Research

My research focuses on the development of rigorous mathematical frameworks for global sensitivity analysis, primarily through the lens of functional analysis and reproducing kernel Hilbert spaces. I am particularly interested in using kernel-based dependence measures, such as the Hilbert-Schmidt Independence Criterion (HSIC), to provide interpretable and robust uncertainty quantification for complex models with correlated inputs. My current work involves developing new indices for analyzing real-world models in the life sciences.

This work is highly collaborative and supported by my role as a funded fellow in the NSF Research Training Group UQ4Life, where my research includes HSIC-informed analysis of a model of the Valsalva maneuver (VM). As a graduate mentor for the DRUMS REU, I oversaw undergraduate research directly tied to this RTG-supported VM project. Furthermore, a Young Scholars Grant from the Comparative Medicine Institute has allowed me fund and mentor an undergraduate researcher in this lab.

Beyond formal lab research, I am dedicated to expanding undergraduate access to advanced mathematics. I founded and currently organize the NC State mathematics department's Directed Reading Program (DRP), where I currently oversee a group of three undergraduate students on a project exploring kernel-based binary classification.