MIT Schwarzman College of Computing
MIT Schwarzman College of Computing

Four from MIT named 2025 Rhodes Scholars

Yiming Chen ’24, Wilhem Hector, Anushka Nair, and David Oluigbo will start postgraduate studies at Oxford next fall.

3 Questions: Inverting the problem of design

MIT and IBM researchers are creating linkage mechanisms to innovate human-AI kinematic engineering.

A causal theory for studying the cause-and-effect relationships of genes

By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.

A portable light system that can digitize everyday objects

A new design tool uses UV and RGB lights to change the color and textures of everyday objects. The system could enable surfaces to display dynamic patterns, such as health data and fashion designs.

Despite its impressive output, generative AI doesn’t have a coherent understanding of the world

Researchers show that even the best-performing large language models don’t form a true model of the world and its rules, and can thus fail unexpectedly on similar tasks.

Empowering systemic racism research at MIT and beyond

Researchers in the MIT Initiative on Combatting Systemic Racism are building an open data repository to advance research on racial inequity in domains like policing, housing, and health care.

Nanoscale transistors could enable more efficient electronics

Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.

MIT Schwarzman College of Computing launches postdoctoral program to advance AI across disciplines

The new Tayebati Postdoctoral Fellowship Program will support leading postdocs to bring cutting-edge AI to bear on research in scientific discovery or music.

A faster, better way to train general-purpose robots

Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.

Making it easier to verify an AI model’s responses

By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.