Gene Dresselhaus, influential research scientist in solid-state physics, dies at 91
Over 50 years at MIT, Dresselhaus made lasting contributions to materials science within the research group of longtime collaborator and wife, Mildred Dresselhaus.
Over 50 years at MIT, Dresselhaus made lasting contributions to materials science within the research group of longtime collaborator and wife, Mildred Dresselhaus.
A new machine-learning system helps robots understand and perform certain social interactions.
MISTI Career Conversations virtual lunch series sees MIT students explore environmental, social, and governance initiatives in a global context across three key sectors.
Reducing the complexity of a powerful machine-learning model may help level the playing field for automatic speech-recognition around the world.
Students featured in public art exhibits in prominent locations throughout Boston.
The Common Ground for Computing Education is facilitating collaborations to develop new classes for students to pursue computational knowledge within the context of their fields of interest.
A new method forces a machine learning model to focus on more data when learning a task, which leads to more reliable predictions.
A National Science Foundation-funded team will use artificial intelligence to speed up discoveries in physics, astronomy, and neuroscience.
Now in its 19th year, the WTP brings high school students with little STEM experience to Cambridge for an immersive, four-week exploration of all things engineering.
A visual analytics tool helps child welfare specialists understand machine learning predictions that can assist them in screening cases.
Honor recognizes professors who went the extra mile advising during the pandemic’s disruptions.
Artificial intelligence is top-of-mind as Governor Baker, President Reif encourage students to “see yourself in STEM.”
A new control system, demonstrated using MIT’s robotic mini cheetah, enables four-legged robots to jump across uneven terrain in real-time.
When asked to classify odors, artificial neural networks adopt a structure that closely resembles that of the brain’s olfactory circuitry.
A new machine-learning system costs less, generates less waste, and can be more innovative than manual discovery methods.