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.
MIT researchers develop a new way to control and measure energy levels in a diamond crystal; could improve qubits in quantum computers.
Neuroscientists find the internal workings of next-word prediction models resemble those of language-processing centers in the brain.
PhD candidate Charlene Xia is developing a low-cost system to monitor the microbiome of seaweed farms and identify diseases before they spread.
Artificial intelligence is top-of-mind as Governor Baker, President Reif encourage students to “see yourself in STEM.”
A certain type of artificial intelligence agent can learn the cause-and-effect basis of a navigation task during training.
The K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center will use mathematical tools to transform data into a deep understanding of the brain.
MIT EECS unveils a new effort to encourage and support women on their journey to — and through — graduate study in computing and information technologies.
MIT Refugee Action Hub celebrates the graduation of its third and largest cohort yet.
A cyber systems expert at Lincoln Laboratory, Okhravi will help investigate bold solutions to fundamental cyber vulnerabilities.
MIT App Inventor’s “Appathon” joins programmers from around the world to imagine a better future and start building it one app at a time.
Preparing for a career advancing the science and policy of climate issues, junior Ryan Conti focuses on math, computer science, and the philosophy of language.
Scientists employ an underused resource — radiology reports that accompany medical images — to improve the interpretive abilities of machine learning algorithms.