Danielle Charlotte Belgrave is a Trinidadian-British computer scientist based at DeepMind, who uses statistics and machine learning to understand the progression of diseases. ^{[1] [2] [4]}

Early life and education

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) [ Wikidata] ^{[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]

Research and career

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 personalized prevention strategies. ^[14]

Her research focuses on Bayesian and statistical machine learning within the healthcare to develop personalized medicine. ^[2] As of 2019 ^[update] 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 organizing committee of the Conference on Neural Information Processing Systems ^[16] and as an advisor for DeepAfricAI. ^[17]

References