Managing traffic in space
Associate Professor Richard Linares is helping satellites safely navigate in increasingly congested orbits.
Associate Professor Richard Linares is helping satellites safely navigate in increasingly congested orbits.
Founded by Tristan Bepler PhD ’20 and former MIT professor Tim Lu PhD ’07, OpenProtein.AI offers researchers open-source models and other tools for protein engineering.
New MIT work advances the growing field of ionotronics, in which data are transferred through ions, potentially providing a bridge between electronics and biological tissue.
A new study suggests that the chemical NDMA is much more likely to cause cancerous mutations after exposure early in life.
MIT Energy Initiative symposium maps a path to tap the planet’s heat-rich rocks for clean power at scale.
Two faculty and six additional alumni win top APS awards and prizes; four faculty and 12 additional alumni named APS Fellows.
The devices represent a key step toward practical quantum sensing, with applications in biomedical sensing, materials characterization, and more.
MIT senior, master's candidate, and airman Brian Robinson lives and works at the intersection of aviation, politics, and technology.
Researchers are developing hardware and algorithms to improve collaboration between divers and autonomous underwater vehicles engaged in maritime missions.
As the School of Humanities, Arts, and Social Sciences marks 75 years, Dean Agustín Rayo reflects on how AI is reshaping higher education and why SHASS disciplines continue to be central to MIT’s mission.
MIT students see the Earth's curvature in reborn AeroAstro intro course.
A chemical-free approach to balancing ocean acidity protects marine life and could dramatically impact the global aquaculture market.
The influential first leader of the Computation Structures Group at MIT played a key role in the development of asynchronous computing.
PhD student Carissma McGee studies exoplanets and examines intellectual property frameworks for space collaborations.
Researchers use control theory to shed unnecessary complexity from AI models during training, cutting compute costs without sacrificing performance.