Press Releases
Professor Sinan Aral Awarded $40,000 IBM Faculty Award
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Sinan Aral, assistant professor of Information, Operations and Management Sciences, was given the 2009 IBM faculty award, a cash grant that is awarded annually as a result of a worldwide competition created to foster collaboration between researchers at leading universities and those in IBM research, development and services organizations. The prize also aims to promote courseware and curriculum innovation to stimulate growth in disciplines that are strategic to IBM. Nominated by IBM employees, Professor Aral was awarded $40,000 to support his research, “Unlocking the Business Value of Information in Large Dynamic Social Networks.”
The main goal of his study is to uncover the business value of social networks inside the enterprise and in the marketplace. Specifically, the research is designed to understand how the movement of information in massive social networks affects the productivity of information workers and population level product adoption and demand patterns. Using data on internal enterprise communication and interaction such as email, phone logs, IM logs and meeting logs, he will map the flow of information in real firms over time, and use these data to estimate dynamic statistical models of the productivity and performance of individuals and teams. The analysis will combine social network analysis with topic models of the information content exchanged between workers to identify expertise, knowledge flows and methods for optimizing knowledge sharing. In addition, he will use data on massive online social networks to identify peer influence in product adoption and demand.
The main goal of his study is to uncover the business value of social networks inside the enterprise and in the marketplace. Specifically, the research is designed to understand how the movement of information in massive social networks affects the productivity of information workers and population level product adoption and demand patterns. Using data on internal enterprise communication and interaction such as email, phone logs, IM logs and meeting logs, he will map the flow of information in real firms over time, and use these data to estimate dynamic statistical models of the productivity and performance of individuals and teams. The analysis will combine social network analysis with topic models of the information content exchanged between workers to identify expertise, knowledge flows and methods for optimizing knowledge sharing. In addition, he will use data on massive online social networks to identify peer influence in product adoption and demand.