I am currently a Foundations of Data Science (FDS) Postdoctoral associate at Yale University. Prior to this, I completed my PhD at New York University under the supervision of the wonderful Jonathan Niles-Weed. A copy of my thesis can be found here. Before my PhD, I completed my BA and MSc in Applied Mathematics at McGill University, where I worked with Tim Hoheisel and Adam Oberman.

I study statistical and mathematical aspects of data science. My recent work focuses on analyzing and developing methods for large-scale probabilistic inference and generative modeling, often through the lens of optimal transport theory.

CPSC 7430 (Topics in flow-based generative modeling and optimal transport)

This course covers the mathematics of modern generative modeling systems through the lens of optimal transport. Topics will include learning optimal transport maps, diffusion models, stochastic interpolants, sampling under tilts, snapshots of unpaired data, among others.