معرفی
Keefe Murphy is a Lecturer in Statistics within the Department of Mathematics and Statistics at Maynooth University, Faculty of Science & Engineering, and is affiliated with the Hamilton Institute. He is an active researcher in statistical machine learning, Bayesian nonparametrics, and clustering/classification of complex, high-dimensional data.
Education:
- PhD in Statistics, University College Dublin
- MSc in Statistics, University College Dublin
- BSc in Economics & Mathematics, University of Limerick
Research Interests:
His work centres on developing and extending statistical methodologies for supervised and unsupervised learning, with emphasis on mixture models, latent variable models, Bayesian nonparametrics, and computational statistics. He actively contributes novel algorithms and software implementations, including the R packages IMIFA, MoEClust, and MEDseq available on CRAN. Current projects include extensions to Bayesian Additive Regression Trees (BART), handling missing data, modelling multivariate count data, and variable selection in model-based clustering.
Publication Profile:
His recent publications (2021–2025) demonstrate a clear trajectory in advancing Bayesian machine learning methods, with contributions to Gaussian process BART models, sparse factor analysis, and educational data mining. Collaborative work spans learning analytics and multi-omic prostate cancer biomarker discovery, illustrating broad interdisciplinary impact.
Awards & Recognition:
- Distinguished Dissertation Award (The Classification Society, 2020)
Service & Advising:
He serves as Associate Editor for Statistical Analysis and Data Mining and on departmental committees (Course Committee, PR Committee). He has successfully supervised PhD student Mateus Maia (graduated 2024) and currently teaches modules such as Advanced R Programming, Introduction to Data Science, and Nonparametric Statistics.
Labs & Collaborations:
He is affiliated with the Hamilton Institute, which fosters interdisciplinary research in applied mathematics and statistics, providing a collaborative environment for advancing computational and methodological statistics.
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