Advance from SMART will help to better identify disease markers and develop targeted therapies and personalized treatment for diseases such as cancer and antibiotic-resistant infection.
A system conceived in Professor Michael Cima’s lab was approved by the Food and Drug Administration after positive results in patients.
The FabObscura system helps users design and print barrier-grid animations without electronics, and can help produce dynamic household, workplace, and artistic objects.
The research center, sponsored by the DOE’s National Nuclear Security Administration, will advance the simulation of extreme environments, such as those in hypersonic flight and atmospheric reentry.
The findings may redefine how cell identity is established and enable the creation of more sophisticated engineered tissues.
Study of 3.5 million cells from more than 100 human brains finds Alzheimer’s progression — and resilience to disease — depends on preserving epigenomic stability.
Popular mechanical engineering course applies machine learning and AI theory to real-world engineering design.
Balancing automation and agency, Associate Professor Arvind Satyanarayan develops interactive data visualizations that amplify human creativity and cognition.
Succeeding founding executive director Renee Robins, Giardina will help shape and implement the goals and initiatives of MIT’s eminent water and food program.
MIT CSAIL researchers developed SustainaPrint, a system that reinforces only the weakest zones of eco-friendly 3D prints, achieving strong results with less plastic.
System developed at MIT could provide realistic predictions for a wide variety of reactions, while maintaining real-world physical constraints.
Artificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.
New findings could help manufacturers design gels, lotions, or even paving materials that last longer and perform more predictably.
Professor Caroline Uhler discusses her work at the Schmidt Center, thorny problems in math, and the ongoing quest to understand some of the most complex interactions in biology.
VaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.