Fast-tracking the search for energy-efficient materials
Doctoral candidate Nina Andrejević combines spectroscopy and machine learning techniques to identify novel and valuable properties in matter.
Doctoral candidate Nina Andrejević combines spectroscopy and machine learning techniques to identify novel and valuable properties in matter.
Fellowship honors ACM members whose accomplishments drive innovation and make broader advances possible.
An MIT team develops 3D-printed tags to classify and store data on physical objects.
MIT neuroscientists have developed a computer model that can answer that question as well as the human brain.
Overseeing business and research units across MIT Open Learning, Breazeal will focus on the future of digital technologies and their applications in education.
Twist is an MIT-developed programming language that can describe and verify which pieces of data are entangled to prevent bugs in a quantum program.
MIT computer scientists and mathematicians offer an introductory computing and career-readiness program for incarcerated women in New England.
MIT scientists discuss the future of AI with applications across many sectors, as a tool that can be both beneficial and harmful.
In 2.C01, George Barbastathis demonstrates how mechanical engineers can use their knowledge of physical systems to keep algorithms in check and develop more accurate predictions.
David Gamarnik has developed a new tool, the overlap gap property, for understanding computational problems that appear intractable.
MIT community members made headlines around the world for their innovative approaches to addressing problems local and global.
Assistant professor of civil engineering describes her career in robotics as well as challenges and promises of human-robot interactions.
The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.
New technique applied to small computer chips enables efficient vision and detection algorithms without internet connectivity.
MIT-IBM Watson AI Lab researchers aim to design concrete mixtures that use AI to shrink environmental footprint and cost, while recycling byproducts and increasing performance.