About me

I am an Assistant Professor in the Department of Mathematics and Statistics at the University of Vermont and core faculty of the Vermont Complex Systems Institute and part of its Steering committee. As a computational topologist, my research develops mathematical frameworks that bridge pure mathematics with practical applications in neuroscience and complex systems.

My work focuses on modeling and analyzing higher-order interactions in networks—relationships that involve multiple entities simultaneously rather than simple pairwise connections. I have developed several mathematical modeling approaches including the simplicial configuration model for null hypothesis testing in complex networks, applications of the Mapper algorithm for topological simplification of gene co-expression data, and measures of "simpliciality" that quantify the structural properties of higher-order networks.

My current research explores sheaf-theoretic frameworks that can characterize and quantify the distance between observed noisy data and underlying topological structures. This approach scope is to embed uncertainty directly into topological models rather than treating it as external noise, offering new ways to handle real-world data complexity.