Danielle Belgrave

Belgrave grew up in Trinidad and Tobago, where her high school mathematics teacher inspired her to work as a data scientist.[5] She studied statistics and business at the London School of Economics (LSE).[6][7] She was a graduate student at University College London (UCL), where she earned a master's degree in statistics.[6] In 2010 Belgrave moved to the University of Manchester, where she earned a PhD for research supervised by Iain Buchan, Christopher Bishop and Adnan Custovic (scientist)[2][3][6] supported by a Microsoft Research scholarship. She was awarded a Dorothy Hodgkin postgraduate award by Microsoft and the Barry Kay Award by the British Society of Allergy and Clinical Immunology (BSACI).[8]

After graduating, Belgrave worked at GlaxoSmithKline (GSK), where she was awarded the Exceptional Scientist Award.[6] Belgrave joined Imperial College London as a Medical Research Council (MRC) statistician in 2015.[6][9][8] She develops statistical machine learning models to look at disease progression in an effort to design new management strategies and understand heterogeneity.[4][10] Statistical learning methods can inform the management of medical conditions by providing a framework for endotype discovery using probabilistic modelling.[5][11] She uses statistical models to identify the underlying endotypes of a condition from a set of phenotypes.[12]

She studied whether atopic march, the progression of allergic diseases from early life, adequately describes atopic diseases like eczema in early life.[13] Belgrave used a latent disease profile model to study atopic march in over 9,000 children, where machine learning was used to identify groups of children with similar eczema onset patterns.[13] She is part of the study team for early life asthma research consortium.[14] Belgrave is interested in using big data for meaningful clinical interpretation, to inform personalised prevention strategies.[14]

Her research focuses on Bayesian and statistical machine learning within the healthcare to develop personalised medicine.[2] As of 2019 Belgrave is developing and implementing methods which incorporate domain knowledge with data-driven models. Her research interests include latent variable models, longitudinal studies, survival analysis, ‘omics, dimensionality reduction, Bayesian graphical models and cluster analysis.[2][1]

Belgrave is part of the regulatory algorithms project, which evaluates how healthcare algorithms should be regulated.[15] In particular, Belgrave is interested in what scheme of liability should be imposed on artificial intelligence for healthcare.[15] She serves on the 2019 organising committee of the Conference on Neural Information Processing Systems[16] and as an advisor for DeepAfricAI.[17]