Big Data from Small Devices: Using Smartphones to Understand Human Behavior

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Today’s smartphones have evolved into incredibly rich sensing and computing devices, that oh-by-the-way can also make phone calls. They are jam-packed with on-board sensors for things like location, movement, temperature, atmospheric pressure, and more. Altogether we are talking about dozens of signals that can be combined and used to infer complex and interesting things about us, our environment, and our communities.

Sensing and automated data collection via mobile phones is becoming a key component in the digital revolution of the social sciences like psychology, sociology, and economics, among others. These techniques are also starting to make their way into consumer applications and services, but there are still many challenges (computational, efficiency, privacy/ethics). We spent the last several years at the MIT Media Lab working on developing these techniques and taking them from the lab into the real world. We believe that understanding human behavior and context is the next frontier, and we want to democratize this capability and make it ubiquitous.

In this talk I will give an overview of user-centric, continuous behavioral sensing with off-the-shelf mobile devices (phones). I will describe our work at MIT – the “Social fMRI” approach for conducting data-rich experiments with people living their everyday lives, the system we’ve built to implement it, and the “Friends and Family” living laboratory where a community of ~150 people lived under continuous data collection for over a year, in one of the largest mobile experiments done in academia to date. We collected over 1 million hours of research data, and with it we are able to see how friendships form and evolve, and how people influence each other, in our effort to understand what makes us human. There will be some examples of what we did and what we learned, as well as some of the big-data challenges and how we approached them. I will talk about our open source project and free “experiment in a box” web-service for non-programmers that allows anyone to do the same kind of data collection, from quantified-self trackers to a large scale research study.

Photo of Nadav Aharony

Nadav Aharony


Dr. Nadav Aharony is co-founder and CEO at Behavio. He completed his PhD at the MIT Media Lab’s Human Dynamics group, where he investigated the use of mobile phones as social and behavioral sensors, conducted one of the largest mobile data experiments done in academia, and initiated the open source mobile sensing platform that became Nadav was also a Fellow at the MIT Center for Civic Media for 3 years, since its inception, where he worked on topics of mobile and social activism.

Most recently Nadav worked at Google, as a product manager in the Android team. He has over 10 years of industry experience in engineering, product management, and business development roles, in organizations ranging from startups to corporate environments. He holds a PhD and MS degrees from the MIT Media Lab, and a bachelor’s degree in electrical engineering cum-laude from the Technion – Israel Institute of Technology. Nadav holds multiple patents in areas of social mobile networking, machine learning, network algorithms, and sensor technologies. His work has been featured in both academic and popular press (Technology Review,, Wall Street Journal, Wired UK, and The Associated Press, among others), and received awards of recognition (including Best and Distinguished Paper awards, Knight News Challenge, SXSW Accelerator, IPSN Extreme Sensing Competition, and three Google Research Awards).


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