Danielle Belgrave

Last updated
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.com/en-us/research/people/dabelgra

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]

Contents

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  [ 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 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.

References

  1. 1 2 3 Danielle Belgrave publications indexed by Google Scholar OOjs UI icon edit-ltr-progressive.svg
  2. 1 2 3 4 5 Belgrave, Danielle (2016). "Danielle Belgrave CV" (PDF). imperial.ac.uk. Imperial College London. Archived from the original (PDF) on 2019-03-13.
  3. 1 2 Belgrave, Danielle Charlotte (2014). Probabilistic causal models for asthma and allergies developing in childhood. manchester.ac.uk (PhD thesis). University of Manchester.
  4. 1 2 "Danielle Belgrave". re-work.co. RE•WORK. Retrieved 2019-03-16.
  5. 1 2 "Danielle Belgrave". deeplearningindaba.com. Deep Learning Indaba . Retrieved 2019-03-16.[ permanent dead link ]
  6. 1 2 3 4 5 "Dr Danielle Belgrave". imperial.ac.uk. Imperial College London. Archived from the original on 2018-01-05. Retrieved 2019-03-16.
  7. ↑ Anon (2019). "Advances and Challenges in Machine Learning for healthcare Seminar". datascience.manchester.ac.uk. University of Manchester. Retrieved 2019-03-16.[ permanent dead link ]
  8. 1 2 "Danielle Belgrave". cipp-meeting.org. CIPP XV. Archived from the original on 2016-08-24. Retrieved 2019-03-16.
  9. ↑ "Unified probabilistic latent variable modelling strategies to accelerate endotype discovery in longitudinal studies". ukri.org. United Kingdom Research and Innovation . Retrieved 2019-03-16.
  10. ↑ "Danielle Belgrave at Microsoft Research". microsoft.com. Microsoft Research. Archived from the original on 2019-03-17. Retrieved 2019-03-16.
  11. ↑ Anon (2017-09-15), "12 Applications of Machine Learning in Healthcare by Danielle Belgrave", youtube.com, Deep Learning Indaba , retrieved 2019-03-16
  12. ↑ Anon (2019-03-07). "Ethical AI". robotethics.co.uk. AI and Robot Ethics. Retrieved 2019-03-16.
  13. 1 2 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.
  14. 1 2 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. Closed Access logo transparent.svg
  15. 1 2 "Regulating algorithms in healthcare: IP and liability". phgfoundation.org. PHG Foundation. Retrieved 2019-03-16.
  16. ↑ "2019 Organizing Committee". nips.cc. Retrieved 2019-03-16.
  17. ↑ "2020 Organizing Committee". nips.cc. Retrieved 2025-12-04.
  18. ↑ "2022 Organizing Committee". nips.cc. Retrieved 2025-12-04.
  19. ↑ "2024 Organizing Committee". nips.cc. Retrieved 2025-12-04.
  20. ↑ "2025 Committees". nips.cc. Retrieved 2025-12-04.
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