Paul

McNicholas


He/Him/His


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

About


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 July 2024)

Team

Eman
Jean
Yicen
Mackenzie

Eman Alamer

Postdoctoral Fellow

Jean Li

M.Sc. Student

Yicen Li

Ph.D. Student

Mackenzie Neal

Ph.D. Candidate

Shiva
Cameron
Pankaj
Alexa

Shiva Rahimipour

Ph.D. Candidate

Cameron Roopnarine

Ph.D. Student

Pankaj Singh

Ph.D. Student

Alexa Sochaniwsky

Ph.D. Student

Elorm
Andrii
Siyi

Elorm Sowu

Ph.D. Candidate

Andrii Turchenko

Ph.D. Candidate

Siyi Wang

Ph.D. Candidate

Output

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

Monographs
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]

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

Journal Articles: Forthcoming & Recently Published (Last 10)
Zhang, X., Murphy, O.A. and McNicholas, P.D., ‘Balanced longitudinal data clustering with a copula kernel mixture model’, Canadian Journal of Statistics. To appear.

Neal, M.R., Sochaniwsky, A.A., and McNicholas, P.D., ‘Hidden Markov models for multivariate panel data’, Statistics and Computing. To appear. [doi]

Sochaniwsky, A.A., Gallaugher, M.P.B., Tang, Y. and McNicholas, P.D., ‘Flexible clustering with a sparse mixture of generalized hyperbolic distributions’, Journal of Classification. To appear. [doi].

Gabour, M.C., You, T., Fleming, R., McNicholas, P.D. and Gona, P.N. (2024), ‘The association of physical activity duration and intensity on emotional intelligence in 10–13 year-old children’, Sports Medicine and Health Science 6(4), 231-237. [doi]

Gallaugher, M.P.B. and McNicholas, P.D. (2024), ‘Clustering and semi-supervised classification for clickstream data via mixture models’, Canadian Journal of Statistics. 52(3), 678-695. [doi]

Clark, K.M. and McNicholas, P.D. (2024), ‘Finding outliers in Gaussian model-based clustering', Journal of Classification 41(2), 313-337. [doi]

Pocuca, N., Gallaugher, M.P.B., Clark, K.M. 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

Software: Recently Published or Updated
Neal, M.R., Sochaniwsky, A.A., and McNicholas, P.D. (2024). CDGHMM: Hidden Markov models for multivariate panel data. R package version 0.1.0.

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.