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]
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 edit
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]
Research and career edit
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 edit
- ^ a b c Danielle Belgrave publications indexed by Google Scholar
- ^ a b c d e Belgrave, Danielle (2016). "Danielle Belgrave CV" (PDF). imperial.ac.uk. Imperial College London. Archived from the original (PDF) on 2019-03-13.
- ^ a b Belgrave, Danielle Charlotte (2014). Probabilistic causal models for asthma and allergies developing in childhood. manchester.ac.uk (PhD thesis). University of Manchester.
- ^ a b "Danielle Belgrave". re-work.co. RE•WORK. Retrieved 2019-03-16.
- ^ a b "Danielle Belgrave". deeplearningindaba.com. Deep Learning Indaba. Retrieved 2019-03-16.
- ^ a b c d e "Dr Danielle Belgrave". imperial.ac.uk. Imperial College London. Archived from the original on 2018-01-05. Retrieved 2019-03-16.
- ^ Anon (2019). "Advances and Challenges in Machine Learning for healthcare Seminar". datascience.manchester.ac.uk. University of Manchester. Retrieved 2019-03-16.
- ^ a b "Danielle Belgrave". cipp-meeting.org. CIPP XV. Retrieved 2019-03-16.
- ^ "Unified probabilistic latent variable modelling strategies to accelerate endotype discovery in longitudinal studies". ukri.org. United Kingdom Research and Innovation. Retrieved 2019-03-16.
- ^ "Danielle Belgrave at Microsoft Research". microsoft.com. Microsoft Research. Archived from the original on 2019-03-17. Retrieved 2019-03-16.
- ^ Anon (2017-09-15), "12 Applications of Machine Learning in Healthcare by Danielle Belgrave", youtube.com, Deep Learning Indaba, retrieved 2019-03-16
- ^ Anon (2019-03-07). "Ethical AI". robotethics.co.uk. AI and Robot Ethics. Retrieved 2019-03-16.
- ^ a b Custovic, Adnan; Henderson, A. John; Buchan, Iain; Bishop, Christopher; Guiver, John; Simpson, Angela; Granell, Raquel; Belgrave, Danielle C. M. (2014). "Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies". PLOS Medicine. 11 (10): e1001748. doi:10.1371/journal.pmed.1001748. ISSN 1549-1676. PMC 4204810. PMID 25335105.
- ^ a b Bønnelykke, Klaus; Sleiman, Patrick; Nielsen, Kasper; Kreiner-Møller, Eskil; Mercader, Josep M; Belgrave, Danielle; den Dekker, Herman T; Husby, Anders; Sevelsted, Astrid; Faura-Tellez, Grissel; Mortensen, Li Juel; et al. (2013). "A genome-wide association study identifies CDHR3 as a susceptibility locus for early childhood asthma with severe exacerbations". Nature Genetics. 46 (1): 51–55. doi:10.1038/ng.2830. ISSN 1061-4036. OCLC 885448463. PMID 24241537. S2CID 20754856.
- ^ a b "Regulating algorithms in healthcare: IP and liability". phgfoundation.org. PHG Foundation. Retrieved 2019-03-16.
- ^ "2019 Organizing Committee". nips.cc. Retrieved 2019-03-16.
- ^ "DeepAfricAI". deepafricai.com. Retrieved 2019-03-16.
