The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.

A collaborative research team from the MIT-Takeda Program combined physics and machine learning to characterize rough particle surfaces in pharmaceutical pills and powders.

A new method could provide detailed information about internal structures, voids, and cracks, based solely on data about exterior conditions.

New LiGO technique accelerates training of large machine-learning models, reducing the monetary and environmental cost of developing AI applications.

J-WAFS researchers are using remote sensing observations to build high-resolution systems to monitor drought.

Computational chemists design better ways of discovering and designing materials for energy applications.

Researchers use machine learning to build faster and more efficient hash functions, which are a key component of databases.

Aided by machine learning, scientists are working to develop a vaccine that would be effective against all SARS-Cov-2 strains.

MIT researchers uncover the structural properties and dynamics of deep classifiers, offering novel explanations for optimization, generalization, and approximation in deep networks.

MIT researchers trained logic-aware language models to reduce harmful stereotypes like gender and racial biases.