Prof. Daniel Gatica-Perez

Adjunct Professor

Daniel Gatica-Perez directs the Social Computing Group at Idiap and is Adjunct Professor at EPFL School of Engineering. He is also affiliated with the College of Humanities (courtesy appointment). He has worked on human-centered computing for the last fifteen years, integrating research in ubiquitous computing, social media, machine learning and the social sciences.

His current interests include the use of mobile and social technologies for social good. His work has been supported by the Swiss National Science Foundation, the Swiss Commission for Technology and Innovation, the European Commission, large companies, and tech start-ups. He also works with cities and local organizations in social innovation projects. His work has received three Best Paper Awards at international conferences.

He is often an invited or keynote speaker at international events. He currently serves as Associate Editor of the new PACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT). He has served as Associate Editor of the IEEE Transactions on Multimedia; General Co-Chair of the ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing (Ubicomp 2015); and Program Co-Chair of the Int. Symposium on Wearable Computers (ISWC 2013), and the ACM Int. Conf. on Multimedia (ACM MM 2013). He has also served as expert reviewer for the European Commission and for the national research agencies of France, Netherlands, Finland, Japan, and Australia.


Research Area

Current research areas include:
  1. Mobile crowdsensing and social media analytics. We study human behavior in the physical world and online through large-scale analysis of phone sensor data and social media. Our work designs social participation mechanisms; characterizes how people interact with physical and online environments; and automates recognition tasks to generate insights on urban phenomena (e.g. collective perception of Airbnb places) and health (e.g. everyday eating practices on Instagram).
  2. Multi-sensor analysis of ubiquitous interactions: We study interactions in the workplace like job interviews and hospitality encounters using cameras and wearables; and develop machine learning methods that automatically extract subtle nonverbal behavior from people's voice and body and learn to infer perceived human attributes like soft skills.


ELD 230 (Bâtiment ELD)
Station 14
CH-1015 Lausanne