Improving Bayesian Neural Network Computation Performance Applied to Health Data

About the Project

Transforming Medical AI: Accelerating Bayesian Neural Networks to provide interpretable, uncertainty-aware healthcare solutions with faster, energy-efficient computational methods from Bayesian Inference.

Deep learning and artificial intelligence (AI) have promised transformative impact in the health sciences: from automatic detection of tumours in medical imaging, to natural language processing of electronic health records. These methods, however, face significant challenges in clinical uptake, partly due to a lack of interpretability around uncertainty of their outputs. Bayesian Neural Networks (BNNs) have shown promise in offering more interpretable model outputs with associated uncertainty estimates by leveraging variational inference and other approximations; however, this comes at a steep computational cost: at prediction, the network must make many forward passes to predict the posterior distribution of the outputs. This sampling of the network not only comes with significant computational cost, but also at an increasingly undesirable energy and environmental cost that drives end for improvements in this area. In statistics, the field of Computational Bayesian Inference has several methods on improving the speed of Bayesian Inference that haven’t yet been applied to BNNs.

In this PhD project, we aim to:

• Review the existing methods and frameworks of improving Bayesian Neural Network performance.
• Translate existing computational improvements in Bayesian Inference to Bayesian Neural Networks
• Explore novel optimisations of Bayesian Neural Network modelling
• Apply these methods to benchmark medical machine learning datasets to show improvements from prior methods

We are looking for students with a background in computer science, mathematics, and/or data science.

Email your CV, cover letter and project title to Samuel Ball: Samuel.ball@liverpool.ac.uk. We will then arrange an informal interview where we can talk about any questions you have about the project before a formal interview as part of the application process.