Processing Social Data

  • Devavrat Shah | MIT

An important byproduct of the emergence of social networking platforms is an access to abundance of social data in all forms: blogs, clicks, facebook feeds, transactions and tweets. It is of great interest to process this large volume of highly unstructured data to facilitate business decisions, public policy making or better social living. The key challenge lies in the fact that even though data is large in volume, the information content is very limited. Therefore, extracting meaningful answers has become a challenging computational and statistical task. In this talk, I will discuss how to resolve it successfully for important questions arising in the context of crowd-sourcing, ranking and viral advertising. The key to our success lies in the identification of the appropriate statistical framework for the problems at hand.

Speaker Details

Devavrat Shah is currently a Jamieson associate professor with the department of electrical engineering and computer science, MIT. He is a member of the Laboratory for Information and Decision Systems (LIDS) and Operations Research Center (ORC). His research focus is on theory of large complex networks which includes network algorithms and statistical inference. He has received 2008 ACM Sigmetrics Rising Star Award and 2010 Erlang Prize from the Applied Probability Society of INFORMS. He currently serves as an associate editor of Operations Research, Queueing Systems and IEEE Transactions on Information Theory.

    • Portrait of Jeff Running

      Jeff Running

Series: Microsoft Research Talks