My research focuses on integrating expert scientific knowledge to develop statistical machine learning models to understand disease progression over time. The aim is to develop probabilistic models in the context of asthma and allergic disease with approaches which are generalizable to identifying distinct subtypes (endotypes) of disease evolution and understanding the underlying mechanisms of these subtypes. I'm particularly interested in machine learning to identify personalized disease management strategies through understanding the underlying latent manifestations of disease and their distinct genetic and environmental characteristics. I received an MRC Career Development Award in Biostatistics (2015 – 2018) with the project “Unified probabilistic latent variable modelling strategies to accelerate endotype discovery in longitudinal studies”.
I was awarded a Microsoft PhD Scholarship and did my PhD at The University of Manchester (2010 – 2013) with Prof Iain Buchan, Prof Christopher Bishop (Microsoft Research Cambridge) and Prof Adnan Custovic. My thesis focused on the development and application of Bayesian machine learning models to understand the aetiology of asthma and allergic disease. Prior to that,I received an MSc in Statistics at University College London and a BSc in Business Mathematics and Statistics at The London School of Economics. Prior to joining Imperial, I worked as a Statistician at GlaxoSmithKline.
I am a collaborator on the Study Team for Early Life Asthma Research (STELAR), a consortium of birth cohorts across the UK. The aims of this project are to develop a web-based Asthma e-Lab which combines rich phenotypic data across these birth cohorts and to develop innovative computational statistical methods to identify novel endotypes of childhood asthma, enabling investigation of and discovery of endotype-specific pathophysiological mechanism.
et al., 2016, Disaggregating asthma: big Investigation vs. big data, Journal of Allergy and Clinical Immunology, Vol:139, ISSN:1097-6825, Pages:400-407
et al., 2016, Distinguishing benign from pathologic TH2 immunity in atopic children., J Allergy Clin Immunol, Vol:137, Pages:379-387
et al., 2016, Age, sex and the association between skin test responses and IgE titres with asthma, Pediatric Allergy and Immunology, Vol:27, ISSN:0905-6157, Pages:313-319
et al., 2014, Trajectories of lung function during childhood., Am J Respir Crit Care Med, Vol:189, Pages:1101-1109
et al., 2013, Joint modeling of parentally reported and physician-confirmed wheeze identifies children with persistent troublesome wheezing., J Allergy Clin Immunol, Vol:132, Pages:575-583.e12
et al., 2014, A genome-wide association study identifies CDHR3 as a susceptibility locus for early childhood asthma with severe exacerbations., Nat Genet, Vol:46, Pages:51-55
et al., 2014, Assessing the association of early life antibiotic prescription with asthma exacerbations, impaired antiviral immunity, and genetic variants in 17q21: a population-based birth cohort study, Lancet Respiratory Medicine, Vol:2, ISSN:2213-2600, Pages:621-630
et al., 2014, Developmental profiles of eczema, wheeze, and rhinitis: two population-based birth cohort studies., Plos Med, Vol:11
et al., 2015, Patterns of IgE responses to multiple allergen components and clinical symptoms at age 11 years., J Allergy Clin Immunol, Vol:136, Pages:1224-1231
et al., 2015, Evolution pathways of IgE responses to grass and mite allergens throughout childhood., J Allergy Clin Immunol, Vol:136
et al., 2015, Relation between circulating CC16 concentrations, lung function, and development of chronic obstructive pulmonary disease across the lifespan: a prospective study., Lancet Respir Med, Vol:3, Pages:613-620
et al., 2015, Atopic dermatitis and respiratory allergy: what is the link, Current Dermatology Reports, Vol:4, ISSN:2162-4933, Pages:221-227