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  • 17 Sep 2010
  • 9:30 AM
  • Jacob Slonim Conference Room (430) 6050 University Ave., Halifax

Faculty of Computer Science

6050 University Avenue

Dalhousie University

Halifax, NS


Speaker:   Edo Airoldi

           Department of Statistics, FAS Center for Systems Biology

           Faculty of Arts & Sciences, Harvard University

Title:     Modeling approaches for analyzing complex networks

Date:      Friday Sept 17, 2010

Time:      9:30 a.m.

Location:  Jacob Slonim Conference Room (430)

           6050 University Ave., Halifax

Note:      Coffee and cookies will be provided, courtesy of Faculty of

           Computer Science.

Abstract: Networks are ubiquitous in science and have become a focal

point for discussion in everyday life. Formal statistical models for the

analysis of network data have emerged as a major topic of interest in

diverse areas of study, and most of these involve a collections of

measurements on pairs of objects. Probability models on graphs date back

to 1959. Along with empirical studies in social psychology and sociology

from the 1960s, these early works generated an active “network

community” and a substantial literature in the 1970s. This effort moved

into the statistical literature in the late 1970s and 1980s, and the

past decade has seen a burgeoning network literature in statistical

physics and computer science. The growth of the World Wide Web and the

emergence of online “networking communities” such as Facebook and

LinkedIn, and a host of more specialized professional network

communities has intensified interest in the study of networks and

network data. In this talk, I will review a few ideas that are central

to this burgeoning literature, placing emphasis on modeling approaches

available for data analysis, and review some of the recent work that is

going on in my group.

Speaker Bio: In December 2006, Dr. Airoldi received a Ph.D. from

Carnegie Mellon, working on statistical machine learning and the

analysis of complex systems with Stephen Fienberg and Kathleen Carley.

His dissertation introduced statistical and computational elements of

graph theory that support data analysis of complex systems and their

evolution. Till December 2008, he was a postdoctoral fellow in the

Lewis-Sigler Institute for Integrative Genomics of Princeton University

working with Olga Troyanskaya, David Botstein, and James Broach. He

developed mechanistic models to gain computational insights into aspects

of the molecular and cellular biology that are not directly observable

with experimental probes. He has been working closely with biologists

and in the areas of cellular differentiation, cellular development and

cancer, since.

Speaker URL:


Host contact: Jeannette Jannsen jannsen@mathstat.dal.ca and

              Nauzer Kalyaniwalla nauzerk@cs.dal.ca

1301-1809 Barrington Street, Halifax, NS B3J 3K8

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