Nathan Eagle

I am interested in exploring the capability of computers to anticipate human behavior. Much of my research involves applying machine learning and network analysis techniques to large human behavioral datasets.

As a Research Scientist at MIT and a Postdoctoral Fellow at the Santa Fe Institute, I am currently analyzing behavioral datasets consisting of call logs and customer databases that effectively represent the topology and dynamics, respectively, of an entire country's social network. Coupling call log data involving hundreds of millions of unique phone numbers and tens of billions of phone calls with information about purchasing behavior, my collaborators and I are developing algorithms to quantify the diffusion of product adoption and churn.

My doctoral research at the MIT Media Lab used 100 mobile phones as behavioral sensors, programmed to continually log communication (call logs), movement and location (cellular tower IDs), and other people within 5-10 meters (regular Bluetooth scans). The resultant 400,000 hours of behavioral data provided insight into individuals' routines , relationships, and the underlying dynamics governing aggregate behavior. We have named this space Reality Mining.

During the last several years I have also been serving as an Adjunct Professor at the GSTIT in Ethiopia and a Fulbright Lecturer at the University of Nairobi. I have been spending a large portion of my time in Africa to develop EPROM (Entrepreneurial Programming and Research on Mobiles) as part of the Program for Developmental Entrepreneurship at the MIT Design Laboratory. The project's aim is to design a globally applicable mobile phone programming curriculum while fostering mobile phone-related research and entrepreneurship.