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

机器学习（ML）是现代人工智能（AI）复兴的计算心脏。从计算机视觉到语音识别，许多领域都因机器学习的成功而彻底改变。但是从业者和政策制定者在将ML的成功从狭义的预测问题中转化出来时会遇到困难-例如，“这是一张猫的照片吗？”“-到更广阔、更混乱的公共卫生和公共政策领域。该奖学金将资助新的ML方法的研究，使我们能够更好地询问和回答有关空间和时间变化的问题，例如：1）疾病风险，贫困或住房质量在一个国家内和随着时间的推移如何变化？2)卫星数据能否使我们能够以更及时和空间局部化的方式回答政策问题？3)不同城市的暴力犯罪动态是否不同？4)世界是否实现了千年发展目标？世界能否实现可持续发展目标？这些问题的定制答案是不够的，因为公共部门的从业人员面临着实时的新挑战。他们需要可复制和记录良好的应用工作流程，以使他们能够在出现重要的公共政策问题时加以解决。

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.