Two MIT PhD students awarded J-WAFS fellowships for their research on water
Jonathan Bessette and Akash Ball have been named 2024-25 J-WAFS Fellows for water treatment technologies.
Fostering research, careers, and community in materials science
MICRO internship program expands, brings undergraduate interns from other schools to campus.
To build a better AI helper, start by modeling the irrational behavior of humans
A new technique can be used to predict the actions of human or AI agents who behave suboptimally while working toward unknown goals.
Using deep learning to image the Earth’s planetary boundary layer
Lincoln Laboratory researchers are using AI to get a better picture of the atmospheric layer closest to Earth's surface. Their techniques could improve weather and drought prediction.
A first-ever complete map for elastic strain engineering
New research by a team of MIT engineers offers a guide for fine-tuning specific material properties.
MIT-derived algorithm helps forecast the frequency of extreme weather
The new approach “nudges” existing climate simulations closer to future reality.
Engineering household robots to have a little common sense
With help from a large language model, MIT engineers enabled robots to self-correct after missteps and carry on with their chores.
For MIT students, there is much to learn from crafting a chair
In class 4.500 (Design Computation), Professor Larry Sass teaches the thoughtful and experimental process of design through the familiar idea of a chair, while exploring “foundational technologies.”
Optimizing nuclear fuels for next-generation reactors
While working to nurture scientific talent in his native Nigeria, Assistant Professor Ericmoore Jossou is setting his sights on using materials science and computation to design robust nuclear components.
Exploring the cellular neighborhood
Software allows scientists to model shapeshifting proteins in native cellular environments.
Cutting carbon emissions on the US power grid
An online model enables users to calculate the least-cost strategy for a specific regional grid under various assumptions; outcomes vary widely from region to region.
Generative AI for smart grid modeling
MIT LIDS awarded funding from the Appalachian Regional Commission as part of a multi-state collaborative project to model and test new smart grid technologies for use in rural areas.
Automated method helps researchers quantify uncertainty in their predictions
An easy-to-use technique could assist everyone from economists to sports analysts.
MIT researchers remotely map crops, field by field
The team used machine learning to analyze satellite and roadside images of areas where small farms predominate and agricultural data are sparse.