Startups led by MIT mechanical engineers offer health care solutions
Companies founded by MechE faculty and alumni solve a variety of health care challenges, from better drug delivery to robotic surgery.
Companies founded by MechE faculty and alumni solve a variety of health care challenges, from better drug delivery to robotic surgery.
The cap will help researchers gain new insight into how the brain functions.
A pandemic-fueled transformation of the MIT course MAS.S64 (How to Grow (Almost) Anything) leads to next steps in democratizing synthetic biology.
The new design works with the diaphragm to improve breathing.
The Kendall Square Association’s annual meeting highlights local efforts to develop and shift toward clean energy solutions.
With NEET, Sherry Nyeo is discovering MIT’s undergraduate research community at the intersection of computer science and biological engineering.
Test that measures a person’s antibodies requires a drop of blood and takes just 10 minutes to show results.
A machine-learning method finds patterns of health decline in ALS, informing future clinical trial designs and mechanism discovery. The technique also extends to Alzheimer’s and Parkinson’s.
MIT researchers demonstrate an intracellular antenna that's compatible with 3D biological systems and can operate wirelessly inside a living cell.
By providing researchers with financial and strategic support from the early stages, the Innovation Center hopes to bring new and disruptive technologies to market.
An MIT-developed device with the appearance of a Wi-Fi router uses a neural network to discern the presence and severity of one of the fastest-growing neurological diseases in the world.
The device senses and wirelessly transmits signals related to pulse, sweat, and ultraviolet exposure, without bulky chips or batteries.
Mathematical modeling speeds up the process of programming bacterial systems to self-assemble into desired 2D shapes.
A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.
An anomaly-detection model developed by SMART utilizes machine learning to quickly detect microbial contamination.