models Workshop, ICML 2020
[arXiv] [PDF] [Virtual Poster Talk] [Code]
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Scalable Extreme Deconvolution
Machine Learning and the Physical Sciences Workshop, NeurIPS 2019
Talks
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Cside 2018
We won one of the categories in the Cside 2018 competition, inferring the parameters of an ordinary differential equation model of the cardiac action potential. I gave a talk about our solution at the associated conference.
[Slides]
Links
Contact
- Email: james.ritchie@ed.ac.uk
- Office: BC 3.07