Machine Learning

Overview

We build robust and interpretable ML models to accelerate scientific discovery across domains, with emphasis on generalization, uncertainty, and physical constraints.

Focus Areas

• Physics-informed neural networks (PINNs)
• Graph neural networks for structured scientific data
• Interpretable models and reliability assessment

Selected Publications

• See our recent work in Nature Machine Intelligence and NeurIPS.