Stat Cafe - Dr. Khai Nguyen
Accelerating Optimal Transport for Data Science
- Time: Tuesday, 9/22/2026 from 11:10AM to 12:25PM
- Location: BLOC 448
Description
Optimal transport (OT) gives a principled way to match, compare, and interpolate distributions, but its computational cost restricts its use at realistic scale. I will present our work on reducing that cost and using OT structure for statistical modeling and inference. The first part introduces sliced-regularized OT, which uses the cheap sliced OT plan as an informative prior for approximating the full plan. The second part introduces distributional determinantal point processes, which use sliced OT geometry to place repulsive priors on distributions, giving a Bayesian model for clustering distributional data. Applications include image processing and clustering single-cell and epilepsy data.
Our Speaker
Dr. Khai Nguyen is an Assistant Professor in the Department of Statistics at Texas A&M University. He received his Ph.D. in Statistics from the University of Texas at Austin and holds a Bachelor’s degree in Computer Science from Hanoi University of Science and Technology. His research spans statistical machine learning, Bayesian statistics, and deep learning. He focuses on computational optimal transport, developing sliced optimal transport methods that scale optimal transport applications in machine learning, statistics, and geometric data processing.