Professor of Statistics
Trained in the French School of Data Analysis in Montpellier, Susan has been working in non-parametric multivariate statistics applied to Biology since 1985. She has taught at MIT and Harvard, and was an Associate Professor of Biometry at Cornell before moving to Stanford in 1998. Susan's work focuses on large heterogeneous multi-layer data analysis, using computational statistics and optimization methods to draw inferences about complex biological phenomena. She likes working on big messy data sets, mostly in the areas of immunology, cancer biology, and microbial ecology, and enjoys teaching in R and BioConductor.
Susan is also a John Henry Samter University Fellow in Undergraduate Education; CoDirector, Mathematical and Computational Sciences IDP; and was a Fellow of the Institute of Mathematical Statistics, IMS.
"Making a complete toolbox for reproducible research for quantitative biological data analyses"
Abstract: I will give a survey of the current challenges in the analyses of heterogeneous biological data. Combining networks, contingency tables and data from multiple omics domains provides the analysts with multiple choices. The result can be an erroneous p-value or a complicated workflow, both can be irreproducible. I will survey some of the recent approaches to this challenge.