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

Lead Research Organisation: Imperial College London
Department Name: Dept of Mathematics


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.

Planned Impact

Who might benefit from this research and how might they benefit?

The partners who have supported this fellowship, NASA, the World Food Programme, and the UNAIDS Reference Group, will all directly benefit from the proposed development of statistical machine learning methods for spatiotemporal data. These methods will be developed to directly tackle challenges faced by these organisations in understanding and improving the health and well-being of humans.

* The World Food Programme assists 86.7 million people in around 83 countries each year, and this fellowship will develop survey design, analysis methods, and data scientific workflows to better target food aid to improve food security in these countries and quickly respond to unfolding humanitarian emergencies.

* NASA is using satellite data to map air pollution in low income countries, and this fellowship will focus on new methods to make timely and fine-grained estimates and theory to support the rigorous evaluations of these methods.

* The UNAIDS Reference Group on Estimates, Modelling, and Projections is responsible for advising UNAIDS and country governments on spatiotemporal statistics and forecasts related to the estimated 37 million people worldwide living with HIV. The fellowship will develop new methods for analysis and data collection to enable the UNAIDS Reference Group to provide estimates at the right spatial and temporal scale to be policy-relevant.

Through the support of the Stan Development Team, methods and applied workflows will be disseminated as widely as possible. The Stan software, which we will extend to handle larger and more flexible spatiotemporal statistical models, is downloaded almost a million times per year. This means that there is a very large audience with the expertise to use and adopt the methods we are developing in a range of application areas, from public health and public policy to natural science, healthcare, and business analytics.


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