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     Department of Systems Engineering and Engineering Management

                    The Chinese University of Hong Kong

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Date: Tuesday, November 17, 2026, 4:30pm to 5:30pm HKT

Venue: ERB909, The Chinese University of Hong Kong

Title:  Optimal Transport for Matching and Simulation

Speaker: Prof. Binh T. Nguyen, VinUniversity

Abstract:

Optimal transport provides a powerful mathematical framework for comparing probability distributions, with applications ranging from matching structured data to simulation and decision-making under uncertainty. In this talk, I will discuss two recent works that explore how optimal transport can be used to address two distinct challenges in optimization and machine learning. In the first part, I consider the Gromov–Wasserstein (GW) problem, which seeks to match objects across incomparable spaces by preserving their underlying geometric structures. Since GW is formulated as a nonconvex quadratic optimization problem, existing algorithms typically provide only locally optimal solutions. I will present a semidefinite programming relaxation that provides computable lower bounds and, in many instances, certifies global optimality. I will also discuss its applications to graph and shape matching. In the second part, I turn to conditional distribution learning, motivated by simulation and personalized decision-making. When observations are scarce, accurately estimating an entire conditional distribution can be substantially more challenging than predicting its mean. I will introduce GENTLE, a neural generative model that combines distributional fitting with entropic optimal transport regularization to learn conditional distributions from limited data. Numerical experiments demonstrate its effectiveness in recovering conditional distributions on real-world and simulation datasets. Together, these works illustrate how optimal transport connects convex optimization, generative modeling, and data-driven decision-making.

Biography:

Binh T. Nguyen is currently an Assistant Professor at VinUniversity. His research interest includes generative modeling (LLM evaluation and alignment, post-training and model steering; diffusion models), optimal transport, and large-scale statistical inference. His works have appeared in leading machine learning, optimization, and natural language processing venues, including NeurIPS, ICML, ICLR, EMNLP, NAACL, and SIAM Journal of Optimization.

Everyone is welcome to attend the talk!

SEEM-5201 Website: http://seminar.se.cuhk.edu.hk