Spoken Networks: Analyzing face-to-face conversations and how they shape our social connections

Speaker
Tanzeem Choudhury
Assistant Professor, Computer Science, Dartmouth

When
-

Where
Newell-Simon Hall 1305 (Michael Mauldin Auditorium)

Description

With the proliferation of sensor-rich mobile devices, it is now possible to collect data that continuously capture the real-world social interactions of entire groups of people. These new data sets provide opportunities to study the social networks of people as they are observed “in the wild.” However, traditional methods often model social networks as static and binary, which are inadequate for continuous behavioral data. Networks derived from behavioral data are almost always temporal, are often non-stationary, and have finer grained observations about interactions as opposed to simple binary indicators. Thus, new techniques are needed that can take into account the variable tie intensities and the dynamics of a network as it evolves in time. In this talk, I will provide an overview of the computational framework we have developed for modeling the micro-level dynamics of human interactions as well as the macro-level network structure and its dynamics from local, noisy sensor observations. Furthermore, by studying the micro and macro levels simultaneously we are able to link dyad-level interaction dynamics (local behavior) to network-level prominence (a global property). I will conclude by providing some specific examples of how the methods we have developed can be applied more broadly to better understand and enhance the lives and health of people.

Based on joint work with Danny Wyatt (University of Washington), James Kitts (Columbia), Jeff Bilmes (University of Washington), Andrew Campbell (Dartmouth), and Ethan Berke (Dartmouth Medical School)

Speaker's Bio

Tanzeem Choudhury is an assistant professor in the computer science department at Dartmouth. She joined Dartmouth in 2008 after four years at Intel Research Seattle. She received her PhD from the Media Laboratory at MIT. Tanzeem develops systems that can reason about human activities, interactions, and social networks in everyday environments. Tanzeem’s doctoral thesis demonstrated for the first time the feasibility of using wearable sensors to capture and model social networks automatically, on the basis of face-to-face conversations. MIT Technology Review recognized her as one of the top 35 innovators under the age of 35 (2008 TR35) for her work in this area. Tanzeem has also been selected as a TED Fellow and is a recipient of the NSF CAREER award.

Speaker's Website
http://www.cs.dartmouth.edu/~tanzeem/

Host
Jason Hong