TRIPODS: Institute for Foundations of Data Science

TRIPODS：数据科学研究所

基本信息

批准号：
2023166
负责人：
Maryam Fazel
金额：
$ 485.3万
依托单位：
University of Washington
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

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2023166&HistoricalAwards=false
关键词：
TRIPODS Institute Foundations Data Science

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影响，但它的成功也揭示了阻碍进一步进步的紧迫新挑战。许多机器学习过程产生的结果和决策对于数据中的错误和损坏并不稳健；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；适用于动态、交互式环境的机器学习系统的开发程度不如适用于静态问题的相应工具。只有借助数据科学的基础，我们才能理解并应对此类挑战。以三个 TRIPODS 第一阶段研究所的工作为基础，新的数据科学研究所 (IFDS) 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，围绕解决这些问题的目标组织起来关键问题。 IFDS 的成员在数学、统计学和理论计算机科学的 TRIPODS 学科中具有互补的优势，并且在通过综合不同领域的知识和经验来突破理论界限的合作方面有着良好的记录。 IFDS 的学生和博士后成员将接受培训，精通多个学科的语言，并能够在这些社区之间架起桥梁，并在数据科学的基础上进行跨学科研究。根据其研究议程，IFDS 将通过研讨会、暑期学校和黑客马拉松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，以扩大对传统上代表性不足的群体的影响。该研究所的治理、管理和评估将建立在第一阶段开发的成功和高效模型的基础上。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其研究。复杂性主题将综合多个学科的各种复杂性概念，突破优化和采样方法的分析，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，使复杂性成为更强大的工具用于理解和发明数据科学中的算法。稳健性主题考虑包含错误或异常值（可能是由于对手造成）的数据，并将设计面对这些错误时稳健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的信息的战略和顺序策略。道德和算法主题解决了机器学习、数据隐私、因果关系和可解释性中的公平和偏见问题。这四个主题在很多方面都有交叉，大多数 IFDS 研究人员都会研究其中的两个或多个主题。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论、提高其有效性以及与应用领域更广泛的相关性。此外，IFDS 将组织和举办让数据科学界各个级别的人员参与的活动。年度研讨会将重点讨论上述关键问题以及未来五年肯定会出现的其他问题。全面的外展和教育计划将借鉴第一阶段机构的以往经验，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 成员可用的多种渠道继续进行，包括 TRIPODS+X 计划中建立的渠道。与每个 IFDS 站点的其他机构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 NSF 的法定使命，并通过使用基金会的智力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（41）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

High-Dimensional Experimental Design and Kernel Bandits

高维实验设计和内核强盗

DOI：
发表时间：
2021-01
期刊：
Proceedings of Machine Learning Research
影响因子：
0
作者：
Camilleri, Romain;Jamieson, Kevin;Katz
通讯作者：
Katz

Stochastic Contextual Bandits with Long Horizon Rewards

具有长期奖励的随机上下文强盗

DOI：
10.1609/aaai.v37i8.26140
发表时间：
2023-06
期刊：
Proceedings of the AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Qin, Yuzhen;Li, Yingcong;Pasqualetti, Fabio;Fazel, Maryam;Oymak, Samet
通讯作者：
Oymak, Samet

Fast First-Order Methods for Monotone Strongly DR-Submodular Maximization

单调强DR子模最大化的快速一阶方法

DOI：
发表时间：
2023-01
期刊：
Proceedings of SIAM Conference on Applied and Computational Discrete Algorithms
影响因子：
0
作者：
Sadeghi, Omid;Fazel, Maryam
通讯作者：
Fazel, Maryam

Near-Optimal Randomized Exploration for Tabular Markov Decision Processes

表格马尔可夫决策过程的近乎最优随机探索