My research focuses on defining statistical models and procedures for various types of complex functional data including shapes and images. With improvements in technology, there is an influx of functional data in various application areas including medical imaging, biology, bioinformatics, biometrics, computer vision, graphics and many more. I work to develop methods on the complicated geometric representation spaces of such datasets, often Riemannian manifolds, which can be used for registration, summarization, dimension reduction, visualization, regression, hypothesis testing, classification, clustering, and other standard tasks. Due to the complex geometry, and infinite-dimensionality, of the spaces in the functional settings I consider, the developed methods require sophisticated computational tools.
Sebastian Kurtek joined the statistics faculty in 2012. He is also a Graduate Faculty member in the Interdisciplinary PhD Program in Biostatistics, as well as an Affiliated Faculty member with Translational Data Analytics at The Ohio State University. His research has been funded by the NSF. He has received several best paper awards at computer vision, pattern recognition and medical imaging conferences, and the IMS and AMS Travel Awards.