About Me

I am an incoming NSF Mathematical Sciences Postdoctoral Research Fellow at Cornell University. I make use of geometry and structure to develop principled computational methods for probabilistic modeling and inference, which encompasses Bayesian inference, generative modeling, and data science. My current focus is on effective design and structure exploitation within dynamic measure transport approaches for sampling, data assimilation, and stochastic inverse problems.

I completed my PhD in Computational Science and Engineering at MIT in 2026, where I was advised by Youssef Marzouk. Prior to beginning my graduate work I earned bachelor’s degrees in Mathematics and Computational Modeling and Data Analytics from Virginia Tech (2019) and spent some time on the technical staff at MIT Lincoln Laboratory. My work has been funded by the National Science Foundation and Google.