Our mission at SenseNetworks is to index the real world using location data. By harnessing this rich, natural, and anonymized data, unprecedented possibilities emerge for user modeling, marketing, advertising, recommendation, search, and collaborative filtering.
Using machine learning algorithms, we can infer the context of a place and the tribe of a user from just their location data. It turns out that the flow and movement of people through the city (who is where and at what time) defines places and their character. Similarly, a person’s movement trail through the city reveals their personality and tribe. With location data, we build a network of places (how similar is place A to place B) and a network of people (how similar is person X to person Y). These networks let us cluster places and people as well as compute next-generation demographics and analytics. As your cell phone learns about you, it helps you find people, places, and things you are interested in and your phone’s mapping software becomes your personal social navigator.
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.
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