Jeff Andrews

Associate Professor

Data Science, Mathematics, Statistics
Office: SCI 111
Phone: 250.807.9931

Graduate student supervisor

Research Summary

Clustering and classification via mixture models with applications to bioinformatics.

Courses & Teaching

Statistics; data science.


Jeff’s research primarily investigates finite mixture models and their usage in statistical machine learning. His focus is on clustering and classification, with peer-reviewed articles exploring parameter estimation, variable selection, and model development.


Google Scholar


PhD (University of Guelph), MSc (University of Guelph), BSc (Honours, Acadia University)

Research Interests & Projects

Andrews Research Group
Active and past projects include parameter estimation algorithms, software development in R, robust variable selection, and applied projects in engineering, biology, ecology, health and physics.

Undergraduate Research Assistants
Undergraduate students interested in summer research opportunities are encouraged to discuss in person. Completion of 3rd year in Math/Statistics/Computer Science/Data Science, including strong performance in Machine Learning (DATA 311) is generally a pre-requisite for fruitful summer projects — but exceptions may be possible.

Graduate Supervision
Prospective students must meet program eligibility requirements with excellent performance in upper-year mathematics/statistics courses, ideally including a course in multivariate statistics/machine learning. Research experience at the undergraduate level is an asset. Proficiency in scientific writing, R (and/or general computer programming), and LaTeX document preparation are also assets. Graduate award applications to NSERC CGS are encouraged — these generally have internal deadlines at the university you are graduating from and it is advantageous to discuss potential projects ahead of time. Teaching assistantships and other sources of funding are available for qualified incoming grad students.


teigen: Model-based clustering and classification with the multivariate t-distribution. CRAN

mmtfa: Model-based clustering and classification with mixtures of modified t factor analyzers. CRAN

vscc: Variable selection for clustering and classification. CRAN

Selected Publications & Presentations

Google Scholar

Selected Grants & Awards

NSERC Discovery Grant (2014-2020)
Mitacs Accelerate (2018)
NSERC Engage (2017-2018)
CFI John R. Evans Leaders Fund (2017)
UBC Startup (2015-2019)
MacEwan University RSACAF (2014-2015)

International Federation of Classification Societies Chikio Hayashi Award (2017)
Classification Society Distinguished Dissertation Award (2013)
NSERC Postgraduate Scholarship (PhD) (2010-2012)
Ontario Graduate Scholarship (2010, declined)
Ontario Graduate Scholarship in Science and Technology (2009)
Statistical Society of Canada Travel Award (2009)

Professional Services/Affiliations/Committees

Board of Directors, Classification Society
Committee Member, New Investigators
Member, Statistical Society of Canada
Member, Medical Physics and Data Analytics Cluster
Member, Materials and Manufacturing Research Institute


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