Dr. Constance van Eeden seminar: Dr. Arnaud Doucet

The van Eeden seminar is a yearly event in which graduate students vote for their favorite statisticians. The winner is contacted by the organizing committee and invited to give a talk in the department’s seminar. The speaker spends one or two days on-campus, and graduate students have the opportunity to have lunch and dinner with them.

The Constance van Eeden Speaker for 2025 will be Dr. Arnaud Doucet, Senior Staff Research Scientist at Google DeepMind. Dr. Doucet’s research interests lie in the development and analysis of efficient computational methods for inference and learning, machine learning, signal processing and related areas.

Abstract

Denoising Diffusion models have revolutionized generative modeling. Conceptually, these methods define a transport mechanism from a noise distribution to a data distribution. Recent advancements have extended this framework to define transport maps between arbitrary distributions, significantly expanding the potential for unpaired data translation. However, existing methods often fail to approximate optimal transport maps, which are theoretically known to possess advantageous properties. In this talk, we will show how one can modify current methodologies to compute Schrödinger bridges—an entropy-regularized variant of dynamic optimal transport. We will demonstrate this methodology on a variety of unpaired data translation tasks.