Sometimes, when competitors collaborate, everybody wins
Engineers developed a planning tool that can help independent entities decide when they should invest in joint projects.
Topic
Laboratory for Information and Decision Systems (LIDS)
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MIT faculty, alumni named 2025 Sloan Research Fellows
Annual award honors early-career researchers for creativity, innovation, and research accomplishments.
Reducing carbon emissions from residential heating: A pathway forward
A new MIT study identifies steps that can lower not only emissions, but also costs, across the combined electric power and natural gas industries that now supply heating fuels.
Validation technique could help scientists make more accurate forecasts
MIT researchers developed a new approach for assessing predictions with a spatial dimension, like forecasting weather or mapping air pollution.
MIT engineers help multirobot systems stay in the safety zone
New research could improve the safety of drone shows, warehouse robots, and self-driving cars.
Expanding robot perception
Associate Professor Luca Carlone is working to give robots a more human-like awareness of their environment.
Algorithms and AI for a better world
Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.
How hard is it to prevent recurring blackouts in Puerto Rico?
Using the island as a model, researchers demonstrate the “DyMonDS” framework can improve resiliency to extreme weather and ease the integration of new resources.
MIT Schwarzman College of Computing welcomes Frida Polli as visiting innovation scholar
The neuroscientist turned entrepreneur will focus on advancing the intersection of behavioral science and AI across MIT.
MIT affiliates named 2024 Schmidt Sciences AI2050 Fellows
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
Researchers reduce bias in AI models while preserving or improving accuracy
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
Enabling AI to explain its predictions in plain language
Using LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model.
So you want to build a solar or wind farm? Here’s how to decide where.
MIT engineers show how detailed mapping of weather conditions and energy demand can guide optimization for siting renewable energy installations.
Improving health, one machine learning system at a time
Marzyeh Ghassemi works to ensure health-care models are trained to be robust and fair.
MIT researchers develop an efficient way to train more reliable AI agents
The technique could make AI systems better at complex tasks that involve variability.