Marc Crampe’s research focuses on improving short-term precipitation forecasting to strengthen urban flood resilience in New York City. His work combines weather-station data, radar observations, satellite imagery, and cloud-tracking techniques with physics-informed machine-learning models to produce high-resolution, real-time rainfall predictions adapted to complex urban environments.

Before starting his PhD, Marc worked as a Geospatial Data Scientist at OpenAtlas, where he developed AI-driven tools for deforestation monitoring and canopy-height estimation using optical and LiDAR data. This work supported companies in complying with the EU Deforestation Regulation (EUDR) and promoted sustainable supply-chain monitoring.