John Brandt is the Data Science Lead for the Restoration program at WRI, where he leads the research, development, and implementation of artificial intelligence and remote sensing models for global-scale mapping and project-level monitoring. His work focuses on applying computer vision and large-scale geospatial data to better understand and monitor forests, trees, and landscape restoration. John is the author of more than 20 research publications on machine learning, remote sensing, and climate and environmental applications.

During his eight years at WRI, John has taken on an increasingly broad role in shaping how the Restoration program uses AI and remote sensing. He leads research priorities, develops new technical approaches, and works with teams and partners to turn emerging methods into operational monitoring tools. His work bridges cutting-edge AI research with the practical challenges of measuring restoration at scale.

John holds a Master of Environmental Management from Yale University and a B.A. from Vassar College. When he isn’t thinking about trees or satellite imagery, he can usually be found climbing, reading nonfiction, or hanging out with his cats.