Community members receive 2024 MIT Excellence Awards, Collier Medal, and Staff Award for Distinction in Service
Staff members receive recognition for their exceptional support of the MIT community.
Staff members receive recognition for their exceptional support of the MIT community.
Ammonia could be a nearly carbon-free maritime fuel, but without new emissions regulations, its impact on air quality could significantly impact human health.
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.
When the senior isn’t using mathematical and computational methods to boost driverless vehicles and fairer voting, she performs with MIT’s many dance groups to keep her on track.
Professor of political science Evan Lieberman discusses his research into perceptions among African and American citizens about the climate crisis and how their governments are responding.
Graduate student Hammaad Adam is working to increase the supply of organs available for transplants, saving lives and improving health equity.
A new method to measure homophily in large group interactions offers insights into how groups might interact in the future.
Tamara Broderick uses statistical approaches to understand and quantify the uncertainty that can affect study results.
With batteries based on iron and air, Form Energy leverages MIT research to incorporate renewables into the grid.
By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.
An easy-to-use technique could assist everyone from economists to sports analysts.
The team used machine learning to analyze satellite and roadside images of areas where small farms predominate and agricultural data are sparse.
Scientists quantify a previously overlooked driver of human-related mercury emissions.
Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.