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:
- 金额:$ 150万
- 依托单位:
- 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:
- 关键词:
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到社会的关键结构中。不可靠的发现导致的决策可能对社会、国防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基础。该项目将在爱荷华州立大学建立可靠数据驱动发现 (D4) 研究所,该研究所将推进基础研究,确保数据驱动发现的高质量。 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产生重大影响。虽然可靠性的概念在计算机系统文献中得到了充分研究,但数据科学中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益处。第二个影响将来自该研究所促进的共享词汇开发过程,及其结果将鼓励 TRIPODS 学科的专家和领域专家就共同的目标和挑战进行合作。第三,该研究所将通过为基础研究提供资金来确定 D4 的研究方向,这将产生一系列单独的影响。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,促进多元化数据科学家骨干的跨学科培训。该项目将通过促进基础研究来推进数据科学的理论基础,以了解数据科学生命周期可靠性的风险,形式化数据科学生命周期可靠性度量的严格数学基础,并确定机制创建可靠的数据科学生命周期。该项目将风险定义为可能导致数据驱动发现失败的原因,以及规划、获取、管理、分析和推断数据的过程统称为数据科学生命周期。例如,一个非常昂贵的推理过程可以向面临最后期限(复杂性作为风险)的人类操作员提供迟到的信息;如果数据科学生命周期提供的建议没有不确定性度量,那么操作员就无法确定是否信任该建议(不确定性是一种风险)。与最近关注机器学习算法的公平性、问责制和可信度问题的工作相比,该项目将采取整体视角并考虑整个数据科学生命周期。在该项目的第一阶段,研究人员将重点关注四个指标:复杂性、资源限制、不确定性和数据新鲜度。在开发研究这些措施的框架时,这项工作将使研究人员做好准备,将他们的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更大部分问题。每项措施的研究都带来了基础性挑战,需要 TRIPODS 多个学科的专业知识来解决。该项目由 HDR TRIPODS 和刺激竞争性研究既定计划 (EPSCoR) 共同资助。该奖项反映了 NSF 的法定使命,并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。
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)
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:
- 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}
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:
- 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.
Repairing Deep Neural Networks: Fix Patterns and Challenges
- DOI:10.1145/3377811.3380378
- 发表时间:2020-05
- 期刊:
- 影响因子:0
- 作者:Md Johirul Islam;Rangeet Pan;Giang Nguyen;Hridesh Rajan
- 通讯作者:Md Johirul Islam;Rangeet Pan;Giang Nguyen;Hridesh Rajan
Fiduciary Responsibility: Facilitating Public Trust in Automated Decision Making
信托责任:促进公众对自动决策的信任
- DOI:10.23919/jsc.2022.0017
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Harper, Shannon B.;Weber, Eric S.
- 通讯作者:Weber, Eric S.
Fairify: Fairness Verification of Neural Networks
- DOI:10.1109/icse48619.2023.00134
- 发表时间:2022-12
- 期刊:
- 影响因子:0
- 作者:Sumon Biswas;Hridesh Rajan
- 通讯作者:Sumon Biswas;Hridesh Rajan
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Hridesh Rajan其他文献
A case for explicit join point models for aspect-oriented intermediate languages
面向方面中间语言的显式连接点模型的案例
- DOI:
10.1145/1230136.1230140 - 发表时间:
2007 - 期刊:
- 影响因子:0
- 作者:
Hridesh Rajan - 通讯作者:
Hridesh Rajan
Automating Cut-off for Multi-parameterized Systems
多参数化系统的自动切断
- DOI:
- 发表时间:
2010 - 期刊:
- 影响因子:0
- 作者:
Youssef Hanna;David Samuelson;Samik Basu;Hridesh Rajan - 通讯作者:
Hridesh Rajan
Gang-of-Four Design Patterns: A Case Study of the Unified Model and the Eos Programming Language
四联设计模式:统一模型和 Eos 编程语言的案例研究
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Hridesh Rajan - 通讯作者:
Hridesh Rajan
Design Patterns : A Canonical Test of Unified Aspect Model
设计模式:统一方面模型的规范测试
- DOI:
- 发表时间:
2005 - 期刊:
- 影响因子:0
- 作者:
Hridesh Rajan;Kevin Sullivan - 通讯作者:
Kevin Sullivan
Intensional Effect Polymorphism
内涵效应多态性
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Yuheng Long;Yu David Liu;Hridesh Rajan - 通讯作者:
Hridesh Rajan
Hridesh Rajan的其他文献
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{{ truncateString('Hridesh Rajan', 18)}}的其他基金
SHF:Small: More Modular Deep Learning
SHF:Small:更加模块化的深度学习
- 批准号:
2223812 - 财政年份:2022
- 资助金额:
$ 150万 - 项目类别:
Standard Grant
Collaborative Research: CCRI: ENS: Boa 2.0: Enhancing Infrastructure for Studying Software and its Evolution at a Large Scale
合作研究:CCRI:ENS:Boa 2.0:增强大规模研究软件及其演化的基础设施
- 批准号:
2120448 - 财政年份:2021
- 资助金额:
$ 150万 - 项目类别:
Standard Grant
Travel Grant to Attend Big Data in Software Engineering Track
参加软件工程大数据课程的旅费补助
- 批准号:
1743070 - 财政年份:2017
- 资助金额:
$ 150万 - 项目类别:
Standard Grant
CI-EN: Boa: Enhancing Infrastructure for Studying Software and its Evolution at a Large Scale
CI-EN:Boa:增强大规模研究软件及其演化的基础设施
- 批准号:
1513263 - 财政年份:2015
- 资助金额:
$ 150万 - 项目类别:
Standard Grant
SHF: Large:Collaborative Research: Inferring Software Specifications from Open Source Repositories by Leveraging Data and Collective Community Expertise
SHF:大型:协作研究:利用数据和集体社区专业知识从开源存储库推断软件规范
- 批准号:
1518897 - 财政年份:2015
- 资助金额:
$ 150万 - 项目类别:
Standard Grant
SHF: Small: Capsule-oriented Programming
SHF:小型:面向胶囊的编程
- 批准号:
1423370 - 财政年份:2014
- 资助金额:
$ 150万 - 项目类别:
Standard Grant
EAGER: Boa: A Community Research Infrastructure for Mining Software Repositories
EAGER:Boa:采矿软件存储库的社区研究基础设施
- 批准号:
1349153 - 财政年份:2013
- 资助金额:
$ 150万 - 项目类别:
Standard Grant
SHF: Small: Phase-Based Tuning for Better Utilization of Performance-Asymmetric Multicores
SHF:小型:基于相位的调整,以更好地利用性能不对称的多核
- 批准号:
1117937 - 财政年份:2011
- 资助金额:
$ 150万 - 项目类别:
Standard Grant
SHF: Small: Collaborative Research: Balancing Expressiveness and Modular Reasoning for Aspect-oriented Programming
SHF:小型:协作研究:平衡面向方面编程的表达性和模块化推理
- 批准号:
1017334 - 财政年份:2010
- 资助金额:
$ 150万 - 项目类别:
Continuing Grant
CAREER: On Mutualism of Modularity and Concurrency Goals
职业:模块化和并发目标的互惠性
- 批准号:
0846059 - 财政年份:2009
- 资助金额:
$ 150万 - 项目类别:
Continuing Grant
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- 项目类别:面上项目
相似海外基金
TRIPODS: Institute for Foundations of Data Science
TRIPODS:数据科学研究所
- 批准号:
2023109 - 财政年份:2020
- 资助金额:
$ 150万 - 项目类别:
Continuing Grant
TRIPODS: Institute for Foundations of Data Science
TRIPODS:数据科学研究所
- 批准号:
2023239 - 财政年份:2020
- 资助金额:
$ 150万 - 项目类别:
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TRIPODS: Institute for Foundations of Data Science
TRIPODS:数据科学研究所
- 批准号:
2023495 - 财政年份:2020
- 资助金额:
$ 150万 - 项目类别:
Continuing Grant
TRIPODS: Institute for Foundations of Data Science
TRIPODS:数据科学研究所
- 批准号:
2023166 - 财政年份:2020
- 资助金额:
$ 150万 - 项目类别:
Continuing Grant
HDR TRIPODS: Building the Foundation for a Data-Intensive Studies Center-
HDR TRIPODS:为数据密集型研究中心奠定基础-
- 批准号:
1934553 - 财政年份:2019
- 资助金额:
$ 150万 - 项目类别:
Continuing Grant