Data-Powered Algorithms

本计画将集中在以下几个方面：自我改善演算法、线上资料重建、次线性演算法、降维、低熵资料结构、非一致价格计算、资料分析。拟议的研究范围有意广泛。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思路和广泛适用的方法的出现。这一提议的智力价值是为数据驱动算法的系统研究奠定基础。这项研究将包括理论研究和大量的计算机实验。数据驱动算法的主要受益者来自工程和自然科学，这就是该提案将寻求更广泛影响的地方;特别是，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定量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.