CAREER: An Integrated Inferential Framework for Big Data Research and Education

职业：大数据研究和教育的综合推理框架

• 批准号: 1454377
• 负责人: Han Liu
• 金额: $ 40万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1454377&HistoricalAwards=false
• 关键词: CAREER; Integrated; Inferential; Framework; Big

This project addresses several fundamental challenges in modern data analysis and aims to create a new research area named Big Data Inference. Currently available literature regarding Big Data research mainly focuses on developing new estimators for complex data. However, most of these estimators are still in lack of systematic inferential methods for uncertainty assessment. This project hopes to bridge this gap by developing new inferential theory for modern estimators unique to Big Data analysis. The deliverables of this project include easy-to-use software packages, which directly help scientists to explore and analyze complex datasets. The principal investigator is also actively collaborating with many scientists to ensure the more direct impact of this project to the targeted scientific communities.This project aims to develop novel inferential methods for assessing uncertainty (e.g., constructing confidence intervals or testing hypotheses) of modern statistical procedures unique to Big Data analysis. In particular, it develops innovative statistical inferential tools for a variety of machine learning methods which have not yet been equipped with inferential power. It also provides necessary inferential tools for the next generation of scientists to be competitive in modern data analysis.

该项目解决了现代数据分析中的几个基本挑战，旨在创建一个名为“大数据推理”的新研究领域。目前关于大数据研究的文献主要集中在为复杂数据开发新的估计器。然而，这些估计者大多仍然缺乏系统的不确定性评估推理方法。本项目希望通过为大数据分析的现代估计者开发新的推理理论来弥合这一差距。该项目的可交付成果包括易于使用的软件包，可直接帮助科学家探索和分析复杂的数据集。首席研究员还积极与许多科学家合作，以确保该项目对目标科学界产生更直接的影响。该项目旨在开发新的推理方法，用于评估大数据分析特有的现代统计程序的不确定性（例如，构建置信区间或检验假设）。特别是，它为各种尚未配备推理能力的机器学习方法开发了创新的统计推理工具。它还为下一代科学家在现代数据分析中具有竞争力提供了必要的推理工具。