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MIT researchers develop an AI model that can detect future lung cancer risk

Deep-learning model takes a personalized approach to assessing each patient’s risk of lung cancer based on CT scans.

Gaining real-world industry experience through Break Through Tech AI at MIT

A new experiential learning opportunity challenges undergraduates across the Greater Boston area to apply their AI skills to a range of industry projects.

Nearly 30 students pose outdoors in between boulders and MIT Building 56 on a bright summer day.

Computers that power self-driving cars could be a huge driver of global carbon emissions

Study shows that if autonomous vehicles are widely adopted, hardware efficiency will need to advance rapidly to keep computing-related emissions in check.

Unpacking the “black box” to build better AI models

Stefanie Jegelka seeks to understand how machine-learning models behave, to help researchers build more robust models for applications in biology, computer vision, optimization, and more.

Simulating discrimination in virtual reality

The role-playing game “On the Plane” simulates xenophobia to foster greater understanding and reflection via virtual experiences.

This is your brain. This is your brain on code

MIT researchers are discovering which parts of the brain are engaged when a person evaluates a computer program.

MIT community members win 2023 IEEE medals and awards

Seven faculty and alumni are among the winners of the prestigious honors for electrical engineers and computer scientists.

Subtle biases in AI can influence emergency decisions

But the harm from a discriminatory AI system can be minimized if the advice it delivers is properly framed, an MIT team has shown.

Computational system streamlines the design of fluidic devices

This computational tool can generate an optimal design for a complex fluidic device such as a combustion engine or a hydraulic pump.

A faster way to preserve privacy online

New research enables users to search for information without revealing their queries, based on a method that is 30 times faster than comparable prior techniques.