Paul McNicholas is the Canada Research Chair in Computational Statistics and a Professor in the Department of Mathematics & Statistics at McMaster University. His research focuses on computational statistics, and he is at the cutting edge of international research on mixture model-based clustering and classification. Current research includes work on non-Gaussian mixtures, big data analytics, and clustering in the presence of outlying or spurious points. He has published extensively in the field, and is an associate editor for several international statistics journals. Over the past few years, Paul has led the largest computational statistics research group in Canada. To date, he has completed the training of nine postdoctoral research fellows, a research associate, nine Ph.D. students, and 37 Master’s students. His research group currently comprises eight Ph.D. students and six Master's students.
Paul's research group has enjoyed support from several sources including NSERC (Collaborative Research & Development and Discovery grants), the Government of Ontario (Small Infrastructure Fund), CFI (Leaders Opportunity Fund), OCE (Collaborative Research), and Compusense Inc. Paul won an Early Researcher Award from the Government of Ontario, the Chikio Hayashi Award from the International Federation of Classification Societies, and the Barrington Medal from the Statistics and Social Inquiry Society of Ireland. Paul recently spent two years as chair of the Statistics section of NSERC Evaluation Group 1508: Mathematics and Statistics and is a member of the ICT, Math and Physics panel for the Ontario Early Researcher Awards.
Paul welcomes enquiries from strong students interested in joining his group at the Master's or Ph.D. level. The best way to contact him is by email.
Research monograph Mixture Model-Based Classification published by Chapman & Hall/CRC Press in August 2016. More information here.
Recent open access publications:
Franczak, B.C., Browne, R.P., McNicholas, P.D. and Findlay, C.J. (2015), 'Product Selection for Liking Studies: The Sensory Informed Design', Food Quality and Preference 44, 36-43. [doi]
Murray, P.M., Browne, R.P. and McNicholas, P.D. (2014), 'Mixtures of skew-t factor analyzers', Computational Statistics and Data Analysis 77, 326-335. [doi]
A full list of publications is given here.
New version of R package pgmm now available!
McNicholas, P.D., ElSherbiny, A., McDaid, A.F. and Murphy, T.B. (2015), pgmm: Parsimonious Gaussian mixture models. R package version 1.2. Available for download on CRAN.
Department of Mathematics & Statistics,
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