Institute for Mathematical and Statistical Innovation

数学与统计创新研究所

• 批准号: 1929348
• 负责人: Kevin Corlette
• 金额: $ 1550万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1929348&HistoricalAwards=false
• 关键词: Institute; Mathematical; Statistical; Innovation

The Institute for Mathematical and Statistical Innovation (IMSI) will apply mathematics and statistics to urgent, complex scientific and societal problems, and spur transformational change in the mathematical sciences communities. The Institute aims to catalyze new mathematical and statistical approaches to further understanding of complex phenomena and issues of great societal interest. As part of its mission, IMSI will facilitate rapid and effective dissemination of these advances to both the mathematical sciences research community and broader audiences. IMSI will also train a new generation of mathematicians and statisticians equipped to advance multidisciplinary research through immersion in the challenges encountered within and beyond the mathematical sciences. All of the IMSI programs will foster diversity and inclusion in the mathematical sciences and engage in impactful outreach programs.IMSI will address major challenges facing society by building conduits from the core disciplines of mathematics and statistics to a broad range of disciplines and applications that need mathematical and statistical insights to understand the dynamics of data- and computation-intensive phenomena. IMSI research affiliates will immerse themselves in major projects, organized around themes, driven by societal challenges such as climate forecasting, epidemiological modeling, economic crises, cancer-marker identification, and neural processing. Conceptual and technical challenges from interdisciplinary applications will create the impetus to advance mathematical and statistical approaches that are urgently needed to understand the principles governing such systems and to make accurate predictions along with quantifying the uncertainty of the predictions. Mathematical scientists at IMSI will be systematically exposed to and deeply engaged with these grand challenges. IMSI programs will bring together mathematical scientists and researchers working in these fields to promote collaborations on important problems and to enrich fundamental mathematics and statistics. One mechanism for achieving this will be through the development of national and international collaborations and partnerships with industry and national labs. The initial long programs will explore distributed decision making processes and the impact of uncertainty in the development of effective models in the social sciences. In addition, workshops will explore topics such as computational materials science, the complexity of machine learning tasks, dimension reduction methods in genomics, topological data analysis, optimized decision making in health care, and the development of verification, validation, and uncertainty quantification methods across disciplines.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.

数学和统计创新研究所（IMSI）将将数学和统计数据应用于紧急，复杂的科学和社会问题，并刺激数学科学社区的变革变革。该研究所旨在促进新的数学和统计方法，以进一步了解复杂现象和极大的社会利益问题。作为其任务的一部分，IMSI将促进这些进步的快速有效传播，以使数学科学研究社区和更广泛的受众群体促进这些进步。 IMSI还将培训新一代的数学家和统计学家，能够通过沉浸在数学科学内外遇到的挑战来推进多学科研究。所有IMSI计划都将促进数学科学的多样性和包容性，并参与有影响力的外展计划。IMSI将通过建立从数学和统计学的核心学科到需要数学和统计洞察力的广泛学科和应用的核心学科来解决社会面临的重大挑战，以了解数据和计算现象的动态。 IMSI研究分支机构将把自己沉浸在主要项目中，围绕主题组织，这是在社会挑战的推动下，例如气候预测，流行病学建模，经济危机，癌症标志物识别和神经处理。跨学科应用程序的概念和技术挑战将创造动力，以推动迫切需要了解有关管理此类系统的原则并做出准确预测以及量化预测的不确定性的动力。 IMSI的数学科学家将系统地接触并深入应对这些巨大的挑战。 IMSI计划将汇集在这些领域工作的数学科学家和研究人员，以促进有关重要问题的合作，并丰富基本的数学和统计数据。实现这一目标的一种机制将是通过与行业和国家实验室建立国家和国际合作以及合作伙伴关系。 最初的长期计划将探索分布式的决策过程以及不确定性在社会科学中有效模型开发中的影响。 In addition, workshops will explore topics such as computational materials science, the complexity of machine learning tasks, dimension reduction methods in genomics, topological data analysis, optimized decision making in health care, and the development of verification, validation, and uncertainty quantification methods across disciplines.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.