The project is a multi-faculty, cross-MIT effort, with input and expertise from multiple institutes.
Thursday August 20, 2020 ...
These sessions will focus on tools for privacy-aware contact tracing.
Recognizing individuals who demonstrate a real commitment to building a diverse and inclusive tech community and making a Better World
These awards recognize outstanding achievement and innovation in the field.
Wednesday June 3, 2020 ...
These are public talks via webcast every Wednesday at 10am. Safe Paths—an MIT-led, free, open source technology that enabl...
Wednesday May 27, 2020 ...
This award is given out annually by the MIT Graduate Student Council and presented at the Awards Convocation ceremony.
Wednesday May 6, 2020 ...
Ramesh Raskar gives an update on his nonprofit’s progression in developing the Safe Paths mobile app and Safe Places web tool.
Ramesh Raskar details these tools and discusses the advantages and disadvantages of different contact tracing methods.
Your phone soon might know if you have spent time near someone with the COVID-19 virus.
Friday April 17, 2020 ...
Researchers are racing to achieve the benefits of location-tracking without the surveillance.
To help explain how this works is Ramesh Raskar, an associate professor at MIT’s Media Lab.
The app is designed to let people discover if they've crossed paths with someone who's been infected with COVID-19.
A multinational team develops new tools to slow the spread of pandemics.
A system that enables smartphones to transmit “chirps” to nearby devices could notify people if they have been near an infected person.
Saturday April 4, 2020 ...
Private Kit: Safe Paths shares information about your movements in a privacy-preserving way—and could let health officials tackle COVID-19.
China, South Korea used smartphone apps to monitor people with the disease. Americans have different views of privacy and data collection.
The MIT Media Lab, a hallowed digital playground and research center into media technologies, is celebrating its 25th anniversary. We'll ...
"The research is…unique in its approach to enable seeing through dense fog for self-driving cars and augmented driving."
On May 11, 2019 Ramesh Raskar was the doctoral hooding ceremony keynote speaker at UNC Chapel Hill Commencement. He shared the ...
Kumbhathon is a year-round initiative to identify and address the challenges of cities in developing countries. It is not just a hackatho...
MIT Media Lab professor Ramesh Raskar talks about how his high-tech inventions and initiatives can help solve real-world problems.
Technique can capture a scene at multiple depths with one shutter click—no zoom lens needed
Developed by MIT researchers, the technology could be a boon for drivers and driverless cars
MIT researchers have developed a system that can produce images of objects shrouded by fog so thick that human vision can’t penetrate it.
Computational method improves the resolution of time-of-flight depth sensors 1,000-fold.
New 3D camera research from the Media Lab’s Camera Culture group was selected for the Best Papers Special Issue of the ICCV.
View past newsletters to get updates on group research, awards, and initiatives:
Technique could lead to cameras that can handle light of any intensity, audio that doesn’t skip or pop.
Machine learning is helping urbanists confirm–or disprove–long-standing theories about cities.
Facebook volunteers and work-at-home moms might be making city planning decisions, thanks to AI research conducted by MIT scientists. Res...
Using computer vision to examine Google Street View, the researchers analyzed how streets and blocks have changed in five American cities.
Tested with five American cities, Streetchange quantifies the physical improvement or deterioration of neighborhoods.
A recently published paper in the Proceedings of the National Academy of Sciences (PNAS) looks at factors that predict neighborhood change.
When a group of forensic dentistry experts set out in 2016 to investigate what may have caused some peculiar holes in a T. rex jaw, they ...
Researchers have used machine learning to quantify the physical improvement or deterioration of neighborhoods in five American cities.