Events

Leveraging Structure for Intelligent Representation Learning in Health and Biomedicine

Speaker: Matthew McDermott

Location: 60 Fifth Avenue, Room 150
Videoconference link: https://nyu.zoom.us/j/92941471430

Date: Wednesday, March 13, 2024

Machine learning today is undergoing a “foundation model” revolution.
Emboldened by successes in natural language processing and computer vision
such as GPT-4 and Stable Diffusion, more and more machine learning problems
are beginning to leverage the incredible potential of these state of the
art representation learning technologies to challenge the kinds of problems
we can solve and the methods we can use to solve them. In the high-impact,
high-risk domain of health and biomedicine, the appeal of foundation models
is especially poignant, given the prevalence of tasks that only have small
and/or noisy datasets available for training. However, there remains
significant uncertainty on how we can build foundation model systems most
effectively in healthcare domains given the significant data heterogeneity,
limited dataset sizes, and the notable differences in health data structure
versus the comparatively simple structure of natural language. In this
talk, I will describe the relevant portions of my prior research and my
research vision to solve these problems and drive the state of foundation
model research forward within health and biomedicine. Building on my
extensive history of research over high capacity representation learning
systems, specifically those empowered by external structure and knowledge,
I will describe what a “medical foundation model” really is and how we can
build them, scale them to new dataset sizes, and leverage them in concert
with existing and prior success to incorporate existing medical and
modeling expertise.