Nathan Eagle

Petabytes of data about human movements, transactions, and communication patterns are continuously being inadvertently generated by technology from mobile phones to credit cards. As an Omidyar Fellow at the Santa Fe Institute, I am currently analyzing behavioral datasets that represent the social network topology and dynamics of entire countries within both the developed and developing worlds. Coupling anonymized communication events from hundreds of millions of people with longitudinal data ranging from financial transactions and movement patterns to regional information about access to health care and socioeconomic status, my collaborators and I are developing machine learning and network analysis algorithms that we hope will provide deeper insight into human societies. Ultimately, my research agenda is to determine how we can use these insights to actively improve the lives of the billions of people who generate this data and the societies in which they live.

To that end, I am chairing the AAAI Spring Symposium on Artificial Intelligence for Development (AI-D). This is a new community of diverse academics and practitioners who believe that the volume of behavioral data currently being generated, particularly in the world's most underserved and understudied regions, provides new opportunities for applying artificial intelligence techniques to problems that will aid in the development of societies. I am currently involved in over a dozen active AI-D research projects involving data on human health, movement, communication, and financial transactions. These collaborations include optimizing the allocation of malaria eradication resources in Kenya, detecting behavioral anomalies associated with outbreaks of cholera in Rwanda, quantifying the dynamics of slums in Nairobi, uncovering patterns in regional communication data associated with the spread of HIV and contraception norms in the Dominican Republic, and assessing the social impact of previous policy decisions ranging from road construction to the placement of latrines throughout the developing world.

I moved to East Africa in 2006, where I jointly served as a research faculty member at MIT, an Adjunct Professor at the GSTIT in Ethiopia and a Fulbright Lecturer at the University of Nairobi. A large portion of my time has been dedicated to creating an initiative called EPROM (Entrepreneurial Programming and Research on Mobiles). EPROM's aim has been to disseminate a globally applicable mobile phone programming curriculum while fostering mobile phone-related research and entrepreneurship. To date, the EPROM curriculum is currently being taught within Computer Science departments in ten Sub-Saharan African countries. Thousands of African computer science students have completed these courses and many have formed a variety of mobile phone start-ups based in Nairobi, Kigali, Addis-Ababa, and beyond.

One such entrepreneurial venture is my own, txteagle - an "artificial artificial intelligence" system enabling the 3 billion mobile phone subscribers living in the developing world to earn small amounts of money by completing simple tasks for companies who pay them in airtime or mPesa (mobile money in Kenya).

My doctoral research introduced the idea of Reality Mining and explored the capability of computers to anticipate human behavior through the use of 100 mobile phones as behavioral sensors, programmed to continually log communication, movement, and proximate phones. The resultant 400,000 hours of behavioral data has been downloaded by thousands of researchers and used in over 100 publications. I am involved in similar studies with subjects ranging from office workers in Helsinki, smokers in New York, teenagers in Kilfi, and male prostitutes in Mtwapa, Kenya.