Andrew is a statistician and professor working at the intersection of statistics, machine learning, and real-world scientific applications. His research focuses on developing and applying statistical methods for large, structured datasets, with applications spanning climate science, 3D printing, bioinformatics, and more. He works with a wide array of techniques, including Bayesian hierarchical models, time series analysis, and modern machine learning tools.
Andrew holds the Hamilton Professorship of Statistics at the Hamilton Institute, Maynooth University. He has co-authored over 90 peer-reviewed publications in high-impact journals such as Science, Nature Communications, and PNAS, as well as in leading statistical journals including Statistics and Computing, The Annals of Applied Statistics, JCGS, and JRSS Series C. He has extensive experience teaching Bayesian statistics, statistical learning, and applied modelling across undergraduate, postgraduate, and doctoral levels.
Education & Career
• Hamilton Professor of Statistics, Hamilton Institute, Maynooth University
• PhD in Statistics (Bayesian Methods for Complex Data)
• Internationally published researcher with over 90 peer-reviewed papers
• Active collaborator with interdisciplinary teams in science and engineering
Research Focus
Andrew’s work is centred on statistical methodology and its integration with machine learning for complex, structured data. He is particularly interested in how Bayesian inference and scalable modelling techniques can enhance data-driven research in the natural sciences, engineering, and public policy.
Current Projects
• Hierarchical Bayesian models for environmental and ecological datasets
• Machine learning methods for analysing high-dimensional, structured data
• Time series modelling for dynamic systems in science and industry
• Statistical approaches to reproducible, transparent modelling practices
Professional Consultancy
Andrew collaborates widely across disciplines, providing expert statistical advice on model development, uncertainty quantification, and data analysis pipelines. His applied consulting includes climate modelling, bioinformatics, additive manufacturing, and data-driven public health initiatives.
Teaching & Skills
• Instructor in Bayesian statistics, time series modelling, and machine learning
• Strong advocate for reproducibility, open-source tools, and accessible education
• Skilled in R, Stan, JAGS, and statistical computing for large datasets
• Experienced mentor and workshop leader at all academic levels
Links
• ResearchGate
• Google Scholar
• ORCID
• LinkedIn
• GitHub
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