Dorothy Killam Fellow

Canada Research Chair in Computational Statistics

Professor, Deptartment of Mathematics and Statistics, McMaster University 

Editor-in-Chief, Journal of Classification

Paul McNicholas


My research involves the development and implementation of statistical methods, especially those that are highly dependent on computation. My principal area of interest is mixture model-based clustering and recent work includes the development of approaches for higher-order data, mixed-type data, and multivariate longitudinal data. I have a special interest in autism and aging, respectively. 

Autism & Communications Disabilities 

Toronto Star Op-Ed, 2023

Selected Honours & Awards

Dorothy Killam Fellowship, 2023

John. L. Synge Award, Royal Society of Canada, 2021

Steacie Prize for the Natural Sciences, 2020

E.W.R Steacie Memorial Fellowship, May 2019

College Member, Royal Society of Canada, 2017

University Scholar, 2017

Tier 1 Canada Research Chair, 2015

Education (Trinity College Dublin)

Sc.D. in Statistics

Ph.D. in Statistics

M.Sc. in High Performance Computing

B.A./M.A. in Mathematics

Work Experience (McMaster University)

Professor, Mathematics & Statistics, 2014-present

Associate Chair (Statistics), 2021-2023

Director, MacData Institute, 2017-2022

Curriculum Vitae (last updated January 2024)



Eman Alamer

Postdoctoral Fellow

Katharine Clark

Ph.D. Candidate

Gujie Fu

M.Sc. Student

Yicen Li

Ph.D. Student


Mackenzie Neal

Ph.D. Student

Shiva Rahimipour

Ph.D. Candidate

Cameron Roopnarine

Ph.D. Student

Pankaj Singh

Ph.D. Student


Alexa Sochaniwsky

M.Sc. Student

Elorm Sowu

Ph.D. Student

Andrii Turchenko

Ph.D. Candidate

Siyi Wang

Ph.D. Candidate


Xi Zhang

Ph.D. Candidate


Selected publications are given below. Click here to see all my publications.

McNicholas, P.D. and Tait, P.A. (2019), Data Science with Julia. Boca Raton: Chapman & Hall/CRC Press. [webpage]

McNicholas, P.D. (2016), Mixture Model-Based Classification. Boca Raton: Chapman & Hall/CRC Press. [webpage]

McNicholas, P.D. ‘Talking about my disorder helped my son — and me’, Toronto Star, A19, March 25, 2023.

Journal Articles: Forthcoming & Recently Published
Gabour, M.C., You, T., Fleming, R., McNicholas, P.D. and Gona, P.N., ‘The association of physical activity duration and intensity on emotional intelligence in 10–13 year-old children’, Sports Medicine and Health Science. To appear. [doi]

Gallaugher, M.P.B. and McNicholas, P.D., ‘Clustering and semi-supervised classification for clickstream data via mixture models’, Canadian Journal of Statistics. To appear. [doi]

Pocuca, N., Gallaugher, M.P.B., Clark, K. and McNicholas, P.D. (2023), ‘Visual assessment of matrix-variate normality’, Australian and New Zealand Journal of Statistics 65(2), 152-165. [doi]

Gallaugher, M.P.B., Biernacki, C. and McNicholas, P.D. (2023), ‘Parameter-wise co-clustering for high-dimensional data’, Computational Statistics 38, 1597-1619. [doi]

Silva, A., Qin, X., Rothstein, S.J., McNicholas, P.D. and Subedi, S. (2023), ‘Finite mixtures of matrix variate Poisson-log normal distributions for three-way count data’, Bioinformatics 39(5), btad167. [doi]

Dang, U.J., Gallaugher, M.P.B., Browne, R.P., and McNicholas, P.D. (2023), ‘Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions’, Journal of Classification 40(1), 145-167. [doi]

Phillips, J.D., Athey, T.B.T., McNicholas, P.D. and Hanner, R.H. (2023), ‘VLF: An R package for the analysis of very low frequency variants in DNA sequences’, Biodiversity Data Journal 11: e96480. [doi] (open access)

Gallaugher, M.P.B., Tomarchio, S.D., McNicholas, P.D. and Punzo, A. (2022), ‘Model-based clustering via skewed matrix-variate cluster-weighted models’, Journal of Statistical Computation and Simulation 31(2), 413-421. [doi]

Gallaugher, M.P.B., Tomarchio, S.D., Punzo, A. and McNicholas, P.D. (2022), 'Mixtures of contaminated matrix variate normal distributions', Journal of Computational and Graphical Statistics 31(2), 413-421. [doi]

Software: Recently Published or Updated
Pocuca, N., Browne, R.P., and McNicholas, P.D. (2024). mixture: Mixture models for clustering and classification. R package version 2.1.1.

McNicholas, P.D., Jampani, K.R., Subedi, S. (2023). longclust: Clustering longitudinal data. R package version 1.5.

McNicholas, P.D., ElSherbiny, A., Jampani, K.R., McDaid, A.F., Murphy, T.B. and Banks, L. (2023). pgmm: Parsimonious Gaussian mixture models. R package version 1.2.7.

Andrews, J.L., Neal, M.R., and McNicholas, P.D. (2023). vscc: Variable selection for clustering and classification. R package version 0.7.