Rima Habre

Papers

Total Citations

121

H-Index

About

Rima Habre is a leading environmental health scientist whose research bridges exposure science, air quality, and spatial epidemiology. Her work focuses on understanding how environmental pollutants—particularly airborne particulate matter—affect human health, with a strong emphasis on vulnerable populations. Habre is best known for advancing spatiotemporal modeling of air pollution exposure, notably through her highly cited 2019 paper on using deep learning to impute and downscale MAIAC Aerosol Optical Depth (AOD) data, which has garnered 121 citations. This work demonstrated how machine learning can fill critical gaps in satellite-derived air quality measurements, enabling more precise and high-resolution exposure estimates for epidemiological studies. Her contributions have been instrumental in linking short- and long-term air pollution exposures to adverse birth outcomes, respiratory disease, and neurodevelopmental effects. Habre’s impact extends beyond methodology; she has led major cohort studies and collaborative projects that translate complex exposure data into actionable public health insights. Her research is widely recognized for its rigor and innovation, making her a pivotal figure in the intersection of data science, environmental monitoring, and population health.