Computational Statistics and Data Science is the application of high level techniques to complex data.
Computational statistics describes those areas of statistics that are necessarily highly computational. Researchers in this field usually have training in statistics, mathematics and computing. Research in computational statistics at McMaster includes work in bioinformatics, classification, clustering, EM algorithms, evolutionary algorithms, high-performance computing, latent variable models, mixed models, mixture models, and MM algorithms. From a mathematics and statistics perspective, data science can be viewed as the application of high level techniques to big, or otherwise complex, data. Often, no such high level technique is available and an approach must be developed to address a particular data question; when this happens, the approach developed is often a computational statistics approach; hence, the natural relationship between computational statistics and data science. At McMaster, researchers work on a wide range of data problems in areas such as health, biology, finance, and insurance.
Assistant Professor
Reserach Area: Statistics and data mining
Research Profile: Statistics and data mining
Dr. Jeganathan’s research focuses on developing statistical and computational methods to analyze multi-domain data, especially addressing statistical challenges in microbiome multi-omic and spatial multi-omic data analysis. Current research includes multi-table integration, preprocessing and transformation of high-throughput sequencing data, visualization, hierarchical modeling, Bayesian statistics, statistical inference, block bootstrap method, data mining, and approximation theory in statistics.
I develop and study universal deep learning models capable of leveraging geometric structures in mathematical finance and data science problems.
Professor, Canada Research Chair and Associate Chair, Statistics
Research Area: Computational Statistics
Research Profile: Computational Statistics Dr. McNicholas’ 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 big data featuring outlying or spurious points, with a focus on classification, clustering, dimension reduction and discriminant analysis. Another important aspect of Dr. McNicholas? current research is work on non-Gaussian mixture models, which present a useful alternative to the Gaussian mixture model. Work on clustering categorical data and data of mixed type is ongoing. Applications of Dr. McNicholas? research are readily found in several fields, including bioinformatics, sensometrics, and psychometrics.