Caleb is a Data Scientist in the Microsoft AI for Good Research Lab. His work focuses on tackling large scale problems at the intersection of remote sensing and machine learning/computer vision. Some of the projects he works on include: estimating land cover from high-resolution satellite imagery across the continent, detecting concentrated animal feeding operations (CAFOs) from aerial imagery, and estimating human population density from satellite imagery. Caleb is interested in research topics that facilitate using remotely sensed imagery more effectively in these types of problems in computational sustainability. 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.