Dr. Volinsky received his Ph.D. in statistics from University of Washington in 1997 and has been working at AT&T research ever since. He is currently the group leader of the statistics research group at AT&T research and his research interests include:
+ Models and analysis of graph and network data, graph matching;
+ Statistical computation and visualization, particularly for graph and network data (mostly Splus/R);
+ Analysis techniques for massive data sets, data mining;
+ Bayesian Modeling, specifically Bayesian Model Averaging (BMA). He maintains a BMA web page which includes downloadable software;
+ Statistical applications to baseball, sabermetrics

When studying large transactional networks such as telephone call detail data, credit card transactions, or web clickstream data, graphs are a convenient and informative way to represent data. In these graphs, nodes represent the transactors, and edges the transactions between them. When these edges have a time stamp, we have a "dynamic graph" where the edges are born and die through time. I will present a framework for representing and analyzing dynamic graphs, with a focus on the massive graphs found in telecommunications and Internet data. The graph is parameterized with three parameters, defining an approximation to the massive graph which allows us to prune noise from the graph. When compared to using the entire data set, the approximation actually performs better for certain predictive loss functions. In this talk I will demonstrate the application of this model to a telecommunications fraud problem, where we are looking for patterns in the graph associated with fraud.