Computers that power self-driving cars could be a huge driver of global carbon emissions
Study shows that if autonomous vehicles are widely adopted, hardware efficiency will need to advance rapidly to keep computing-related emissions in check.
Study shows that if autonomous vehicles are widely adopted, hardware efficiency will need to advance rapidly to keep computing-related emissions in check.
New fellows are working on health records, robot control, pandemic preparedness, brain injuries, and more.
AeroAstro major and accomplished tuba player Frederick Ajisafe relishes the community he has found in the MIT Wind Ensemble.
Stefanie Jegelka seeks to understand how machine-learning models behave, to help researchers build more robust models for applications in biology, computer vision, optimization, and more.
The role-playing game “On the Plane” simulates xenophobia to foster greater understanding and reflection via virtual experiences.
A new method can produce a hundredfold increase in light emissions from a type of electron-photon coupling, which is key to electron microscopes and other technologies.
Built on recent advances in machine learning, the model predicts how well individuals will produce and comprehend sentences.
MIT researchers are discovering which parts of the brain are engaged when a person evaluates a computer program.
But the harm from a discriminatory AI system can be minimized if the advice it delivers is properly framed, an MIT team has shown.
CAST Visiting Artist Andreas Refsgaard engages the MIT community in the ethics and play of creative coding.
This year's fellows will work across research areas including telemonitoring, human-computer interactions, operations research, AI-mediated socialization, and chemical transformations.
Researchers used a powerful deep-learning model to extract important data from electronic health records that could assist with personalized medicine.
Researchers at the Center for Theoretical Physics lead work on testing quantum gravity on a quantum processor.
Dan Huttenlocher is a professor of electrical engineering and computer science and the inaugural dean at MIT Schwarzman College of Computing.
New technique significantly reduces training and inference time on extensive datasets to keep pace with fast-moving data in finance, social networks, and fraud detection in cryptocurrency.