Danielle Belgrave

Biography

My research focuses on integrating expert scientific knowledge to develop statistical machine learning models to understand disease progression over time. The aim is to develop probabilistic models in the context of asthma and allergic disease with approaches which are generalizable to identifying distinct subtypes (endotypes) of disease evolution and understanding the underlying mechanisms of these subtypes. I'm particularly interested in machine learning to identify personalized disease management strategies through understanding the underlying latent manifestations of disease and their distinct genetic and environmental characteristics. I received an MRC Career Development Award in Biostatistics (2015 – 2018) with the project “Unified probabilistic latent variable modelling strategies to accelerate endotype discovery in longitudinal studies”. I was awarded a Microsoft PhD Scholarship and did my PhD at The University of Manchester (2010 – 2013) with Prof Iain Buchan, Prof Christopher Bishop (Microsoft Research Cambridge) and Prof Adnan Custovic. My thesis focused on the development and application of Bayesian machine learning models to understand the aetiology of asthma and allergic disease. Prior to that,I received an MSc in Statistics at University College London and a BSc in Business Mathematics and Statistics at The London School of Economics. Prior to joining Imperial, I worked as a Statistician at GlaxoSmithKline.

Research Interest

asthma