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TRIPODS: Algorithms for Data Science: Complexity, Scalability, and Robustness.

TRIPODS：数据科学算法：复杂性、可扩展性和稳健性。

基本信息

批准号：
1740551
负责人：
Sham Kakade
金额：
$ 150万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740551&HistoricalAwards=false
关键词：
TRIPODS Algorithms Data Science Complexity

项目摘要

Award: CCF 1740551, Principal Investigator: Sham KakadeAlgorithmic tools underpin the ways in which modern data science methods glean insights from data, manipulate their environments, and estimate underlying statistical properties in the world. With increasing computational resources and an unprecedented growth of large datasets, there is an increased need for scalable and robust algorithmic tools which can provide insights into data in an automated manner, and thus, help to accelerate the pace of science and engineering. The modern challenges that a range of fields now face are no longer easily handled by ideas from a single discipline. A central goal of this project is to provide a common language and unifying methods for addressing contemporary data science challenges. At their core, each of the three disciplines of computer science, mathematics, and statistics has rich theories of complexity and robustness. These theories have influenced the design of the available tools that are used to address real world computational problems. Going forward, this project seeks new algorithms and design principles that unify ideas and provide a common language for addressing contemporary data science challenges. The PIs will draw from their expertise in computer science, mathematics, and statistics to aid in providing these unifying approaches. In parallel, aiming for a strong educational impact of the work, the aim is to train students an postdoctoral scholars to be well-versed in different areas underpinning data science and will incorporate appropriate theoretical ideas into a data science curriculum. The PIs will also organize events that help train students (including a hackathon and a bootcamp) and a research workshop that bring together researchers from the three disciplines for discussion and collaboration.In particular, the research objectives of this project are in unifying basic abstractions and techniques in order to yield not only further breakthroughs in all three fields, but also to impact societal and technological growth. The complexity and algorithmic questions this work seeks to address include: (i) how to unify various notions of complexity (which range from information theoretic to computational to black box oracle models), (ii) how to unify notions of robustness and adaptivity (e.g., how solutions and methods change as oracle models are corrupted by random or adversarial noise), (iii) how to address optimization challenges due to nonconvexity, and (iv) how to use these unified approaches to design more effective scalable tools, in theory and practice. These foundations will directly draw from the PIs close collaborations with various technological and scientific practitioners. Funds for the project come from CISE Computing and Communications Foundations, CISE Information Technology Research, MPS Division of Mathematical Sciences, and MPS Office of Multidisciplinary Activities.

奖项：CCF 1740551，首席研究员：Sham Kakade 算法工具支撑着现代数据科学方法从数据中收集见解、操纵环境以及估计世界潜在统计特性的方式。随着计算资源的增加和大型数据集的空前增长，对可扩展且强大的算法工具的需求不断增加，这些工具可以以自动化的方式提供对数据的洞察，从而有助于加快科学和工程的步伐。一系列领域现在面临的现代挑战不再能够通过单一学科的思想轻松应对。该项目的中心目标是提供通用语言和统一方法来应对当代数据科学挑战。计算机科学、数学和统计学这三个学科的核心都拥有丰富的复杂性和鲁棒性理论。这些理论影响了用于解决现实世界计算问题的可用工具的设计。展望未来，该项目寻求新的算法和设计原则，以统一思想并为应对当代数据科学挑战提供通用语言。 PI 将利用他们在计算机科学、数学和统计学方面的专业知识来帮助提供这些统一的方法。与此同时，为了使这项工作产生强大的教育影响，目的是培养学生和博士后学者，使其精通支持数据科学的不同领域，并将适当的理论思想纳入数据科学课程。 PI 还将组织帮助培训学生的活动（包括黑客马拉松和训练营）和研究研讨会，将三个学科的研究人员聚集在一起进行讨论和合作。特别是，该项目的研究目标是统一基本抽象和技术，以便不仅在所有三个领域取得进一步突破，而且影响社会和技术发展。这项工作寻求解决的复杂性和算法问题包括：（i）如何统一各种复杂性概念（从信息论到计算再到黑盒预言机模型），（ii）如何统一鲁棒性和适应性的概念（例如，随着预言机模型被随机或对抗性噪声破坏，解决方案和方法如何变化），（iii）如何解决由于非凸性而导致的优化挑战，以及 (iv) 如何在理论和实践中使用这些统一的方法来设计更有效的可扩展工具。这些基金会将直接借鉴 PI 与各种技术和科学从业者的密切合作。该项目的资金来自 CISE 计算和通信基金会、CISE 信息技术研究中心、MPS 数学科学部和 MPS 多学科活动办公室。