Research — MIT Media Lab

We are an interdisciplinary research lab working to invent the future of

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construction
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fabrication
makers
wiesner
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performance
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neural interfacing and control
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human augmentation
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Comprehensive report on pandemic response solutions developed by 180 leading experts

Online platform harnesses collective intelligence to accelerate recovery from Covid-19 and prepare for future disease outbreaks.

Article Research

FIGUR8 and Toppan Create Service for Upper-Body Misalignment Visualization

Spinoff Figur8 and member company Toppan Printing have developed a new service using musculoskeletal sensors to assess body misalignment.

Research

Community Biotechnology

Article Research

Innovative face masks and medical-grade gowns to combat Covid-19 and future pandemics

Three most promising PPE solutions selected by expert judges on MIT Pandemic Response CoLab.

Research

Camera Culture

Making the invisible visible–inside our bodies, around us, and beyond–for health, work, and connection

Project Research

Pandemic Response CoLab

Pandemic Response CoLab will help individuals and groups work together to solve practical problems created by the COVID-19 pandemic. By l...

Project Research

Pandemic Supermind Activation

The Pandemic Supermind Activation brings together leaders in the biosciences and beyond, harnessing the power of ...

Article Research

A history of LEGO Education, Part 3: Mindstorms over matter

The Brothers Brick looks back at the history of LEGO Education's collaboration with the Media Lab to launch LEGO Mindstorms in 1998.

Article Research

E14 Fund helps Media Lab community deploy startups

“First and foremost we’re a community-builder,” says Habib Haddad, a managing partner of the venture fund.

Archived

Project Research

Zensei: Embedded, Multi-Electrode Bioimpedance Sensing for Implicit, Ubiquitous User Recognition

We introduce Zensei, an implicit sensing system that leverages bio-sensing, signal processing, and machine learning to classify uninstrum...

Archived

Publication Research

Zensei: Embedded, Multi-electrode Bioimpedance Sensing for Implicit, Ubiquitous User Recognition

Munehiko Sato, Rohan S. Puri, Alex Olwal, Yosuke Ushigome, Lukas Franciszkiewicz, Deepak Chandra, Ivan Poupyrev, and Ramesh Raskar. 2017. Zensei: Embedded, Multi-electrode Bioimpedance Sensing for Implicit, Ubiquitous User Recognition. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). ACM, New York, NY, USA, 3972-3985.