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CANSSI Collaborative Research Team awards for professors Gabriela Cohen Freue and Will Welch

Congratulations to our professors Gabriela Cohen Freue and Will Welch, who have each received a Collaborative Research Team (CRT) award from the Canadian Statistical Sciences Institute (CANSSI). These awards will fund CRT projects that involve collaborators across Canada.

CANSSI CRT projects

CANSSI CRT projects span three years and have a broad research scope. During a project, a team will work on a research problem by creating new—or creatively using established—statistical and data science methods and models.

Gabriela's CANSSI CRT project

Gabriela is co-leading a project called Improving robust high-dimensional causal inference and prediction modelling. This is co-led by Celia Greenwood from the Lady Davis Institute for Medical Research.

The other team members include Sahir Bhatnagar (McGill University), Dehan Kong (University of Toronto), Karim Oualkacha (Université du Québec à Montréal), David Soave (Laurier University), Linbo Wang (University of Toronto), Brent Richards (McGill University), Tom Blydt-Hansen (UBC), and Zhaolei Zhang (University of Toronto).

CANSSI summarizes this project’s central goal as follows:

… to develop and establish an advanced analytical framework for the study and integration of complex data in biomedical sciences, including advanced regularized regression methods, robust regularized instrumental variable methods, and matrix-valued causal models, all for high-dimensional settings. These advancements are essential for building useful models in precision medicine.

Will's CANSSI CRT project

Will is co-leading a project called Statistical machine learning with functional data for assessment of landscape vulnerability to climate change and land cover development. Co-leaders are Ali Ameli from UBC’s Department of Earth, Ocean and Atmospheric Sciences and Jiguo Cao from Simon Fraser University’s Department of Statistics and Actuarial Science.

The other team members include Pierre Duchesne (Université du Montréal); Richard Arsenault (Université du Quebec à Montréal); and the British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development (Prince George).

CANSSI summarizes this project's goals as follows:

This project aims to bring together hydrology and statistical scientists in order to develop new generalizable statistical learning tools using multivariate functional data to (1) identify causes and consequences of environmental disturbances, (2) identify individual and interactive controls on landscape vulnerability to multi-dimensional environmental disturbances and (3) reflect the bi-directional feedback between environmental disturbances and the hydrologic function of earth systems, across distinct geographies and environmental settings in Canada. These scientific aims will require corresponding advances in statistical modelling and analysis of multivariate functional data.

February 23, 2021