Jure Leskovec : 2013 Plenary Session

Activity of millions of humans on the Web leaves massive digital traces, that can be naturally represented and analyzed as complex dynamic networks of human interactions. Today the Web is a `sensor' that captures the pulse of humanity and allows us to observe phenomena that were once essentially invisible to us: the social interactions and collective behavior of hundreds of millions of people. In this talk we discuss how large-scale data analytics can be applied to model user behavior in online networks and to inform the design of future social computing applications: How will a community or a social network evolve in the future? How friends in the network shape one's opinions? What are emerging ideas and trends in the network? How does information flow and mutate as it is passed from a node to node like an epidemic? We discuss algorithmic methods that scale to massive networks and mathematical models that seek to abstract some of the underlying phenomena.

Jure Leskovec is Assistant Professor of Computer Science at Stanford University where he is a member of the Info Lab and the AI Lab. His research focuses on mining large social and information networks. Problems he investigates are motivated by large scale data, the Web and on-line media. This research has won several awards including best paper awards at KDD (2005, 2007, 2010), WSDM (2011), ICDM (2011) and ASCE J. of Water Resources Planning and Management (2009), ACM KDD dissertation award (2009), Microsoft Research Faculty Fellowship (2011), as well as Alfred P. Sloan Fellowship (2012). Jure received his bachelor's degree in Computer Science from University of Ljubljana, Slovenia, Ph.D. in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University. You can follow him on Twitter @jure.