Making higher education more accessible to students in Pakistan
EduFi, founded by an MIT alumna, provides low-interest student loans to families in Pakistan so more can attend college.
EduFi, founded by an MIT alumna, provides low-interest student loans to families in Pakistan so more can attend college.
A new international collaboration unites MIT and maritime industry leaders to develop nuclear propulsion technologies, alternative fuels, data-powered strategies for operation, and more.
Researchers fuse the best of two popular methods to create an image generator that uses less energy and can run locally on a laptop or smartphone.
Stuart Levine ’97, director of MIT’s BioMicro Center, keeps departmental researchers at the forefront of systems biology.
As artificial intelligence develops, we must ask vital questions about ourselves and our society, Ben Vinson III contends in the 2025 Compton Lecture.
U.S. Air Force engineer and PhD student Randall Pietersen is using AI and next-generation imaging technology to detect pavement damage and unexploded munitions.
New research could allow a person to correct a robot’s actions in real-time, using the kind of feedback they’d give another human.
Felice Frankel discusses the implications of generative AI when communicating science visually.
Materials scientist is honored for his academic leadership and innovative research that bridge engineering and nature.
Agreement between MIT Microsystems Technology Laboratories and GlobalFoundries aims to deliver power efficiencies for data centers and ultra-low power consumption for intelligent devices at the edge.
The programmable proteins are compact, modular, and can be directed to modify DNA in human cells.
FragFold, developed by MIT Biology researchers, is a computational method with potential for impact on biological research and therapeutic applications.
A new study shows LLMs represent different data types based on their underlying meaning and reason about data in their dominant language.
ReviveMed uses AI to gather large-scale data on metabolites — molecules like lipids, cholesterol, and sugar — to match patients with therapeutics.
Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.