Network data --- i.e., collections of measurements on pairs, or tuples, of units in a population of interest --- are ubiquitous nowadays in a wide range of machine learning applications, from molecular biology to marketing on social media platforms. Surprisingly, assumptions underlying popular statistical methods are often untenable in the presence of network data. Established machine learning algorithms often break when dealing with combinatorial structure. And the classical notions of variability, sample size and ignorability take unintended connotations. These failures open to door to a number of technical challenges, and to opportunities for introducing new fundamental ideas and for developing new insights. In this talk, I will discuss open statistical and machine learning problems that arise when dealing with large networks, mostly focusing on modeling and inferential issues, and provide an overview of key technical ideas and recent results and trends.

 

Edoardo M. Airoldi is an Associate Professor of Statistics at Harvard University, where he leads the Harvard Laboratory for Applied Statistical Methodology. He holds a holds Ph.D. in Computer Science and an M.Sc. in Statistics from Carnegie Mellon University, and a B.Sc. in Mathematical Statistics and Economics from Bocconi University. His current research focuses on statistical theory and methods for designing and analyzing experiments in the presence of network interference, and on inferential issues that arise in models of network data. He works on applications in molecular biology and proteomics, and in social media analytics and marketing. Airoldi is the recipient several research awards including the ONR Young Investigator Award, the NSF CAREER Award, and the Alfred P. Sloan Research Fellowship, and has received several outstanding paper awards including the Thomas R. Ten Have Award for his work on causal inference, and the John Van Ryzin Award for his work in biology. He has recently advised the Obama for America 2012 campaign on their social media efforts, and serves as a technical advisor at Nanigans and Maxpoint.