Imperial College London


Faculty of MedicineSchool of Public Health

Honorary Lecturer



+44 (0)20 7594 3347a.m.lewin




157Norfolk PlaceSt Mary's Campus





My main research area is developing Bayesian methods in statistical genomics and epidemiology, in particular Bayesian hierarchical models and variable selection models. I have worked on several Bayesian models for analysing high-throughput molecular biology data, including gene expression microarrays, next-generation RNA-sequence data and metabolomics data. My current research is on methods for data integration and variable selection for multiple "omics" data sets.

I also work on methods in the Classical statistical framework, and apply these methods in genetic epidemiology and medical applications. I am particularly interested in variable selection and multiple testing issues.

I have a background in Mathematics and a PhD in Cosmology, where I worked on detecting non-Gaussianity in the cosmic microwave background and on analysis methods for Type Ia supernovae light curves.

Current Research

  • Statistical methodology: Highly structured stochastic systems; Bayesian hierarchical models; Variable selection and prediction; Bayesian model criticism; Methods for multiple testing.
  • Statistical genomics and genetic epidemiology: Variable selection in high-dimensional modelling of genomics, epigenomics, transcriptomics, proteomics and metabolomics data.
  • Molecular Biology: Statistical methods for modelling high-throughput molecular biology data, including microarray and sequencing data.

See below for Publications, Software and Presentations.


Papers in refereed journals:



Goncalves BP, Procter SR, Clifford S, et al., 2021, Estimation of country-level incidence of early-onset invasive Group B Streptococcus disease in infants using Bayesian methods, Plos Computational Biology, Vol:17, ISSN:1553-734X

Bottolo L, Banterle M, Richardson S, et al., 2021, A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery, Journal of the Royal Statistical Society Series C-applied Statistics, Vol:70, ISSN:0035-9254, Pages:886-908

Alves AC, De Silva NMG, Karhunen V, et al., 2019, GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI, Science Advances, Vol:5, ISSN:2375-2548

Kessy A, Lewin A, Strimmer K, 2018, Optimal Whitening and Decorrelation, American Statistician, Vol:72, ISSN:0003-1305, Pages:309-314

Hinney A, Kesselmeier M, Jall S, et al., 2017, Evidence for three genetic loci involved in both anorexia nervosa risk and variation of body mass index., Mol Psychiatry, Vol:22, Pages:192-201

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