Collaborative effort supports an MIT resilient to the impacts of extreme heat
Increasing severity and duration of heat drives data collection and resiliency planning for the forthcoming Climate Resiliency and Adaptation Roadmap.
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Increasing severity and duration of heat drives data collection and resiliency planning for the forthcoming Climate Resiliency and Adaptation Roadmap.
The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.
Members of the MIT community, supporters, and guests commemorate the opening of the new college headquarters.
PhD student Xinyi Zhang is developing computational tools for analyzing cells in the age of multimodal data.
New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
This new tool offers an easier way for people to analyze complex tabular data.
In a retrospective talk spanning multiple decades, Professor Al Oppenheim looked back over the birth of digital signal processing and shared his thoughts on the future of the field.
This tiny, biocompatible sensor may overcome one of the biggest hurdles that prevent the devices from being completely implanted.
Twelve faculty members have been granted tenure in six units across MIT’s School of Engineering.
These models, which can predict a patient’s race, gender, and age, seem to use those traits as shortcuts when making medical diagnoses.
Known for building connections between the social sciences, data science, and computation, the political science professor will lead IDSS into its next chapter.
This novel circuit architecture cancels out unwanted signals at the earliest opportunity.
VEIR, founded by alumnus Tim Heidel, has developed technology that can move more power over long distances, with the same footprint as traditional lines.
MosaicML, co-founded by an MIT alumnus and a professor, made deep-learning models faster and more efficient. Its acquisition by Databricks broadened that mission.