SuperUROP scholars apply deep learning to improve accuracy of climate models, profitably match computers in the cloud with customers, and more.
Her research focuses on more-efficient deep neural networks to process video, and more-efficient hardware to run applications.
The advance could accelerate engineers’ design process by eliminating the need to solve complex equations.
MIT alumni and friends from around the globe attended an online event that featured presentations from Institute leaders, faculty, and alumni about human health-related research.
Researchers propose a method for finding and fixing weaknesses in automated programming tools.
Cardea software system aims to bring the power of prediction to hospitals by streamlining complex machine learning processes.
Using deep convolutional neural networks, researchers devise a system that quickly analyzes wide-field images of patients’ skin in order to more efficiently detect cancer.
MIT research team finds machine learning techniques offer big advantages over standard experimental and theoretical approaches.
A new approach to identifying useful formulations could help solve the degradation issue for these promising new lightweight photovoltaics.
Regina Barzilay, Fotini Christia, and Collin Stultz describe how artificial intelligence and machine learning can support fairness, personalization, and inclusiveness in health care.