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

奖：CCF 1740551，首席研究员：伪卡卡德算法工具，支持现代数据科学方法从数据中收集见解、操纵环境和估计世界上潜在的统计特性的方法。随着计算资源的不断增加和大型数据集的空前增长，对可扩展和健壮的算法工具的需求越来越大，这些工具可以以自动化的方式提供对数据的见解，从而帮助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是单一学科的想法所能轻易应对的。该项目的一个中心目标是为解决当代数据科学挑战提供公共语言和统一方法。就其核心而言，计算机科学、数学和统计学这三个学科的每一个都有丰富的复杂性和稳健性理论。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，以统一想法并为应对当代数据科学挑战提供共同语言。PIS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。同时，为了对这项工作产生强大的教育影响，目标是培养学生成为博士后学者，精通支撑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。PIS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，将来自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，而且还影响社会和技术增长。这项工作试图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(从信息理论到计算到黑盒Oracle模型)，(Ii)如何统一健壮性和自适应性概念(例如，随着Oracle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造成的优化挑战，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伸缩工具。这些基金会将直接从私人投资机构与各种技术和科学从业人员的密切合作中获益。该项目的资金来自CEISE计算和通信基金会、CEISE信息技术研究、MPS数学科学部和MPS多学科活动办公室。

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.