Associate Professor of Statistics
Elizabeth Purdom’s research interests lie in developing statistical methods for high-dimensional data arising in the field of biology and genetics. She focuses on questions of robust estimation and hypothesis testing for high-throughput biological experiments, in particular gene expression microarrays and next generation sequencing. She is also interested in integration of heterogeneous sources of data, where the data can be multiple experimental platforms or, more generally, arbitrary forms of preexisting biological knowledge such as networks or trees. Statistically, she is interested in questions of high-dimensional inference and multivariate analysis — problems that arise naturally in trying to create a unified understanding of this type of data.