Dr Keefe Murphy

Mathematics and Statistics, Hamilton Institute


Logic House
(01) 708 4641


I am a statistician with primary research interests in developing methodologies for both supervised and unsupervised classification tasks involving complex, high-dimensional, mixed-type data, often in the presence of outliers. Publications to date include work on Bayesian nonparametric mixtures of factor analysers, covariate-dependent parsimonious model-based clustering, and risk-stratification for multi-omic prostate cancer data. I am currently working on applications of novel distance-based clustering approaches for longitudinal categorical sequences. I take a keen interest in the computational implementation of statistical models and have authored the R packages IMIFA, MoEClust, and MEDseq, all of which are available on CRAN.

Research Interests

I have a broad range of research interests in the fields of Statistics, Data Science, and Machine Learning including, but not limited to; mixture/latent-variable models, model-based clustering and classification, Bayesian statistics, shrinkage priors, applications to high-dimensional, mixed-type data, computational statistics, dimension reduction, sequence analysis, missing data mechanisms, tree-based methods, non-parametric regression, model selection, model aggregation, and data visualisation. Most of the research projects I have worked on involved a non-trivial computation aspect and I take a keen interest in distributing software implementations of my novel methodologies.

Peer Reviewed Journal

Year Publication
2021 Murphy K.; Murphy T.B.; Piccarreta R.; Gormley I.C. (2021) 'Clustering longitudinal life-course sequences using mixtures of exponential-distance models'. Journal of the Royal Statistical Society. Series A, (Statistics in Society), . [DOI]
2020 Murphy K.; Murphy T.B. (2020) 'Gaussian parsimonious clustering models with covariates and a noise component'. Advances in Data Analysis and Classification, 14 (2):293-325. [DOI] [Full-Text]
2020 Murphy K.; Viroli C.; Gormley I.C. (2020) 'Infinite mixtures of infinite factor analysers'. Bayesian Analysis, 15 (3):937-963. [DOI] [Full-Text]
2018 Murphy K.; Murphy T.B.; Boyce S.; Flynn L.; Gilgunn S.; O'Rourke C.J.; Rooney C.; Stöckmann H.; Walsh A.L.; Finn S.; O'Kennedy R.J.; O'Leary J.; Pennington S.R.; Perry A.S.; Rudd P.M.; Saldova R.; Sheils O; Shields D.C.; Watson R.W.; (2018) 'Integrating biomarkers across omic platforms: an approach to improve stratification of patients with indolent and aggressive prostate cancer'. Molecular Oncology, 12 (9). [DOI]
2020 Gilgunn, S; Murphy, K; Stockmann, H; Conroy, PJ; Murphy, TB; Watson, RW; O' Kennedy, RJ; Rudd, PM; Saldova, R (2020) 'Glycosylation in Indolent, Significant and Aggressive Prostate Cancer by Automated High-Throughput N-Glycan Profiling'. International Journal of Molecular Sciences, 21 . [DOI] [Full-Text]
2020 Jalali A; Foley RW; Maweni RM; Murphy K; Lundon DJ; Lynch T; Power R; O'Brien F; O'Malley KJ; Galvin DJ; Durkan GC; Murphy TB; Watson RW; (2020) 'A risk calculator to inform the need for a prostate biopsy: a rapid access clinic cohort'. BMC Medical Informatics and Decision Making, 20 (1). [DOI] [Full-Text]
2017 Hanrahan K; O'Neill A; Prencipe M; Bugler J; Murphy L; Fabre A; Puhr M; Culig Z; Murphy K; Watson RW; (2017) 'The role of epithelial-mesenchymal transition drivers ZEB1 and ZEB2 in mediating docetaxel-resistant prostate cancer'. Molecular Oncology, 11 (3). [DOI]
2016 Foley RW; Maweni RM; Gorman L; Murphy K; Lundon DJ; Durkan G; Power R; O'Brien F; O'Malley KJ; Galvin DJ; Brendan Murphy T; William Watson R; (2016) 'European Randomised Study of Screening for Prostate Cancer (ERSPC) risk calculators significantly outperform the Prostate Cancer Prevention Trial (PCPT) 2.0 in the prediction of prostate cancer: a multi-institutional study'. BJU International, 118 (5). [DOI]
2016 Foley RW; Gorman L; Sharifi N; Murphy K; Moore H; Tuzova AV; Perry AS; Murphy TB; Lundon DJ; Watson RW; (2016) 'Improving multivariable prostate cancer risk assessment using the Prostate Health Index'. BJU International, 117 (3). [DOI]
2015 Foley RW; Lundon DJ; Murphy K; Murphy TB; Galvin DJ; Watson RW; (2015) 'Predicting prostate cancer: analysing the clinical efficacy of prostate cancer risk calculators in a referral population'. Irish Journal of Medical Science, 184 (3). [DOI]

Computer Program

Year Publication
2020 Murphy, K.; Murphy, T.B.; Piccarreta, R.; Gormley, I.C. (2020) MEDseq: Mixtures of Exponential-Distance Models with Covariates. [Computer Program]
2020 Murphy K.; Murphy T.B. (2020) MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component. [Computer Program]
2020 Murphy K.; Viroli C.; Gormley I.C. (2020) IMIFA: Infinite Mixtures of Infinite Factor Analysers and Related Models. [Computer Program]
Certain data included herein are derived from the © Web of Science (2023) of Clarivate. All rights reserved.

Honors and Awards

Date Title Awarding Body
01/06/2020 Distinguished Dissertation Award The Classification Society


Start date Institution Qualification Subject
University College Dublin Doctor of Philosophy Statistics
University College Dublin Master of Science Statistics
University of Limerick Bachelor of Science Economics & Mathematics

Editorial / Academic Reviews

Amount Role From / To
Annals of Applied Statistics Reviewer -
Computational Statistics and Data Analysis Reviewer -
Journal of Classification Reviewer -
Australian and New Zealand Journal of Statistics Reviewer -
Methodology Reviewer -
Statistics and Computing Reviewer -
The R Journal Reviewer -
Advances in Data Analysis and Classification Reviewer -
Communications in Statistics Part B: Simulation and Computation Reviewer -
Journal of Computational and Graphical Statistics Reviewer -
International Journal of Computer Applications in Technology Reviewer -
Scandinavian Journal of Statistics Reviewer -
Statistical Analysis and Data Mining Reviewer -

Teaching Interests

I am currently lecturing modules on Linear Models, Statistical Computing with R, and Nonparametric Statistics. Other courses I have previously taught include Statistical Machine Learning, Stochastic Processes, and Design & Analysis of Experiments.