Inferring Social Networks Automatically from Sensor Data


July 22, 2009


Human behavior in the real world is a difficult thing to study: it is not possible to have human observers follow someone around all day, and surveys often tend to be biased and unreliable. On the other hand, sensor data is easy to collect but inferring human behavior from this data is still a challenging problem. In this talk, I will present some of the computational methods we have developed for inferring the micro-level dynamics of human interactions as well as the macro-level latent social network structure from local, noisy sensor observations.
By studying the micro and macro levels simultaneously we are able to explore the relationship between interaction dynamics (local behavior) and network prominence (a global property), and can identify the behavioral correlates of tie strengths within a network. We believe these methods have the potential to allow more quantitative inquiry into human behavior and social dynamics. They will also enable us to develop socially aware ubiquitous computing systems that are cognizant of and responsive to users’ engagement with their social environment.


Tanzeem Choudhury

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 research was the first to demonstrate 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 35 innovators under the age of 35 (2008 TR35) for her work in this area. Tanzeem has also been selected as a TED Fellow (2009), PopTech Science and Public Leadership Fellow (2010), and is a recipient of the NSF CAREER award (2009). More information can be found at Tanzeem’s webpage: