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Applied Math and Comp Sci Colloquium

Friday, March 29, 2019 - 2:00pm

Stefano Ermon

Stanford University


University of Pennsylvania


Recent technological developments are creating new spatio-temporal data streams that contain a wealth of information relevant to sustainable development goals. Modern AI techniques have the potential to yield accurate, inexpensive, and highly scalable models to inform research and policy. A key challenge, however, is the lack of large quantities of labeled data that often characterize successful machine learning applications.

In this talk, I will present new approaches for learning useful spatio-temporal models in contexts where labeled training data is scarce or not available at all. I will show applications to predict and map poverty in developing countries, monitor  agricultural productivity and food security outcomes, and map infrastructure access in Africa. Finally, I will discuss opportunities and challenges for using these predictions to support decision making, including techniques calibration and for inferring human preferences from data.