Data-Powered Algorithms

数据驱动的算法

• 批准号: 0634958
• 负责人: Bernard Chazelle
• 金额: $ 40万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-10-01 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0634958&HistoricalAwards=false
• 关键词: Data; Powered; Algorithms

This project intends to focus on several aspects of this development: self-improving algorithms; online data reconstruction; sublinear algorithms; dimension reduction; low en-tropy data structures, nonuniformly priced computation; and data analysis. The scope of the proposed research is intentionally wide-ranging. Indeed, it is the PI's belief, backed by his preliminary investigations, that progress on these topics will rely on the emergence of common threads and broadly applicable methods.The intellectual merit of this proposal is to lay down the foundations for a systematic study of data-powered algorithms. The eort will involve theoretical investigations coupled with extensive computer experimentation. The principal beneciaries of data-powered algorithms come from engineering and the natural sciences, and this is where the broader impact of this proposal will be sought; specically, via the PI's ongoing research collaborations with biologists and physicists and, on the educational front, his joint effort with them to develop a year-long algorithms-based course for quantitative scientists.

这个项目打算重点关注这个开发的几个方面：自我改进算法；在线数据重建；次线性算法；降维；低熵数据结构，非均匀定价计算；和数据分析。拟议研究的范围有意广泛。事实上，PI 相信，在他的初步调查的支持下，这些主题的进展将依赖于共同线索和广泛适用方法的出现。该提案的智力价值在于为数据驱动算法的系统研究奠定了基础。这项工作将涉及理论研究和广泛的计算机实验。数据驱动算法的主要受益者来自工程和自然科学，这也是该提案寻求更广泛影响的领域；具体来说，通过 PI 与生物学家和物理学家的持续研究合作，以及在教育方面，他与他们共同努力为定量科学家开发为期一年的基于算法的课程。