Bio: Sherrie is a Ciriacy-Wantrup Postdoctoral Fellow working on machine learning for remote sensing and applications in sustainable development. She holds a Ph.D. in Computational and Mathematical Engineering from Stanford University. Her dissertation developed machine learning methods and used novel datasets to map agriculture with satellite imagery in settings where ground labels are scarce. Prior to that, she obtained a B.A. in Biomedical Engineering from Harvard University.
Research: Sherrie's research focuses on extracting insights from Earth observation to monitor sustainable development, especially in regions of the world with poor data infrastructure. Since a lack of ground labels is often the bottleneck for measuring development, one strand of her research develops transfer learning methods to enable land cover mapping in the absence of ground labels. Once maps are created, she uses them to understand spatially disaggregated trends over time and evaluate the effects of policies and technologies.
Fields of Interest: Machine learning, remote sensing, agriculture, spatial statistics
