Making climate models relevant for local decision-makers
A new downscaling method leverages machine learning to speed up climate model simulations at finer resolutions, making them usable on local levels.
A new downscaling method leverages machine learning to speed up climate model simulations at finer resolutions, making them usable on local levels.
In the new economics course 14.163 (Algorithms and Behavioral Science), students investigate the deployment of machine-learning tools and their potential to understand people, reduce bias, and improve society.
With generative AI models, researchers combined robotics data from different sources to help robots learn better.
A new approach could streamline virtual training processes or aid clinicians in reviewing diagnostic videos.
Fifteen new faculty members join six of the school’s academic departments.
Graduate student Nolen Scruggs works with a local tenant association to address housing inequality as part of the MIT Initiative on Combatting Systemic Racism.
Ashutosh Kumar, a materials science and engineering PhD student and MathWorks Fellow, applies his eclectic skills to studying the relationship between bacteria and cancer.
TorNet, a public artificial intelligence dataset, could help models reveal when and why tornadoes form, improving forecasters’ ability to issue warnings.
Researchers have developed a security solution for power-hungry AI models that offers protection against two common attacks.
MIT Sea Grant students apply machine learning to support local aquaculture hatcheries.