The group’s interests include the application of statistical shape analysis to biological data sets and the statistical analysis of high-dimensional biological data sets from modern high throughput biological applications such as next-generation sequencing and plant phenomics experiments.

Kim Kenobi’s interests include using shape manifolds to model the development of plant root structures and other biological situations where the shapes of structures change over space and/or time. The research also focuses on finding biologically meaningful patterns in high dimensional data sets using clustering, supervised and unsupervised learning and fitting smoothing splines to model smoothly varying temporal profiles of genes or other biological units.