<html><body>Colloquium Speaker<br>Edoardo Airoldi, Harvard University<br>Tuesday, December 2, 4:30pm<br>Computer Science 105<br><br><br>Statistical and machine learning challenges in the analysis of large networks<br><div id="9fa6274a-1b34-4f45-893b-c0a3fda3060b"><span name="x"></span></div><br><div>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.</div>
<div> </div>
<div>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.</div></body></html>