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, causal inference.
Luo Y, Stephens DA, Buckeridge DL, 2021, Bayesian Clustering for Continuous-Time Hidden Markov Models
et al., 2020, Bayesian latent multi-state modeling for nonequidistant longitudinal electronic health records, Biometrics, Vol:77, ISSN:0006-341X, Pages:78-90
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
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