Campus Event Calendar: Tanzeem Choudhury (06/25/2010 in G26/206)

Campus Event Calendar

Campus Event Calendar:
- All Upcoming:
  - only for D1
  - only for D2
  - only for INET
  - only for D4
  - only for D5
  - only for D6
  - only for RG1
  - Mailing Lists
  - by Speaker
  - by Type
  - by Category
  - by Title
  - Calendar
  - RSS Feed
- History of Events:

Event Entry

What and Who

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

Tanzeem Choudhury

Dartmouth College

SWS Colloquium

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. More information can be found at Tanzeem's webpage:http://www.cs.dartmouth.edu/~tanzeem

SWS

Expert Audience

English

Note: We use this to send email in the morning.

Date, Time and Location

Friday, 25 June 2010

13:30

60 Minutes

G26

206

Kaiserslautern

Abstract

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)

Contact

Brigitta Hansen

0681 - 9325691

--email hidden

System used:

Meeting URL:

Meeting ID:

Passcode:

passcode not visible

Code Visible for:

logged in users only

Rose Hoberman, 07/01/2010 18:46
Brigitta Hansen, 07/01/2010 15:25
Brigitta Hansen, 06/22/2010 09:35 -- Created document.

Imprint / Impressum | Data Protection / Datenschutzhinweis