Sharon McNicholas

Sharon McNicholas is an Assistant Professor at the Department of Mathematics and Statistics, McMaster University. She holds two Ph.D. degrees, one in Statistics and the other in Physics. Her Ph.D. in Statistics was supported by an Alexander Graham Bell Scholarship from the Natural Sciences and Engineering Council of Canada.

Her research focus is on data science and the computational aspects of statistics, with a particular focus on the analysis of modern data sets, i.e., so-called big data. The focus of some recent resarch has been on using evolutionary computation techniques for parameter estimation within various statistical models. She has also developed an approach that is effective for finding subgroups in high-dimensional data. This approach is based on mixtures of variance gamma distributions, which permit asymmetric and heavier-tailed subgroups.

About

Appointments

- ▪ Assistant Professor, Department of Mathematics and Statistics, McMaster University. 2016-present.
- ▪ Postdoctoral Fellow, Department of Mathematics and Statistics, University of Guelph, 2009.
- ▪ Postdoctoral Fellow, Department of Physics, University of Guelph, 2008-2009.
- ▪ Postdoctoral Fellow, Department of Mathematics and Statistics, University of Guelph, 2008.

Research interests

Big data, data science, evolutionary computation, statistical learning.

Research funding

- ▪ NSERC Discovery Grant, principal investigator, Parameter Estimation for Non-Gaussian Model-Based Clustering with High-Dimensional Data, $14,000 per year, 2017-2023.

Education

▪ Ph.D. in Statistics, University of Guelph, 2016.

Thesis title: Topics in Evolutionary Computation and Model-Based Clustering.

▪ Ph.D. in Physics, Trinity College Dublin, 2008.

Thesis title: Optical Characterization of Silicon Nanowire Structures.

▪ B.Sc. in Physics and Physics Technology, Dublin Institute of Technology, 2003.

First class honours.

Publications

Peer-Reviewed Contributions

McNicholas, S.M. , McNicholas, P.D. and Browne, R.P. (2017), 'A mixture of variance-gamma factor analyzers' in S.E. Ahmed (ed.) Big and Complex Data Analysis. Cham: Springer International Publishing, pp. 369-385.

Ashlock, D. and McNicholas, S. (2013), 'Fitness landscapes of evolved cellular automata'. IEEE Transactions on Evolutionary Computation 17(2), 198-212.

Ashlock, D. and McNicholas, S. (2012), 'Single parent generalization of cellular automata rules' in Proceedings of the 2012 IEEE Congress on Evolutionary Computation, 1-8.

NiMhuircheartaigh, E., Giordani, S., MacKernan, D., King, S.M. , Rickard, D., Val Verde, L.M., Senge, M.O. and Blau W.J. (2011), 'Molecular engineering of nonplanar porphyrin and carbon nanotube assemblies: A linear and nonlinear spectroscopic and modeling study'. Journal of Nanotechnology 8, Article ID 745202, 12 pages.

King, S.M. , Chaure, S., Krisnamurthy, S., Colli, A., Ferrari, A. and Blau W.J. (2008), 'Optical characterisation of oxide encapsulated silicon nanowires of various morphologies'. Journal of Nanoscience and Nanotechnology 8(8), 4202-4206.

King, S.M. , Chaure, S., Doyle, J., Colli, A., Ferrari, A. and Blau W.J. (2007), 'Scattering induced optical limiting in Si/SiO2 nanostructure dispersions'. Optics Communications 276, 305-309.

Submitted Contributions

McNicholas, S.M. , McNicholas, P.D. and Ashlock, D.A., 'An evolutionary algorithm with crossover and mutation for model-based clustering’.

Kampo, R.S., McNicholas, S.M., McNicholas, P.D., ‘Clustering incomplete data using evolutionary algorithms’.

Teaching

Ph.D. Courses

- ▪ STATS 794: Directed Reading

Winter 2019

- ▪ STATS/CSE 780: Data Science

Winter 2018, Fall 2018, Winter 2019, Fall 2019, Winter 2020

Undergraduate/Master’s Courses

- ▪ STATS 3DS03: Introduction to Data Science Theory

Winter 2020

- ▪ STATS 4M03/6M03: Multivariate Analysis

Winter 2017, Fall 2017, Winter 2019

- ▪ STATS 3Y03/3J04: Probability and Statistics for Engineers

Winter 2017, Fall 2017

Course Development

▪ STATS 3DS03: Introduction to Data Science Theory