CAREER: New Frontiers In Large-Scale Spatiotemporal Data Analysis

这个为期五年的职业发展计划旨在建立一个协同的研究和教育计划，推进大规模时空数据的分析（即，跨空间和时间收集的数据），以实现高效、稳健和值得信赖的实时决策。在从气候科学到公共卫生的各个科学领域，大量的时空数据正在迅速出现。传统的时空分析工具要么依赖于强大的建模假设，要么太慢，无法实时操作。虽然深度学习（DL）提供了很大的灵活性和可扩展性，但它理解大规模时空数据并最终为科学领域做出贡献的能力是有限的。一个主要原因是时空数据的独特性质：它是高度动态的，受物理定律支配，并具有复杂的相互作用。受物理科学用例的启发，本研究计划旨在开发DL技术，以解决三个核心挑战：（1）预测时空动态，同时符合物理定律;（2）推断时空相互作用，以捕获复杂的依赖关系;（3）量化时空预测的不确定性，用于决策制定。最终目标是设计出能够比数值求解器更快、更准确地模拟洋流、交通流和疫情传播的DL工具，从而实现实时场景规划、控制和策略优化。该教育计划将开发本科、研究生阶段的新课程和大规模开放式在线课程（MOOC）。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This five-year career development plan aims to build a synergistic research and education program that advances analysis of large-scale spatiotemporal data (i.e., data collected across space and time) towards efficient, robust, and trustworthy real-time decision-making. Massive spatiotemporal data are emerging rapidly in various scientific fields from climate science to public health. Traditional spatiotemporal analysis tools either rely on strong modeling assumptions or are too slow to operate in real-time. While deep learning (DL) offers great flexibility and scalability, its ability to make sense of large-scale spatiotemporal data and ultimately contribute to scientific fields, is however, limited. A primary reason is the distinctive nature of spatiotemporal data: it is highly dynamic, governed by physical laws and has intricate interactions. These characteristics pose fundamental challenges to existing machine learning approaches.Inspired by the use cases in physical sciences, this research plan seeks to develop DL techniques that address three central challenges: (1) forecasting spatiotemporal dynamics while conforming to physical laws; (2) inferring spatiotemporal interactions to capture complex dependencies; and, (3) quantifying the uncertainty of spatiotemporal forecasts for decision making. The ultimate goal is to design DL tools that can emulate ocean currents, traffic flows and epidemic spread faster and more accurately than numerical solvers, thus allowing real-time scenario planning, control and strategy optimization. The education plan will develop new curricula at undergraduate, graduate level and massive open online courses (MOOCs). The outreach activities will emphasize the early engagement of women and minorities in machine learning research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.