Danielle Belgrave
Trinidadian-British computer scientist
From Wikipedia, the free encyclopedia
Danielle Charlotte Belgrave is a Trinidadian-British computer scientist whose research uses machine learning to understand disease progression and healthcare personalisation. She is VP of AI/ML at GSK where she heads the AI/ML Clinical Development Team. She was previously tenured at DeepMind, Microsoft Research and Imperial College.[1][2][4]
University College London (MSc)
University of Manchester (PhD)
Danielle Belgrave | |
|---|---|
| Born | Danielle Charlotte Belgrave |
| Alma mater | London School of Economics (BSc) University College London (MSc) University of Manchester (PhD) |
| Scientific career | |
| Fields | Statistics Machine learning[1] |
| Institutions | DeepMind Microsoft Research Imperial College London GlaxoSmithKline |
| Thesis | Probabilistic causal models for asthma and allergies developing in childhood (2014) |
| Doctoral advisor | Iain Buchan Christopher Bishop Adnan Custovic[2][3] |
| Website | microsoft |
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 mathematics and statistics 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[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 faculty after receiving a research fellowship award from the Medical Research Council (MRC) 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 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 early research interests include latent variable models, longitudinal studies, survival analysis, 'omics, dimensionality reduction and Probabilistic graphical models.[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 served on the organizing committee of the Conference on Neural Information Processing Systems in 2019,[16] 2020,[17] 2022,[18] 2024,[19] and 2025.[20] She was General Chair of NeurIPS 2025 and is a member of the NeurIPS Board.
