FRG: Collaborative Research: Statistical Approaches to Topological Data Analysis that Address Questions in Complex Data
FRG: Collaborative Research: Statistical Approaches to Topological Data Analysis that Address Questions in Complex Data
批准号:
1854220
负责人:
Jessica Kehe
金额:
$36.87万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-01 至 2020-08-31
中文摘要
由于仪器设备的改进和对基本数据生成机制的更深入了解,真实数据和模拟数据都变得越来越复杂,需要改进的统计方法才能进行适当的分析。天文学和生物学等具有空间错综复杂的网状数据(例如,宇宙的大规模结构、纤维蛋白网络)的领域可以受益于利用网状信息的方法。拓扑数据分析(TDA)领域在解决这些重要和具有挑战性的科学问题所需的创新方面具有巨大的潜力。这个项目将扩展现有的TDA算法、统计理论和应用程序,并通过将工作纳入免费提供的R包TDA而使进步变得容易获得。这个项目的目标是(1)扩展TDA中的现有算法,以允许统计严格的推断和改进的可视化;(2)发展必要的统计学理论,将假设检验应用于拓扑描述符集;(3)开发合理的参数选择算法;以及(4)将这些方法应用于复杂数据,特别是天体物理学的关键领域。这些进展将使跨学科的科学家和数据分析师更容易获得TDA,并将为TDA提供严格的统计学基础。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
As both real and simulated data become increasingly complex due to improved instrumentation and deeper understanding of the underlying data-generating mechanisms, improved statistical methodology is required for proper analysis. Fields such as astronomy and biology that have spatial intricate, web-like data (e.g., the large-scale structure of the Universe, fibrin networks) can benefit from methodology that exploits the web-like information. The field of Topological Data Analysis (TDA) has great potential for the innovations needed to address these important and challenging scientific questions. This project will extend existing TDA algorithms, statistical theory and applications, and make the advancement easily accessible by incorporating the work into the freely available R package TDA. Moreover, the research will train undergraduate and graduate students in an interdisciplinary and collaborative environment.The goals of this project are (1) to extend existing algorithms in TDA to allow statistically rigorous inferences and improved visualization, (2) to develop the statistical theory necessary to apply hypothesis testing to sets of topological descriptors, (3) to develop justifiable algorithms for parameter selection, and (4) to apply these methods to complex data, especially to critical areas in astrophysics. These developments will make TDA more accessible to scientists and data analysts across disciplines and will give TDA a rigorous statistical foundation.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.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
Unmasking Stellar Variability: Hierarchical Bayesian methods for characterization of low-mass planets with EPRV spectroscopy
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批准号:2204701
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项目类别:Standard Grant
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资助金额:$51.12万
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财政年份:2022
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负责人:Jessica Kehe
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依托单位:
FRG: Collaborative Research: Statistical Approaches to Topological Data Analysis that Address Questions in Complex Data
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批准号:2038556
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项目类别:Standard Grant
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资助金额:$33.48万
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财政年份:2020
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负责人:Jessica Kehe
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依托单位:
海外基金