Nathan Eagle knows what you’re doing and who you’re doing it with.

As a Visiting Professor at the famed MIT Media Lab, Adjunct Professor at Harvard University, Omidyar Fellow at the Santa Fe Institute and CEO of txteagle Inc, Eagle understands how data from our cell phones and other mobile devices can be used to observe and predict the activities of individuals and groups, which he calls “reality mining.” This project tackles questions such as how social networks evolve over time, how and why information flows in certain patterns, and how a group’s interactions can be changed in order to promote better functioning. His book being published by MIT Press, Reality Mining: Using Big Data to Engineer a Better World, highlights how the petabytes of data about human movements, transactions, and communication patterns that are continuously being generated by everyday technologies such as mobile phones and credit cards can be repurposed for good.

As a Fulbright Professor in 2006, Eagle launched MIT’s EPROM initiative (Entrepreneurial Programming and Research on Mobiles), developing a mobile phone programming curriculum that has been adopted by twelve Computer Science departments across Africa. To date, thousands of African computer science students have gone through the program, developing hundreds of innovative mobile phone applications designed specifically for African users, resulting in many start-ups based in Nairobi, Addis Ababa and beyond.

Eagle’s latest venture is txteagle Inc, an early stage company that is revolutionizing the world of market research by providing a mobile crowdsourcing service. txteagle enables mobile phone subscribers in the developing world to earn small amounts of airtime credit by completing simply tasks for companies. With partnerships with over 220 mobile operators, txteagle can make airtime payments to 2.1 billion people distributed across 80 countries.

Eagle graduated from Stanford University with a B.S. in Mechanical Engineering, an M.S. in Management Science and Engineering, and an M.S. in Electrical Engineering. His PhD from the MIT Media Laboratory on Reality Mining was declared one of the ‘10 technologies most likely to change the way we live’ by the MIT Technology Review magazine. Nokia recently named him one of the top mobile phone developers in the world. His research is regularly featured in the media including The Economist, The New York Times, The Wall Street Journal, BusinessWeek, and CNN.

In his presentations, Eagle overviews a variety of topics ranging from the opportunities for innovation within the developing world, to how these types of data can be used to uncover the structure in behaviour of both individuals and organizations, infer relationships, and study social network dynamics. By combining theoretical models with rich and systematic measurements, he shows it is possible to gain insight into the underlying behaviour of complex social systems. The ultimate goal of his work is to use “big data” for social good – to actively improve the lives of the billions of people who generate this data and the societies in which they live.

1. Reality Mining

Nathan Eagle has used mobile phones to continuously gather information including proximity, location, and communication from 100 human subjects at MIT. Systematic measurements from these people over the course of nine months have generated one of the largest dataset of continuous human behavior ever collected, representing over 300,000 hours of daily activity. Additionally, in collaboration with one of Europe's major telecommunication companies, Eagle is currently analyzing the call logs of an entire country - a dynamic social network consisting of 250 million nodes (people) and 12 billion temporal edges (calls).

Eagle's original "Reality Mining" study, declared one of the '10 technologies most likely to change the way we live' by the MIT Technology Review magazine, generated an unprecedented dataset of individual and group behavior. This data has been downloaded by thousands of researchers and used in over 75 publications. He is currently running similar ongoing studies involving subjects ranging from office workers in Helsinki, smokers in New York, teenagers in Delhi, and male prostitutes in East Africa.

In this talk, Eagle will describe how this type of data can be used to uncover the structure in behavior of both individuals and organizations, infer relationships, and study social network dynamics. By combining theoretical models with rich and systematic measurements, he shows it is possible to gain insight into the underlying behavior of complex social systems.

While results such as uncovering scaling laws from the communication patterns of hundreds of millions of people will certainly be one emphasis in this talk, of equal importance is how this data can enable applications that improve our society. Eagle will demonstrate a variety of ways these insights into our own behaviors can be used to develop applications that better support both the individual, organization, and society.

1. Crowd-Sourcing on Mobile Phones in the Developing World

Earlier this year, the number of mobile phone users exceeded 4 billion – with the majority of users living in the developing world. The implications of this fact are profound: most people on Earth are currently carrying computers that continually transmit information about their relationships, movements, and even financial decisions to closed databases distributed throughout the world. While the privacy implications of this data should not be understated, Nathan Eagle believes this ubiquitous infrastructure of wearable computers can be repurposed in ways that better serve both the billions of individuals who carry them and ultimately the societies in which they live.

Nathan Eagle moved to East Africa over three years ago, where he has been working with mobile phone operators and launching EPROM (Entrepreneurial Programming and Research on Mobiles) programs, an initiative he began in 2006 to teach mobile phone programming within local computer science departments in order to develop applications specifically for local users. Customized mobile phone applications could dramatically accelerate economic development in even the poorest communities in Africa, yet despite the incredible growth of mobile phone usage, these applications are rare.

Eagle has access to data about what we believe to be the largest social network ever analyzed – a network that may exceed one billion people by the end of this year. While most analyses of this type of data have focused on understanding Western societies, mobile phone subscribers from under-studied populations within the developing world are generating data that hold even greater potential. Together with Eric Horvitz, the current president of the Association for the Advancement of Artificial Intelligence (AAAI), they are attempting to build an AI sub-discipline: Artifical Intelligence for Development (AI-D). Working with epidemiologists, we are attempting to identify behavioral signatures associated with potential regional disease-outbreaks and concurrently modeling human movement in East Africa to support informed decisions about allocation of malaria eradication resources. Using data from every mobile phone in Rwanda over the last four years, they are working with the city planners of Kigali to understand the dynamics of slums and the impact of policy decisions ranging from road construction to the placement of latrines. With developmental economists, they are attempting to quantify a society's reactions to exogenous events, such as the collapse of crop prices in local markets or natural disasters, such as droughts and earthquakes.

As their sample size inevitably expands into the billions, it is important to consider the implications of planetary behavioral data and comparisons across cultures and continents. At first glance, it appears that some information spreads through a village in Kenya in a very similar way it spreads through Greater London. Mobility patterns in the Dominican Republic are comparable to how Rwandans move within their country, which bear striking parallels to movement in San Francisco. While we may be nearing the point of making claims about universal laws of human behavior, Eagle believes the harder
questions occur after we fit the distribution and declare an understanding of a complex social system: how can we use these data and insights to improve life?

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