This group uses computational and statistical tools to understand environmental change, water systems, and natural hazards. Their work combines deep learning, physical modeling, and social science to study complex systems like floods, land use, and hydrology in a changing climate.
Themes:
- deep learning
- causal inference
- surrogate modeling
- satellite imaging
Members
- Andrew Bennett - computational hydrology, with a focus on deep learning, physically based models and hybrid differentiable models. Also scientific computing and open science.
- Beth Tellman - causes and consequences of global environmental change for vulnerable populations, with a focus on illicit activities, flood risk, water scarcity, and land use change