BIGDATA: F: DKA: Collaborative Research: High-Dimensional Statistical Machine Learning for Spatio-Temporal Climate Data

BIGDATA：F：DKA：协作研究：时空气候数据的高维统计机器学习

• 批准号: 1664720
• 负责人: Pradeep Ravikumar
• 金额: $ 32万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-22 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1664720&HistoricalAwards=false
• 关键词: BIGDATA; DKA; Collaborative; Research; Dimensional

While statistical machine learning has seen major advances over the past two decades, rigorous approaches for high-dimensional spatio-temporal scientific data analysis have not received as much attention. On the other hand, several core scientific areas, including climate science, ecology, environmental sciences, and neuroscience, are generating increasing amounts of high-resolution spatio-temporal data. It is vital to develop rigorous machine learning approaches for such complex high-dimensional spatio-temporal data in order for key scientific breakthroughs in these areas in the next few decades. The project contributes to these endeavors by focusing on two key technical and scientific areas: spatio-temporal big data analysis and climate science. The project systematically develops the statistical machine learning foundations for the analysis of large scale complex high-dimensional spatio-temporal data, and applies such advances to problems arising in climate science, where the total amount of data is set to cross an Exabyte (1 Exabyte = 1000 Petabytes) soon. The technical work in the project has three broad and interacting components: structured probabilistic graphical models for spatio-temporal data analysis, generalized graphical models for multivariate heavy tailed distributions, and physics-guided models with a richer class of structural constraints and capturing multi-scale phenomena. The project applies these technical advances to climate science, by generating climate projections at high-resolutions. Currently, the lack of requisite spatial resolution of current climate models makes automatic assessments of impacts, adaptation and vulnerability (IAV) difficult for a variety of sectors, including urban planning, freshwater resources, food security, energy, transportation systems, human health, and coastal systems.

尽管统计机器学习在过去二十年中取得了重大进展，但高维时空科学数据分析的严格方法尚未受到足够的关注。 另一方面，包括气候科学、生态学、环境科学和神经科学在内的几个核心科学领域正在产生越来越多的高分辨率时空数据。 为了在未来几十年内在这些领域取得关键科学突破，针对此类复杂的高维时空数据开发严格的机器学习方法至关重要。 该项目通过关注两个关键技术和科学领域为这些努力做出贡献：时空大数据分析和气候科学。 该项目系统地开发了用于分析大规模复杂高维时空数据的统计机器学习基础，并将这些进展应用于气候科学中出现的问题，其中数据总量设置为跨艾字节（1 Exabyte） = 1000 PB）很快。该项目的技术工作包含三个广泛且相互作用的组成部分：用于时空数据分析的结构化概率图模型、用于多元重尾分布的广义图模型以及具有更丰富的结构约束类别并捕获多尺度的物理引导模型。现象。 该项目通过生成高分辨率的气候预测，将这些技术进步应用于气候科学。 目前，当前气候模型缺乏必要的空间分辨率，使得各个部门难以自动评估影响、适应和脆弱性（IAV），包括城市规划、淡水资源、粮食安全、能源、交通系统、人类健康和沿海系统。