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SUMMARY:Learning Networks of People and Places from Location Data  
DTSTART:20090626T101500
DTSTAMP:20260924T173804Z
UID:8ee59306614df8d8ab7b32de76a8e07ada6af7a39b51fb83a5d1468d
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Tony Jebara\, Columbia University\, New York\nNetworks a
 nd graphs have become essential for understanding the online world with ap
 plications ranging from the Web to FaceBook. I will discuss building such 
 networks in the offline real world by using location and GPS data. By gath
 ering long-term high frequency location data from millions of mobile devic
 es it becomes possible to track movement trends in real-time in cities\, l
 earn networks of real places and learn real social networks of people. We 
 build graphs from this data using generalized matching algorithms and also
  apply novel visualization\, clustering and classification tools to them. 
 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 colocate
 d. Embedding and clustering these graphs reveals interesting trends in beh
 avior and organizes people into tribes that are more detailed than traditi
 onal demographics. With learning algorithms applied to these human activit
 y graphs\, it becomes possible to make predictions for advertising\, marke
 ting and collaborative recommendation from real offline behavior. \nProf J
 ebara's homepage
LOCATION:BC 01 https://plan.epfl.ch/?room==BC%2001
STATUS:CONFIRMED
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