Imperial College London

Dr Danielle Belgrave

Faculty of MedicineDepartment of Medicine

Research Fellow in Biomedical Modelling



+44 (0)20 7594 3633d.belgrave CV




Sir Alexander Fleming BuildingSouth Kensington Campus





My main research areas are in latent variable modelling, Bayesian inference, time-to-event analysis, longitudinal data analysis, inference with missing data and endotype discovery in asthma and allergic diseases.

I received an MRC Career Development Award in Biostatistics

Project Title: Unified probabilistic latent variable modelling strategies to accelerate endotype discovery in longitudinal studies

The aim of this fellowship is to develop statistical models which allow us to understand the complexity of asthma and allergic diseases in order to identified more personalized treatment management strategies.

The grand challenge addressed in this project is to advance methods of discovery of the subtypes of complex diseases (which may indicate disease "endotypes") by using 'intelligent phenotypes'. This is applied to disaggregating the complex phenotype of 'asthma' through a series of interlinked specific aims:

(1) To extend the current probabilistic graphical modelling framework by incorporating data from multiple longitudinal birth cohorts (The STELAR consortium)

(2) To up/multi-scale subtype discovery based on clinical data to incorporate genetic, biological and molecular data, using an integrative systems biology approach to endotype discovery

(3) To combine statistical machine learning and hypothesis-driven epidemiological approaches to obtain more refined subtype definitions, employing informatics principles of maximum information utility around clinically important problems

(4) To understand the strengths, weaknesses and complementarities of Bayesian and Frequentist methods for longitudinal analysis of birth cohort data - weighing model uncertainty alongside information utility for causal inference.


show research
  • MRC Career Development Award in Biostatistics (2015 – 2018)  Project: “Unified probabilistic latent variable modelling strategies to accelerate endotype discovery in longitudinal studies”. 
  • Systems Biology Young Investigator's Award (2016)
  • European Academy of Allergy and Clinical Immunology Travel Award (2016)
  • GlaxoSmithKline Exceptional Scientist Award (2015)
  • GlaxoSmithKline Bronze Award (2015)
  • European Academy of Allergy and Clinical Immunology Best Poster Award (2013)
  • Barry Kay Research Award  - British Society of Allergy and Clinical Immunology (2012)
  • European Academy of Allergy and Clinical Immunology Best Presentation Award (2010)
  • Dorothy Hodgkin Postgraduate Award - Microsoft Research PhD Scholarship Programme (2010)


show research
  • Keynote: Machine Learning for Healthcare Conference, Stanford (2018)
  • Tutorial: Machine Learning for Personalised Health. International Conference on Machine Learning, Stockholm (2018)
  • Keynote: Advances in Data Science, Manchester (2018)
  • Keynote: Big Challenges of Big Data – Spanish Allergy Society, Valencia 2018
  • Tutorial: Machine Learning Strategies in Healthcare Research. Deep Learning Indaba, Johannesburg (2017)
  • A Bayesian Predictive Modelling Framework for Endotype Discovery. University of Manchester (2017)
  • Statistical Learning Approaches to Latent Variable Modelling to Accelerate Endotype Discovery. Systems Genomics Group, University of Melbourne, Australia (2016)
  • A Bayesian Approach to Compensating for Missing Data. Missing Data Methods Group, Murdoch Children's Research Institute, Australia (2016)
  • Centre for Epidemiology and Biostatistics, University of Melbourne, Australia (2016)
  • Invited Speaker at the 1st UK Prediction Modelling in Psychiatric Research Workshop, King's College London (2016)
  • Machine Learning to Understand Subtypes of Childhood Wheezing. International Congress on Pediatric Pulmonology, Naples (2016)
  • Workshop: Statistics for the Respiratory Pediatrician. International Congress on Pediatric Pulmonology, Naples (2016)
  • The Asthma E-Lab: Discovering Subtypes of Disease with Model-based Machine Learning. Royal Statistical Society Lancashire and East Cumbria, University of Lancaster (2016)
  • GlaxoSmithKline Biostatistics Annual Conference, London (2015)
  • Machine Learning and Perception Group, Microsoft Research Cambridge (2012)
  • Teaching Assistant:Generalized Linear Latent and Mixed Models. University of Oxford Spring School, Oxford (2010)
  •  Course Assistant: Generalized Linear Latent and Mixed Models. 39th GESIS Spring Seminar: Testing and Modeling with Latent Variables, Cologne (2010)