TRIPODS: Institute for Foundations of Data Science

TRIPODS:数据科学研究所

基本信息

  • 批准号:
    2023495
  • 负责人:
  • 金额:
    $ 223.04万
  • 依托单位:
  • 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:
    2020-09-01 至 2025-08-31
  • 项目状态:
    未结题

项目摘要

Data science is making an enormous impact on science and society, but its success is uncovering pressing new challenges that stand in the way of further progress. Outcomes and decisions arising from many machine learning processes are not robust to errors and corruption in the data; data science algorithms are yielding biased and unfair outcomes, as concerns about data privacy continue to mount; and machine learning systems suited to dynamic, interactive environments are less well developed than corresponding tools for static problems. Only by an appeal to the foundations of data science can we understand and address challenges such as these. Building on the work of three TRIPODS Phase I institutes, the new Institute for Foundations of Data Science (IFDS) brings together researchers from the Universities of Washington, Wisconsin-Madison, California-Santa Cruz, and Chicago, organized around the goal of tackling these critical issues. Members of IFDS have complementary strengths in the TRIPODS disciplines of mathematics, statistics, and theoretical computer science, and a proven record of collaborating to push theoretical boundaries by synthesizing knowledge and experience from diverse areas. Students and postdoctoral members of IFDS will be trained to be fluent in the languages of several disciplines, and able to bridge these communities and perform transdisciplinary research in the foundations of data science. In concert with its research agenda, IFDS will engage the data science community through workshops, summer schools, and hackathons. Its diverse leadership, committed to equity and inclusion, proposes extensive plans for outreach to traditionally underrepresented groups. Governance, management, and evaluation of the institute will build on the successful and efficient models developed during Phase I.To address critical issues at the cutting edge of data science research, IFDS will organize its research around four core themes. The complexity theme will synthesize various notions of complexity from multiple disciplines to make breakthroughs in the analysis of optimization and sampling methods, develop tools for assessing the complexity of data models, and seek new methods with better complexity properties, to make complexity a more powerful tool for understanding and inventing algorithms in data science. The robustness theme considers data that contains errors or outliers, possibly due to an adversary, and will design methods for data analysis and prediction that are robust in the face of these errors. The theme on closed-loop data science tackles the issues of acquiring data in ways that reveal the information content of the data efficiently, using strategic and sequential policies that leverage information gathered already from past data. The theme on ethics and algorithms addresses issues of fairness and bias in machine learning, data privacy, and causality and interpretability. The four themes intersect in many ways, and most IFDS researchers will work in two or more of them. By making concerted progress on these fundamental fronts, IFDS will lower several of the barriers to better understanding of data science methodology and to its improved effectiveness and wider relevance to application areas. Additionally, IFDS will organize and host activities that engage the data science community at all levels of seniority. Annual workshops will focus on the critical issues identified above and others that are sure to arise over the next five years. Comprehensive plans for outreach and education will draw on previous experience of the Phase I institutes and leverage institutional resources at the four sites. Collaborations with domain science researchers in academia, national laboratories, and industry, so important in illuminating issues in the fundamentals of data science, will continue through the many channels available to IFDS members, including those established in the TRIPODS+X program. Relationships with other institutes at each IFDS site will further extend the impact of IFDS on domain sciences and applications.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.
数据科学正在对科学和社会产生巨大的影响,但它的成功也揭示了阻碍进一步发展的紧迫新挑战。许多机器学习过程产生的结果和决策对数据中的错误和损坏并不健壮;随着对数据隐私的担忧不断加剧,数据科学算法正在产生有偏见和不公平的结果;与解决静态问题的相应工具相比,适合于动态、交互式环境的机器学习系统开发得还不够好。只有借助于数据科学的基础,我们才能理解并应对这些挑战。新成立的数据科学基础研究所(IFDS)以三个TRIPODS第一阶段研究所的工作为基础,汇集了来自华盛顿大学、威斯康星大学麦迪逊分校、加利福尼亚大学圣克鲁斯分校和芝加哥大学的研究人员,围绕解决这些关键问题的目标组织起来。IFDS的成员在TRIPODS的数学、统计学和理论计算机科学学科方面具有互补优势,并通过综合不同领域的知识和经验来推动理论界限的合作记录。IFDS的学生和博士后成员将接受培训,精通多个学科的语言,能够在这些社区之间建立桥梁,并在数据科学基础上进行跨学科研究。为了配合其研究议程,IFDS将通过研讨会、暑期学校和黑客马拉松等方式吸引数据科学界的参与。其多元化的领导层致力于公平和包容,提出了广泛的计划,向传统上代表性不足的群体伸出援手。研究所的治理、管理和评估将建立在第一阶段开发的成功和高效的模型之上。为了解决数据科学研究前沿的关键问题,IFDS将围绕四个核心主题组织研究。复杂性主题将综合来自多个学科的各种复杂性概念,在优化和采样方法的分析方面取得突破,开发评估数据模型复杂性的工具,寻求具有更好复杂性属性的新方法,使复杂性成为理解和发明数据科学算法的更强大工具。鲁棒性主题考虑包含错误或异常值的数据,可能是由于对手,并将设计在这些错误面前具有鲁棒性的数据分析和预测方法。闭环数据科学的主题解决了以有效揭示数据信息内容的方式获取数据的问题,使用利用从过去数据中收集到的信息的战略和顺序策略。关于伦理和算法的主题解决了机器学习中的公平和偏见、数据隐私、因果关系和可解释性等问题。这四个主题在许多方面相互交叉,大多数IFDS研究人员将研究其中的两个或两个以上。通过在这些基本方面取得协调一致的进展,IFDS将降低一些障碍,以更好地理解数据科学方法,提高其有效性和更广泛的应用领域的相关性。此外,IFDS还将组织和举办各种活动,吸引各个级别的数据科学界人士参与。年度讲习班将集中讨论上述关键问题和今后五年肯定会出现的其他问题。全面的外联和教育计划将借鉴第一阶段研究所以往的经验,并利用四个地点的机构资源。与学术界、国家实验室和工业界的领域科学研究人员的合作,对于阐明数据科学基础问题非常重要,将通过IFDS成员可用的许多渠道继续进行,包括那些在TRIPODS+X计划中建立的渠道。与各个IFDS站点的其他研究所的关系将进一步扩大IFDS对领域科学和应用的影响。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(42)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Fairness Transferability Subject to Bounded Distribution Shift
公平可转让性受有界分配变化影响
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Chen, Yatong;Raab, Reilly;Wang, Jialu;Liu, Yang
  • 通讯作者:
    Liu, Yang
Efficient Learning Losses for Deep Hinge-Loss Markov Random Fields
深度铰链损失马尔可夫随机场的高效学习损失
Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search
性别中立的查询真的是性别中立的吗?
A latent space model for cognitive social structures data
  • DOI:
    10.1016/j.socnet.2020.12.002
  • 发表时间:
    2021-05-01
  • 期刊:
  • 影响因子:
    3.1
  • 作者:
    Sosa, Juan;Rodriguez, Abel
  • 通讯作者:
    Rodriguez, Abel
Unintended Selection: Persistent Qualification Rate Disparities and Interventions
意外选择:持续存在的合格率差异和干预措施
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Lise Getoor其他文献

Soft quantification in statistical relational learning
  • DOI:
    10.1007/s10994-017-5647-3
  • 发表时间:
    2017-07-12
  • 期刊:
  • 影响因子:
    2.900
  • 作者:
    Golnoosh Farnadi;Stephen H. Bach;Marie-Francine Moens;Lise Getoor;Martine De Cock
  • 通讯作者:
    Martine De Cock
Research Challenges and Opportunities in Knowledge Representation
知识表示的研究挑战和机遇
  • DOI:
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Natasha Noy;Deborah L. McGuinness;Eyal Amir;Chitta Baral;Michael Beetz;S. Bechhofer;C. Boutilier;Anthony Cohn;J. Kleer;Michel Dumontier;Tim Finin;Kenneth D. Forbus;Lise Getoor;Yolanda Gil;J. Heflin;P. Hitzler;Craig A. Knoblock;Henry Kautz;Yuliya Lierler;Vladimir Lifschitz;Peter F. Patel;C. Piatko;D. Riecken;M. Schildhauer
  • 通讯作者:
    M. Schildhauer

Lise Getoor的其他文献

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{{ truncateString('Lise Getoor', 18)}}的其他基金

III: Medium: Collaborative Research: A Unified and Declarative Approach to Causal Analysis for Big Data
III:媒介:协作研究:大数据因果分析的统一声明式方法
  • 批准号:
    1703331
  • 财政年份:
    2017
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Standard Grant
TRIPODS: Towards a Unified Theory of Structure, Incompleteness & Uncertainty in Heterogeneous Graphs
TRIPODS:迈向结构、不完备性的统一理论
  • 批准号:
    1740850
  • 财政年份:
    2017
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Continuing Grant
III: Small: A Theoretical Framework for Practical Entity Resolution in Network Data
III:小:网络数据中实际实体解析的理论框架
  • 批准号:
    1218488
  • 财政年份:
    2012
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Standard Grant
FODAVA: Collaborative Research: Foundations of Comparative Analytics for Uncertainty in Graphs
FODAVA:协作研究:图形不确定性比较分析的基础
  • 批准号:
    0937094
  • 财政年份:
    2009
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Standard Grant
Student Poster Program and Travel Scholarships for International Conference on Machine Learning (ICML) 2009
2009 年国际机器学习会议 (ICML) 学生海报计划和旅行奖学金
  • 批准号:
    0935087
  • 财政年份:
    2009
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Standard Grant
CAREER: Graph Identification
职业:图形识别
  • 批准号:
    0746930
  • 财政年份:
    2008
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Continuing Grant
Student Poster Program and Travel Scholarships for ICML 2008
ICML 2008 学生海报计划和旅行奖学金
  • 批准号:
    0830962
  • 财政年份:
    2008
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Standard Grant
SoD: Data and Meta-Data Integration Maintenance
SoD:数据和元数据集成维护
  • 批准号:
    0438866
  • 财政年份:
    2005
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Standard Grant
Link Mining and Discovery
链接挖掘和发现
  • 批准号:
    0308030
  • 财政年份:
    2003
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Standard Grant

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:
    $ 223.04万
  • 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
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    2020
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    $ 223.04万
  • 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:
    $ 223.04万
  • 项目类别:
    Continuing Grant
HDR TRIPODS: UIC Foundations of Data Science Institute
HDR TRIPODS:UIC 数据科学研究所基础
  • 批准号:
    1934915
  • 财政年份:
    2019
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Continuing Grant
HDR TRIPODS: UT Austin Institute on the Foundations of Data Science
HDR TRIPODS:UT Austin 数据科学基础研究所
  • 批准号:
    1934932
  • 财政年份:
    2019
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Continuing Grant
HDR TRIPODS: Institute for Integrated Data Science: A Transdisciplinary Approach to Understanding Fundamental Trade-offs and Theoretical Foundations
HDR TRIPODS:综合数据科学研究所:理解基本权衡和理论基础的跨学科方法
  • 批准号:
    1934846
  • 财政年份:
    2019
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Continuing Grant
HDR TRIPODS: Penn Institute for Foundations of Data Science
HDR TRIPODS:宾夕法尼亚大学数据科学研究所
  • 批准号:
    1934876
  • 财政年份:
    2019
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Continuing Grant
HDR Tripods: Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS)
HDR 三脚架:德克萨斯 A
  • 批准号:
    1934904
  • 财政年份:
    2019
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Continuing Grant
HDR TRIPODS: Institute for the Foundations of Graph and Deep Learning
HDR TRIPODS:图形和深度学习基础研究所
  • 批准号:
    1934979
  • 财政年份:
    2019
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Continuing Grant
TRIPODS: Institute for Foundations of Data Science (IFDS)
TRIPODS:数据科学研究所 (IFDS)
  • 批准号:
    1740751
  • 财政年份:
    2017
  • 资助金额:
    $ 223.04万
  • 项目类别:
    Continuing Grant
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