Wednesday, April 2, 2025 4:00pm – 5:30pm PDT

Simonyi Conference Center, CoDa, 389 Jane Stanford Way, Stanford, CA 94305

Abstract: Foundation models are revolutionizing biology by learning the underlying structure of complex biological data and transforming how we analyze, interpret, and generate scientific knowledge. In this talk, we explore how these models accelerate discovery by integrating vast datasets, uncovering biological organization, and guiding hypothesis-driven research. We begin with models that learn universal representations of cells across tissues and species. Without explicit supervision, these models capture fundamental biological organization, allowing researchers to analyze, compare, and annotate cells with unprecedented precision. This ability to model biological diversity naturally leads to the next challenge: how to generate and test new biological hypotheses. Large language models, trained on vast scientific literature, can play a crucial role in this process.

For more information: https://datascience.stanford.edu/events/scientific-discovery-age-data-ai/jure-leskovec-stanford