Andrew McCormack

Andrew McCormack is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on mathematical statistics, with a particular emphasis on non-Euclidean data analysis, algebraic statistics, and information geometry.

His research interests include:

• Non-Euclidean Data Analysis: Developing methodologies for data on manifolds and metric spaces.
• Algebraic Statistics: Applying nonlinear algebra to understand statistical models, especially graphical models.
• Tensor Decompositions: Exploring low-dimensional representations of high-dimensional data using matrices and tensors.
• Information Geometry: Studying statistical models through geometric lenses, incorporating ideas from optimal transport.
• Statistical Decision Theory: Investigating foundational approaches to evaluating statistical methods from both classical and Bayesian perspectives.

Mccormack's recent work spans robust statistical methods, information geometry in covariance modeling, and the application of algebraic techniques to statistical problems. His contributions often bridge theory and computation, addressing challenges in high-dimensional and geometric data analysis.

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