Research — MIT Media Lab

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

health
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human-machine interaction
learning
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design
technology
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wearable computing
networks
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history
archives
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sensors
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covid19
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developing countries
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ethics
civic technology
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synthetic biology
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affective computing
virtual reality
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transportation
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social change
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zero gravity
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3d printing
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manufacturing
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prosthetic design
women
behavioral science
construction
fashion
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social networks
banking and finance
open source
systems
cryptocurrency
security
crowdsourcing
climate change
diversity
collective intelligence
fabrication
makers
wiesner
language learning
cognitive science
internet of things
bionics
extended intelligence
performance
neural interfacing and control
perception
human augmentation
startup
interactive
autonomous vehicles
natural language processing
ecology
civic action
marginalized communities
nonverbal behavior
mapping
physiology
visualization
clinical science
physics
holography
long-term interaction
gesture interface
microfabrication
mechanical engineering
networking
trust
point of care
orthotic design
water
sports and fitness
autism research
primary healthcare
electrical engineering
voice
microbiology
hacking
member company
pharmaceuticals
rfid
nanoscience
clinical trials
mechatronics
trade
academia
member event
open access
gender studies
soft-tissue biomechanics
code
chemistry
real estate
randomized experiment
publishing
biomedical imaging
gis
event
exhibit
cartography
metamaterials
installation

Pandemic Response Catalyst Conversations

The MIT Center for Collective Intelligence, MIT Media Lab’s Community Biotechnology Initiative, and MilliporeSigma—the lif...

Post Research

Pandemic Response Catalyst Conversations

Part of a collaboration between the MIT Center for Collective Intelligence, the Lab’s Community Biotechnology Initiative, and MilliporeSigma

Research

Community Biotechnology

Article Research

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.

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.