Department of Statistics and Department of Psychological and Brain Sciences
Data mining of network data often focuses on classification
methods from machine learning, statistics, and pattern
recognition perspectives. These techniques have been
described by many, but many of these researchers are unaware
of the rich history of classification and clustering
techniques originating in social network analysis.
The growth of rich social media, on-line communities, and
collectively produced knowledge resources has greatly
increased the need for good analytic techniques for social
networks. We now have the opportunity to analyze social
network data at unprecedented levels of scale and temporal
resolution; this has led to a growing body of research at the
intersection of the computing, statistics, and the social and
This talk discusses some of the current challenges in the
analysis of large-scale social network data, focusing on the
inference of social processes from data. The invasion of
network science by computer scientists has produced much
interesting, both good and bad, research.
Stan Wasserman, an applied statistician, joined the Departments of Sociology and
Psychology at Indiana University in Bloomington in Fall 2004, as Rudy Professor
of Statistics, Psychology, and Sociology. He also has an appointment in the Karl
F. Schuessler Institute for Social Research. Prior to moving to Indiana, he held
faculty positions at Carnegie-Mellon University, University of Minnesota, and University
of Illinois, in the disciplines of Statistics, Psychology, and Sociology; in addition,
at Illinois, he was a part-time faculty member in the Beckman Institute of Advanced
Science and Technology, and has had visiting appointments at Columbia University
and the University of Melbourne. In 2005, he helped create the new Department of
Statistics in Bloomington, and became its first chair in 2006.
Wasserman is best known for his work on statistical models for social networks and
for his text, co-authored with Katherine Faust, Social Network Analysis: Methods
and Applications. His other books have been published by Sage Publications and Cambridge
University Press. He has published widely in sociology, psychology, and statistics
journals, and has been elected to a variety of leadership positions in the Classification
Society of North America and the American Statistical Association. He teaches courses
on applied statistics.
He is a fellow of the Royal Statistical Society, and an honorary fellow of the American
Statistical Association and the American Association for the Advancement of Science.
He has been an Associate Editor of a variety of statistics and methodological journals
(Psychometrika, Journal of the American Statistical Association, Sociological Methodology,
to name a few), as well as the Book Review Editor of Chance. His research has been
supported over the years by NSF, ONR, ARL, and NIMH.
Wasserman was also Chief Scientist of Visible Path Corporation in Foster City, California,
a software firm engaged in developing social network analysis for corporate settings.
He currently blogs at http://www.iq.harvard.edu/blog/netgov/ He was educated at
the University of Pennsylvania (receiving two degrees in 1973) and Harvard University
(Ph.D., in Statistics, 1977).