Gabriela works in statistical problems that usually appear in the analysis of proteomics and genomics data. In these applications, some variables may be measured with error; the number of features is much larger than the number of observations; the observations are not always independent; and outliers are present in the data. Gabriela develops statistical and computational methods to address some of these common issues in statistical genomics. In particular, she is interested in the development of robust estimation methods for sparse linear models. Gabriela is a Canada Research Chair (Tier 2) in Statistical Proteomics.
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