Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Research Associate







Huxley BuildingSouth Kensington Campus





I am currently a Research Associate in Statistics in the Department of Mathematics, Imperial College London. My principal research focus has been on developing theory, methodology and computational tools to solve emerging problems in biomedicine and science more generally, especially under fully Bayesian settings. Specific areas of interest include biostatistics, mixture models, hidden Markov models, casual inference.



Luo Y, Stephens DA, Verma A, et al., 2020, Bayesian latent multi-state modeling for nonequidistant longitudinal electronic health records, Biometrics, ISSN:0006-341X

Powell GA, Verma A, Luo Y, et al., 2019, Modeling Chronic Obstructive Pulmonary Disease Progression Using Continuous-Time Hidden Markov Models., Stud Health Technol Inform, Vol:264, ISSN:0926-9630, Pages:920-924

Yuan M, Boston-Fisher N, Luo Y, et al., 2019, A systematic review of aberration detection algorithms used in public health surveillance, Journal of Biomedical Informatics, Vol:94, ISSN:1532-0464

Luo Y, Stephens DA, Buckeridge DL, 2018, Estimating prevalence using indirect information and Bayesian evidence synthesis, Canadian Journal of Statistics-revue Canadienne De Statistique, Vol:46, ISSN:0319-5724, Pages:673-689


Verma A, Powell G, Luo Y, et al., Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov models, NeurIPS 2018

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