CAREER: New Frontiers In Large-Scale Spatiotemporal Data Analysis

职业：大规模时空数据分析的新领域

基本信息

批准号：
2146343
负责人：
Qi Yu
金额：
$ 60万
依托单位：
University of California-San Diego
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2027-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146343&HistoricalAwards=false
关键词：
CAREER New Frontiers Large Scale

项目摘要

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.

这项五年的职业发展计划旨在建立协同的研究和教育计划，该计划对大规模时空数据（即，跨时时间和时间收集的数据）进行分析，以提高高效，稳健和可信赖的实时决策。从气候科学到公共卫生的各个科学领域，大量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，要么实时操作太慢。虽然深度学习（DL）具有极大的灵活性和可扩展性，但它具有大规模时空数据并最终有助于科学领域的能力。一个主要原因是时空数据的独特性质：它是高度动态的，受物理定律的控制，并具有复杂的相互作用。这些特征对现有的机器学习方法构成了根本挑战。本研究计划在物理科学领域的用例吸引人，旨在开发针对三个核心挑战的DL技术：（1）预测时空动态，同时遵守物理法律；（2）推断时空相互作用以捕获复杂的依赖性；（3）量化决策时空预测的不确定性。最终目标是设计可以模仿洋流，交通流和流行病的DL工具比数值求解器更快，更准确，从而可以实时场景计划，控制和策略优化。教育计划将在本科，研究生层面和大规模开放在线课程（MOOC）开发新课程。外展活动将强调妇女和少数民族在机器学习研究中的早期参与。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准，被认为值得通过评估来获得支持。