HDR TRIPODS: D4 (Dependable Data-Driven Discovery) Institute

HDR TRIPODS：D4（可靠数据驱动的发现）研究所

基本信息

批准号：
1934884
负责人：
Hridesh Rajan
金额：
$ 150万
依托单位：
Iowa State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934884&HistoricalAwards=false
关键词：
HDR TRIPODS D4 Dependable Data

项目摘要

Data-driven discoveries are permeating critical fabrics of society. Unreliable discoveries lead to decisions that can have far-reaching and catastrophic consequences on society, defense, and the individual. Thus, the dependability of data-science lifecycles that produce discoveries and decisions is a critical issue that requires a new holistic view and formal foundations. This project will establish the Dependable Data Driven Discovery (D4) Institute at Iowa State University that will advance foundational research on ensuring that data-driven discoveries are of high quality. The activities of the D4 Institute will have a transformative impact on the dependability of data-science lifecycles. First, the problem definition itself will have a significant impact by helping future innovations beyond academia. While the notion of dependability is well-studied in the computer-systems literature, challenges in data science push the boundary of existing knowledge into the unknown. This institute's work will define D4, and increase data science's benefit to society by providing a transformative theory of D4. The second impact will come from the process of shared vocabulary development facilitated by this institute, and its result that would encourage experts across TRIPODS disciplines and domain experts to collaborate on common goals and challenges. Third, the institute will set research directions for D4 by providing funding for foundational research, which will have a separate set of impacts. Fourth, the institute will facilitate transdisciplinary training of a diverse cadre of data scientists through activities such as the Midwest Big Data Summer School and the D4 workshop. The project will advance the theoretical foundations of data science by fostering foundational research to enable understanding of the risks to the dependability of data-science lifecycles, to formalize the rigorous mathematical basis of the measures of dependability for data science lifecycles, and to identify mechanisms to create dependable data-science lifecycles. The project defines a risk to be a cause that can lead to failures in data-driven discovery, and the processes that plan for, acquire, manage, analyze, and infer from data collectively as the data-science lifecycle. For instance, an inference procedure that is significantly expensive can deliver late information to a human operator facing a deadline (complexity as a risk); if the data-science lifecycle provides a recommendation without an uncertainty measure for the recommendation, a human operator has no means to determine whether to trust the recommendation (uncertainty as a risk). Compared to recent works that have focused on fairness, accountability, and trustworthiness issues for machine learning algorithms, this project will take a holistic perspective and consider the entire data-science lifecycle. In phase I of the project the investigators will focus on four measures: complexity, resource constraints, uncertainty, and data freshness. In developing a framework to study these measures, this work will prepare the investigators to scale up their activities to other measures in phase II as well as to address larger portions of the data-science lifecycle. The study of each measure brings about foundational challenges that will require expertise from multiple TRIPODS disciplines to address.This project is jointly funded by HDR TRIPODS and the Established Program to Stimulate Competitive Research (EPSCoR).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.

数据驱动的发现正在渗透社会的关键织物。不可靠的发现导致决策可能对社会，辩护和个人产生深远的灾难性后果。因此，产生发现和决策的数据科学生命周期的可靠性是一个关键问题，需要新的整体观点和正式的基础。该项目将在爱荷华州立大学建立可靠的数据驱动发现（D4）研究所，该研究所将推进基础研究，以确保数据驱动的发现具有高质量。 D4研究所的活动将对数据科学生命周期的可靠性产生变革性的影响。首先，问题定义本身将通过帮助学术界以外的未来创新来产生重大影响。尽管可靠性的概念在计算机系统文献中得到了充分研究，但数据科学的挑战将现有知识的边界推向未知。该研究所的工作将定义D4，并通过提供D4的变革性理论来增加数据科学对社会的利益。第二种影响将来自该研究所促进的共同词汇发展的过程，其结果将鼓励跨三脚架学科和领域专家的专家协作，以协作共同的目标和挑战。第三，该研究所将通过为基础研究提供资金来为D4设定研究方向，该研究将产生一系列影响。第四，该研究所将通过中西部大数据暑期学校和D4研讨会等活动来促进对各种数据科学家的跨学科培训。该项目将通过促进基础研究来推动数据科学的理论基础，以使了解数据科学生命周期的可靠性的风险，以形式化数据科学生命周期的可靠性度量的严格数学基础，并确定机制，以创造可靠的数据科学生命周期。该项目定义了可能导致数据驱动的发现失败的原因，以及计划，获取，管理，分析和推断数据的流程作为数据科学生命周期。例如，一种非常昂贵的推理程序可以将延迟信息传递给面临截止日期的人类操作员（复杂性作为风险）；如果数据科学生命周期提供了建议，而没有针对建议的不确定性措施，则人类操作员无需确定是否信任建议（不确定性作为风险）。与最近关注机器学习算法的公平，问责制和可信赖性问题的作品相比，该项目将采用整体观点并考虑整个数据科学生命周期。在项目的第一阶段，研究人员将重点关注四个措施：复杂性，资源限制，不确定性和数据新鲜度。在制定研究这些措施的框架时，这项工作将使研究人员准备将其活动扩展到第二阶段的其他措施，并解决数据科学生命周期的较大部分。对每种措施的研究都带来了基本挑战，这些挑战需要多个三脚架学科的专业知识才能解决。该项目由HDR三脚架共同资助和启发竞争性研究的既定计划（EPSCOR）。该奖项反映了NSF的法定任务，并认为通过基金会的知识优点和广泛的crietia crietia cribitia criperia criperia crigitia criperia criperia criperia criperia criperia criperia criperia均值得通过评估。

项目成果

期刊论文数量（50）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Accelerating the distributed Kaczmarz algorithm by strong over-relaxation

通过强过度松弛加速分布式 Kaczmarz 算法

DOI：
10.1016/j.laa.2020.10.035
发表时间：
2021
期刊：
Linear Algebra and its Applications
影响因子：
1.1
作者：
Borgard, Riley;Harding, Steven N.;Duba, Haley;Makdad, Chloe;Mayfield, Jay;Tuggle, Randal;Weber, Eric S.
通讯作者：
Weber, Eric S.

Size-Constrained k-Submodular Maximization in Near-Linear Time

近线性时间内尺寸约束的 k 子模最大化

DOI：
发表时间：
2023
期刊：
Uncertainty in Artificial Intelligence
影响因子：
0
作者：
Nie, Guanyu;Zhu, Yanhui;Nadew, Yiddiya Y.;Basu, Samik;Pavan, A.;Quinn, Christopher John}
通讯作者：
Quinn, Christopher John}

Repairing Deep Neural Networks: Fix Patterns and Challenges