Learning Networks of Places and People from Location Data
Speaker: Tony Jebara, Columbia University
Location: Warren Weaver Hall 1302
Date: January 30, 2009, 11:30 a.m.
Host: Chris Bregler
Networks and graphs have become essential for understanding large scale systems and data. Systems like the world wide web can be described as a large graph connecting virtual places. Social networks like Facebook are graphs connecting people (or at least their online personas). I discuss building and using such graphs not in the online world but rather in the real world by collecting location and GPS data. Sense Networks gathers long-term high frequency location data from over 100,000 devices making it possible to learn networks of real places and real social networks of people. We build graphs from data using kernels and generalized matching algorithms. With these graphs, many learning tasks are straightforward including visualization, clustering and classification. 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 trails or histories. Embedding and clustering these graphs reveals interesting trends in behavior and tribes of people that are more detailed than traditional demographics. With learning algorithms applied to these human activity graphs, it becomes possible to make predictions for advertising, marketing and collaborative recommendation from offline data.
Tony Jebara is Associate Professor of Computer Science at Columbia University and director of the Columbia Machine Learning Laboratory. His 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 is also co-founder of Sense Networks. He has published over 50 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. Jebara is the recipient of the Career award from the National Science Foundation and has also received honors for his papers from the International Conference on Machine Learning and from the Pattern Recognition Society. 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, Scientific American, Newsweek, etc.). He obtained his PhD in 2002 from MIT. Recently, Esquire magazine named him one of their Best and Brightest of 2008. Jebara's lab is supported in part by the NSF, CIA, NSA and ONR.
Refreshments will be offered starting 15 minutes prior to the scheduled start of the talk.