CAREER: Algorithmic Foundations for Social Data

Harvard University, Cambridge MA

Investigators

Abstract

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.