TRIPODS: Institute for Foundations of Data Science

TRIPODS：数据科学研究所

• 批准号: 2023495
• 负责人: Lise Getoor
• 金额: $ 223.04万
• 依托单位: University of California-Santa Cruz
• 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=2023495&HistoricalAwards=false
• 关键词: TRIPODS; Institute; Foundations; Data; Science

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.

数据科学对科学和社会产生了巨大的影响，但是它的成功正在揭示迫切需要进一步进步的新挑战。许多机器学习过程产生的结果和决定对数据中的错误和损坏并不强大。数据科学算法正在产生偏见和不公平的结果，因为对数据隐私的担忧继续存在。和适合动态，交互式环境的机器学习系统不如静态问题的相应工具发达。只有通过对数据科学基础的呼吁，我们才能理解并应对诸如此类的挑战。新的数据科学基金会（IFD）基于三个三脚架阶段研究所的工作，将来自华盛顿大学，威斯康星州 - 麦迪逊，加利福尼亚 - 圣克鲁斯和芝加哥大学的研究人员汇集在一起​​，围绕解决这些关键问题的目的而组织。 IFD的成员在数学，统计学和理论计算机科学的三脚架学科中具有互补的优势，并通过综合不同领域的知识和经验来实现合作的良好合作记录。 IFD的学生和博士后成员将接受培训，以精通几个学科的语言，并能够在数据科学的基础上桥接这些社区并进行跨学科研究。 IFD与研究议程一致，将通过研讨会，暑期学校和黑客马拉松与数据科学界参与。其多样化的领导力，致力于公平和包容性，提出了向传统代表性不足的团体推广的广泛计划。该研究所的治理，管理和评估将建立在I.阶段的成功有效模型的基础上，以解决数据科学研究的尖端，IFD将围绕四个核心主题组织其研究。复杂性主题将从多个学科中综合各种复杂性概念，在分析优化和采样方法中取得突破，开发用于评估数据模型复杂性的工具，并寻求具有更好复杂性属性的新方法，以使复杂性成为更强大的工具，以理解数据科学中的算法和发明算法。鲁棒性主题考虑包含错误或离群值的数据，可能是由于对手的，并且将设计用于面对这些错误的数据分析和预测的方法。闭环数据科学的主题通过使用战略和顺序策略来利用已经从过去数据中收集的信息，以有效地揭示数据的信息内容的方式来解决数据的信息。关于道德和算法的主题解决了机器学习，数据隐私以及因果关系和解释性的公平和偏见问题。这四个主题在许多方面相交，大多数IFD研究人员都会在其中两个或多个工作中工作。通过在这些基本方面取得一致的进展，IFD将降低一些障碍，以更好地理解数据科学方法论，并提高其与应用领域的有效性和更广泛的相关性。此外，IFD将组织和主持与数据科学界各个资历相关的活动。年度研讨会将重点关注上面确定的关键问题以及在未来五年内肯定会出现的其他研讨会。全面的外展和教育计划将利用I阶段机构的先前经验，并利用四个地点的机构资源。与学术界，国家实验室和行业的领域科学研究人员的合作，在阐明数据科学基础知识的问题中非常重要，将继续通过IFDS成员可用的许多渠道，包括在Tripods+X计划中建立的渠道。与每个IFDS网站的其他机构的关系将进一步扩展IFD对领域科学和应用的影响。该奖项反映了NSF的法定任务，并使用基金会的智力优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

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: SOCIAL NETWORKS
• 影响因子: 3.1
• 作者: Sosa, Juan;Rodriguez, Abel
• 通讯作者: Rodriguez, Abel

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

• 发表时间: 2022
• 期刊: Workshop on Tractable Probabilistic Modeling (TPM
• 作者: Dickens, Charles;Pryor, Connor;Augustine, Eriq;Abalak, Alon;Getoor, Lise
• 通讯作者: Getoor, Lise

Fairness Transferability Subject to Bounded Distribution Shift

公平可转让性受有界分配变化影响

• 发表时间: 2022
• 期刊: 2022.
• 作者: Chen, Yatong;Raab, Reilly;Wang, Jialu;Liu, Yang
• 通讯作者: Liu, Yang

Context-Aware Online Collective Inference for Templated Graphical Models

模板化图形模型的上下文感知在线集体推理

• 发表时间: 2021
• 期刊: Proceedings of the 38th International Conference on Machine Learning
• 作者: Charles Dickens, Connor Pryor

Estimating Instance-dependent Label-noise Transition Matrix using a Deep Neural Network

使用深度神经网络估计实例相关的标签噪声转移矩阵

• 发表时间: 2022
• 期刊: International Conference on Machine Learning
• 作者: Yang, Shuo;Yang, Erkun;Han, Bo;Liu, Yang;Xu, Min;Niu, Gang;Liu, Tongliang
• 通讯作者: Liu, Tongliang