I design and analyze Langevin algorithms: Markov chain Monte Carlo methods for sampling high-dimensional distributions across networks of agents, inside constrained domains, and at higher orders of accuracy. At Iowa State I work with David P. Herzog. I earned my Ph.D. at Florida State University in 2026, advised by Lingjiong Zhu.
Carver Hall 461, Ames, IA Google Scholar GitHub
Collision-free stochastic particle systems inside a constrained domain, and numerical schemes that stay stable near the singularity without creating artificial collisions. Joint work with D. P. Herzog.
Agents on a network sample a shared posterior without pooling their data. This line of work removes network bias with EXTRA-type corrections and handles convex constraints through proximal regularization.
Samplers built from \(P\)-th order Langevin dynamics through splitting and accurate integration. Their Wasserstein guarantees depend better on the dimension and the accuracy level as the order grows.
You played a large part in convincing me that I am capable of learning difficult subjects.Student evaluation, MAP 2302 Ordinary Differential Equations, Summer 2026
I have taught calculus, differential equations, precalculus, and actuarial mathematics since 2018. This fall I lead Calculus III recitations at Iowa State.