About Us

Sanjeena's research focuses on developing efficient and scalable statistical models for analysis of complex large scale genomic data. Her work primarily focuses on developing clustering algorithms for multivariate discrete data, compositional count data, skewed data and high dimensional data such as RNA-seq data, microbiome data, microarray data, etc.

Volodymyr's research interests include finite mixture models, model-based cluster analysis, data visualization, and statistical software development. He serves as an Associate Editor for the Journal of Classification and Sankhya Series B.

Jeff's research is primarily focused on mixture models, with recent manuscripts exploring alternative parameter estimation schemes, clustering performance assessment, and applications in both engineering and medical physics.

Brian's research focuses on developing mixtures of non-Gaussian multivariate distributions for model-based classification. He is also a collaborator on a number of applied papers in other fields such as neuroscience and immunology.

Meredith’s research broadly focuses on developing and applying cutting-edge methods for multidimensional and multimodal data in psychiatry. She is particularly interested in understanding the role of multidimensional sleep (captured through self-report, actigraphy, and polysomnography) in health outcomes. Her methodological interests relate to clustering with non-elliptical distributions and novel importance metrics for random forests in machine learning.

Michael recently completed a Banting postdoctoral fellowship at McMaster University, and has now joined the Department of Statistical Science at Baylor University. His research interests lie in clustering and classification methodology for more complex data types. One example includes multiway data such as multivariate longitudinal data, and images that come in the form of matrices and tensors. Other examples include mixed type data, clickstream data, and very high dimensional data.

Sunny’s research focuses on developing new statistical tools to analyze, model, and interpret complex systems such as human dynamics and high dimensional multimodal data arising from medical research. This research lies at the intersection of modern statistical learning and “traditional” statistical ideas.

Cristina’s research focuses on data analysis, specifically, on the development of advanced clustering techniques addressing cluster flexibility, missing data, robustness in the presence of outliers, and high dimensional data sets. She collaborates with experts in applied fields, including transportation and industrial engineering.

As a researcher and quantitative analyst, Stephen aim is to establish himself as a methodologist who works at the boundaries of marketing, psychometrics, applied statistics, and data mining. Stephen integrates knowledge from all of these areas in order to make sense of complex marketing data and to find solutions to real world problems found in industry.

Orla's research interests revolve around multivariate statistics, including clustering and mixture models, discrete and mixed-type data modelling, and extreme value analysis.

Paul's research focuses on statistical approaches to clustering, aka unsupervised classification. In particular, his research group develops and implements statistical approaches based on (finite) mixture models. He has written a monograph and over 100 research articles on mixture model-based clustering. Recent work includes approaches for higher order data as well as approaches for dealing with outliers. He is a past winner of the Chikio Hayashi Award from the International Federation of Classification Societies, and a member of the College of the Royal Society of Canada.

Mackenzie’s research broadly focuses on the development of clustering methods, with recent work on missing data mechanisms, hidden Markov models for longitudinal data, and clustering approaches for the analysis of flow cytometry data.