Caleb is a Research Scientist in the Microsoft AI for Good Research Lab (opens in new tab). He graduated from the Georgia Institute of Technology with a PhD in 2020 and his work focuses on tackling large scale problems at the intersection of remote sensing and machine learning/computer vision. At the AI for Good Lab he co-leads the Geospatial ML research group (opens in new tab) and is the lead researcher on the Global Renewables Watch (opens in new tab), rapid damage assessment, and global building density estimation teams. Caleb is interested in research topics that facilitate using remotely sensed imagery more effectively in conservation, sustainability, and damage response application. For example: self-supervised methods for training deep learning models with large amounts of unlabeled satellite imagery, human-in-the-loop methods for creating and validating modeled layers, and domain adaptation methods for developing models that can generalize over space and time.