2022
- Generalized Soft Impute for Matrix Completion
- University of Calgary Statistics Seminar, Calgary AB
- Slides
- Generalized Soft Impute for Matrix Completion
- Annual Meeting of the Statistical Society of Canada, Online
- Winner of the Award for Best Presentation by a New Investigator
- Slides
2021
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Investigating text data using Topological Data Analysis
- Annual Meeting of the Statistical Society of Canada, Online
-
Analyser les tweets de chefs politiques canadiens grâce à l’Analyse Topologique de Données
- UQAM Statistics Seminar, Université du Québec à Montréal, Montréal QC
- Slides
-
Investigating text data using Topological Data Analysis
- Machine Learning Special Interest Group, University of Manitoba, Winnipeg MB
- Slides
2020
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Arbitrary-precision linear algebra in
R
usingRcppEigen
andBH
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How to see in 100 dimensions: Transforming your data to better understand it
-
Principal Component of Explained Variance: High-Dimensional Estimation and Inference
- Statistics Seminar, University of Winnipeg, Winnipeg MB
- Slides
2019
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Principal Component of Explained Variance: High-Dimensional Estimation and Inference
- PhD Thesis Defense, McGill University, Montreal QC
- Slides
-
A Tracy-Widom Empirical Estimator For Valid P-values With High-Dimensional Datasets
-
casebase
: An alternative framework for survival analysis- Bioinformatics-Biostatistics Research Seminar, University of Manitoba, Winnipeg MB
- Slides
- Package website
2018
-
Principal Component of Explained Variance: High-Dimensional Estimation and Inference
- Joint Special Seminar, Departments of Statistics and Computer Science, University of Manitoba, Winnipeg MB
- Slides
-
Nonlinear Dimension Reduction to Improve Predictive Accuracy in Genomic and Neuroimaging Studies
2017
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Reduced-Rank Singular Value Decomposition for Dimension Reduction with High-Dimensional Data
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A Novel Approach To Competing-Risk Analysis Using Case-Base Sampling
- Fifth Annual Canadian Statistics Student Conference, Winnipeg MB
- Slides
2016
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Principal Component of Explained Variance: An Efficient and Optimal Data Dimension Reduction Framework for Association Studies
-
A novel approach to competing risks analysis using case-base sampling
- Research Day, Department of Epidemiology, Biostatistics and Occupational Health, McGill University
- Winner of the Dr. Jim Hanley Research Day Award for Best Presentation in Biostatistics
- Slides
2015
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Principal Component of Explained Variance: An Efficient and Optimal Data Dimension Reduction Framework for Association Studies
- Montreal Genomics Monthly Meeting
- Slides
-
Efficient dimension-reduction technique for the joint analysis of correlated phenotypes
- Annual Human and Statistical Genetics Meeting, Vancouver BC
- Slides