Data-Powered Algorithms

本项目旨在关注这一发展的几个方面：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；以及数据分析。拟议的研究范围是有意广泛的。事实上，PI相信，在这些问题上的进展将取决于共同线索和广泛适用方法的出现，这一建议的智力价值是为系统研究数据驱动的算法奠定基础，这一点得到了他的初步调查的支持。EORT将包括理论研究和广泛的计算机实验。数据驱动算法的主要好处来自工程学和自然科学，这也是这项提议将寻求更广泛影响的地方；具体地说，通过PI与生物学家和物理学家正在进行的研究合作，以及在教育方面，他与他们共同努力，为量化科学家开发为期一年的基于算法的课程。

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.