Danielle Belgrave

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]

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]

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 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.