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.
Guy Bresler builds mathematical models to understand multifaceted, interdisciplinary engineering problems that have far-reaching applications.
Mohammad Javad Khojasteh, a postdoc at MIT LIDS, uses both classical and quantum physics to improve state-of-the-art capabilities in communication, sensing, and computation.
Professor of electrical engineering and computer science will receive additional support to advance his research and career.
Researchers develop tools to help data scientists make the features used in machine-learning models more understandable for end users.
Graduate student Sarah Cen explores the interplay between humans and artificial intelligence systems, to help build accountability and trust.
Researchers use artificial intelligence to help autonomous vehicles avoid idling at red lights.
For individuals who communicate using a single switch, a new interface learns how they make selections, and then self-adjusts accordingly.
Researchers design a user-friendly interface that helps nonexperts make forecasts using data collected over time.
Veteran and PhD student Andrea Henshall has used MIT Open Learning to soar from the Air Force to multiple aeronautics degrees.
The computer-vision technique behind these maps could help avoid contrail production, reducing aviation’s climate impact.
MIT Energy Initiative edX course asks students to rethink how we operate power systems.
A new model shows that the more polarized and hyperconnected a social network is, the more likely misinformation will spread.
Strategy accelerates the best algorithmic solvers for large sets of cities.
A new method forces a machine learning model to focus on more data when learning a task, which leads to more reliable predictions.