For the first time, researchers use a combination of MEG and fMRI to map the spatio-temporal human brain dynamics of a visual image being recognized.

By providing plausible label maps for one medical image, the Tyche machine-learning model could help clinicians and researchers capture crucial information.

FeatUp, developed by MIT CSAIL researchers, boosts the resolution of any deep network or visual foundation for computer vision systems.

The sticky, wearable sensor could help identify early signs of acute liver failure.

The neuroscientist is recognized for her ongoing work to understand molecular and cellular mechanisms that enable the brain to adapt to experience.

Using fluorescent labels that switch on and off, MIT engineers can study how molecules in a cell interact to control the cell’s behavior.

The wearable device, designed to monitor bladder and kidney health, could be adapted for earlier diagnosis of cancers deep within the body.

MIT CSAIL researchers combine AI and electron microscopy to expedite detailed brain network mapping, aiming to enhance connectomics research and clinical pathology.

Thirteen new graduate student fellows will pursue exciting new paths of knowledge and discovery.

In a first, researchers have observed how lithium ions flow through a battery interface, which could help engineers optimize the material’s design.