Overview

Machine learning has transformed our approach to scientific discovery, revealing patterns and generating insights that extend far beyond its traditional predictive applications. The opportunity ahead is to harness AI not as a black box, but as a partner in advancing scientific understanding.

Researchers across disciplines are grappling with remarkably similar questions. For example, 

• In materials science and chemistry, can models predicting new compounds also capture the physics of molecular interactions and reaction mechanisms?
  
  

• In biology, can imaging models go beyond detecting diseases to generating testable hypotheses about biological mechanisms?
  
  

• In climate sciences, can data-driven systems reflect causal drivers of environmental change?
  
  

• In economics, can hidden market patterns translate into interpretable theories about human behavior?

While each field has made significant progress independently, we believe cross-disciplinary exchange is key to building interpretable, scientifically grounded, discovery-oriented AI.

This retreat will bring together ~40 researchers, including faculty, PhD students, and postdocs, from diverse scientific domains to share insights and foster collaborations in AI for scientific discovery.