[Ml-stat-talks] Fwd: [talks] Colloquium speaker Edoardo Airoldi- Tuesday, Dec 2, 4:30pm

Barbara Engelhardt bee at CS.Princeton.EDU
Mon Dec 1 14:13:03 EST 2014

Colloquium Speaker
Edoardo Airoldi, Harvard University
Tuesday, December 2, 4:30pm
Computer Science 105

Statistical and machine learning challenges in the analysis of large

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
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