Spatiotemporal statistical machine learning (ST-SML): theory, methods, and applications

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Machine learning (ML) is the computational beating heart of the modern Artificial Intelligence (AI) renaissance. A number of fields, from computer vision to speech recognition have been completely transformed by the successes of machine learning. But practitioners and policymakers struggle when it comes to translating the successes of ML from narrowly defined prediction problems---e.g. "is this a picture of a cat?"---to the broader and messier world of public health and public policy. This fellowship will fund research on new ML methods to enable us to better ask and answer questions concerning change over space and time, such as: 1) How does disease risk, poverty, or housing quality vary within a country and over time?2) Can satellite data enable us to answer policy questions in a more timely and spatially localised manner?3) Do the dynamics of violent crime differ in different cities?4) Did the world achieve the Millennium Development Goals? Will the world achieve the Sustainable Development Goals?Bespoke answers to these questions are not enough, because practitioners in the public sector face new challenges in real-time. They need reproducible and well-documented applied workflows to follow to enable them to tackle important public policy problems as they arise.