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The work in the Social Computing Group integrates theories and models from ubiquitous computing, social media, machine learning, and social sciences, to sense, analyze, and interpret human and social behavior in everyday life, and to create devices and systems that support interaction and communication. Current research includes methods for urban computing from phone and mobile social network data, behavioral analysis of social video, ubiquitous sensing of face-to-face interaction, and crowdsourcing.
Current Group Members
Social Computing News
Daniel Gatica-Perez (EPFL Adjunct Professor)