• Veriplace
  • AT&T Interactive
  • DigitalGlobe
  • Google
  • Yahoo! Inc.
  • ZoomAtlas
  • Digital Map Products
  • Microsoft Research (MSR)
  • Pitney Bowes Business Insight

Sponsorship Opportunities

For information on exhibition and sponsorship opportunities at the conference, contact Yvonne Romaine at yromaine@oreilly.com

Media Partner Opportunities

For media partnerships, contact mediapartners@ oreilly.com or download the Media & Promotional Partner Brochure (PDF)

Press and Media

For media-related inquiries, contact Maureen Jennings at maureen@oreilly.com

Where 2.0 Newsletter

To stay abreast of conference news and to receive email notification when registration opens, please sign up for the Where 2.0 Conference newsletter (login required)

Where 2.0 Ideas

Have an idea for Where to share? where-idea@oreilly.com

Contact Us

View a complete list of Where 2.0 contacts

Learning Networks of People and Places from Location and Communication Data

Location: Ballroom IV Level: Novice
Average rating: ****.
(4.00, 1 rating)

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.

Photo of Tony Jebara

Tony Jebara

Columbia University | Netflix

Tony Jebara is director of machine learning at Netflix and professor on leave from Columbia University. He has published over 100 peer-reviewed papers in leading conferences and journals across machine learning, computer vision, social networks, and recommendation and is the author of the book Machine Learning: Discriminative and Generative. His work has been recognized with best paper awards from the International Conference on Machine Learning and from the Pattern Recognition Society, the Career award from the National Science Foundation, and faculty awards from Google, Yahoo, and IBM. He has cofounded and advised multiple startup companies in the domain of artificial intelligence and served as general chair and program chair for the International Conference on Machine Learning. He holds a PhD from MIT.