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Networks and graphs have become essential for understanding the online world. I will discuss how to use mobile location and communication data to build similar networks in the real world offline. We can track movement trends in real time in cities, learn networks of real places, and learn real social networks by gathering long-term high-frequency location data from millions of mobile devices. For example, we can visualize the network of places in a city by showing the similarity between different locations and their activity level in real time. Another graph is the network of users that shows how similar person X is to person Y by comparing their movement histories and how often they colocated. These networks reveal interesting trends in behavior, and they organize people into tribes that are more detailed than traditional demographic groups. With learning algorithms applied to these human activity networks, we can make predictions for advertising, marketing, and social analysis. More importantly, we can build such networks from data without compromising individual privacy, since they only require statistics on user data rather than the original raw bits.
Tony Jebara is associate professor of computer science at Columbia University as well as chief scientist and co-founder at Sense Networks. His research intersects computer science and statistics to develop algorithms that learn from spatio-temporal data, networks, images and text. He has published over 50 scientific articles and is the author of the book Machine Learning: Discriminative and Generative (Springer). Jebara is the recipient of the Career award from the National Science Foundation and has also received awards for his papers from the International Conference on Machine Learning and from the Pattern Recognition Society. Jebara’s work has been featured on TV (ABC, BBC, New York One, TechTV) as well as in the popular press (New York Times, Slash Dot, Wired, Scientific American, Newsweek). He obtained his PhD in 2002 from MIT.