Six MIT researchers elected to the National Academy of Engineering for 2019
Members have made advances in molecular processes, rheology, computer networking, nanocrystalline metals, affective computing, and semiconductor tech.
Members have made advances in molecular processes, rheology, computer networking, nanocrystalline metals, affective computing, and semiconductor tech.
Casey Evans, Stewart Isaacs, and Jessica Zhu are honored for outstanding academic performance, civic contributions, and research.
Undergraduate researchers discussed their projects at a well-attended poster session.
NASA’s OSIRIS-REx sample-return spacecraft, carrying MIT instrument, arrived at asteroid in December; now begins the science to select a sampling location.
William Oliver says a lack of available quantum scientists and engineers may be an inhibitor of the technology’s growth.
First measurement of its kind could provide stepping stone to practical quantum computing.
Lincoln Laboratory's lidar data, processed quickly with support from the organization MCNC, helped FEMA assess flooding and damages caused by Hurricane Florence.
Technologies ranging from a hurricane-evacuation decision platform to algorithms that compare DNA samples honored as some of the world's best inventions of 2018.
Long-time EECS professor and Lincoln Laboratory division head is best known for research on transistors, lasers, and masers.
Microhydraulic actuators, thinner than one-third the width of human hair, are proving to be the most powerful and efficient motors at the microscale.
Study finds shoebox-sized CubeSats gather weather data comparably to data collected by larger satellites.
Lincoln Laboratory's 3-D printing lead has been named to Manufacturing Engineering magazine's 30 Under 30 list.
Innovation in Societal Infrastructure Award recognizes Lincoln Laboratory researchers who created a system that gives first responders the big picture.
Model from MIT Lincoln Laboratory Intelligence and Decision Technologies Group sets a new standard for understanding how a neural network makes decisions.
Adaptable Interpretable Machine Learning project is redesigning machine learning models so humans can understand what computers are thinking.