Speaker:
Bio: Kun Meng is an Assistant Professor of Statistics at Florida State University. He received his Ph.D. and Sc.M. in Biostatistics from Brown University. Following his doctoral studies, he spent three years at Brown as a Prager Assistant Professor of Applied Mathematics. His current research interests include topological data analysis, manifold learning, and fMRI data analysis.
Abstract: In the 21st century, we have seen a growing availability of shape-valued and imaging data, prompting the development of new statistical methods to analyze them. Importantly, bridging the new methods and existing frameworks is advisable.
In this talk, I will introduce several statistical inference methods for shapes and images based on the Euler characteristic. These methods have applications in many fields, such as geometric morphometrics and radiomics. From a statistical perspective, these methods are naturally connected to functional data analysis. From a mathematical viewpoint, they are grounded in solid foundations, bridging various branches of mathematics: algebraic and tame topology, Euler calculus, functional analysis, and probability theory. I will also briefly discuss some of my ongoing and future research directions.
Zoom Meeting: https://duke.zoom.us/j/95527184190
Meeting ID: 955 2718 4190