Networks and graphs have become essential for understanding the online world with applications ranging from the Web to FaceBook. I will discuss how Sense is building such networks in the offline real world by using mobile location and communication data. By gathering long-term high frequency location data from millions of mobile devices it becomes possible to track movement trends in real-time in cities, learn networks of real places and learn real social networks of people. For example, we can visualize the network of places in a city showing the similarity between different locations and how active they are right now. Another graph is the network of users showing 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 organizes people into "Lifestyle" tribes that are more relevant than traditional demographics.
Tony Jebara is Associate Professor of Computer Science at Columbia University and co-founder of Sense Networks. He directs the Columbia Machine Learning Laboratory whose research intersects computer science and statistics to develop new frameworks for learning from data with applications in vision, networks, spatio-temporal data, and text. Jebara has published over 75 peer-reviewed papers in conferences and journals including NIPS, ICML, UAI, COLT, JMLR, CVPR, ICCV, and AISTAT. He is the author of the book Machine Learning: Discriminative and Generative and co-inventor on multiple patents in vision, learning and spatio-temporal modeling. In 2004, Jebara was the recipient of the Career award from the National Science Foundation. His work was recognized with a best paper award at the 26th International Conference on Machine Learning, a best student paper award at the 20th International Conference on Machine Learning as well as an outstanding contribution award from the Pattern Recognition Society in 2001. Jebara's research has been featured on television (ABC, BBC, New York One, TechTV, etc.) as well as in the popular press (New York Times, Slash Dot, Wired, Businessweek, IEEE Spectrum, etc.). He obtained his PhD in 2002 from MIT. Recently, Esquire magazine named him one of their Best and Brightest of 2008.
Tony is Associate Editor for the Journal of Machine Learning Research, Associate Editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence, and Associate Editor for the journal Machine Learning. He is currently serving as area chair for Neural Information Processing Systems, as member of the senior program committee of Uncertainty in Artificial Intelligence, and as a member of the steering committee of the NYAS Machine Learning Symposium.