Sharon McNicholas

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.