This talk will discuss some of the uses of generative models in healthcare, dive into continuous time generative models, and as this is a workshop, step back to high level speculations about generative modeling and the needs of generative modeling in healthcare. Along the way, I will cover two developments in continuous time generative models 1) learning the noising process in a diffusion model to maximize likelihood and 2) choosing the base distribution in flows/interpolants to facilitate learning. References to what I will cover:

Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions: https://arxiv.org/abs/2302.07261

Stochastic interpolants with data-dependent couplings: https://arxiv.org/abs/2310.03725

On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease: https://arxiv.org/abs/2301.11962