CAREER: Algorithmic Foundations for Social Data

职业：社交数据的算法基础

• 批准号: 1452961
• 负责人: Yaron Singer
• 金额: $ 51.5万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1452961&HistoricalAwards=false
• 关键词: CAREER; Algorithmic; Foundations; Social; Data

In the past several years, there has been a great deal of exposure to the opportunities and promise that lie in large-scale data. Although massive data sets have been collected and analyzed in well over a decade, the excitement is largely due to the relatively recent availability of social data: massive digital records of human interactions. This provides a unique system-wide perspective of collective human behavior which poses fundamental challenges and opportunities. Despite the tremendous progress made in recent years, very few algorithmic frameworks to-date have been purposefully developed for analyzing social data sets.  The goal of this project is to develop frameworks that enable analysis of large-scale social data.  This project seeks novel models that are rich in problems, raise deep questions about computation, and can lead to long-lasting impact on sociology and data science.  From a technical perspective, the goal of the project is to develop appropriate algorithmic machinery with strong theoretical guarantees that translate to results in practice.  The project consists of three main lines of research.  The first line of research seeks to develop a theory to optimize events in the future given a distribution on the consequences of actions we take in the present.  The second line of research considers learnability and scalability of social data, and its interpretation for optimization.  The third line of research considers design of robust optimization algorithms for noisy data.  The methodology includes experimentation on real data sets to develop appropriate algorithmic machinery with strong theoretical guarantees that translate to results in practice. Both undergraduate and graduate curriculum will benefit from the development of courses in this interdisciplinary area.

在过去的几年里，人们大量接触到大规模数据中蕴含的机遇和前景。尽管十多年来已经收集和分析了大量数据集，但令人兴奋的很大程度上是由于社交数据的相对较新的可用性：人类互动的大量数字记录。这为人类集体行为提供了独特的全系统视角，带来了根本性的挑战和机遇。尽管近年来取得了巨大进步，但迄今为止，很少有专门开发用于分析社交数据集的算法框架。  该项目的目标是开发能够分析大规模社交数据的框架。  该项目寻求问题丰富的新颖模型，提出有关计算的深刻问题，并对社会学和数据科学产生长期影响。  从技术角度来看，该项目的目标是开发适当的算法机制，并具有强有力的理论保证，可以转化为实践结果。  该项目由三个主要研究方向组成。  第一线研究旨在发展一种理论，根据我们当前采取的行动的后果分布来优化未来的事件。  第二条研究考虑了社交数据的可学习性和可扩展性，及其对优化的解释。  第三条研究考虑针对噪声数据设计鲁棒优化算法。  该方法包括对真实数据集进行实验，以开发适当的算法机制，并具有强有力的理论保证，可转化为实践结果。 本科生和研究生课程都将从这个跨学科领域的课程开发中受益。