CAREER: New Frontiers In Large-Scale Spatiotemporal Data Analysis

University Of California-San Diego, La Jolla CA

Investigators

Abstract

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 weather 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). 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.