Ryan McCarthy

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Efficient cooling method could enable chip-based trapped-ion quantum computers

January 15, 2026

New technique could improve the scalability of trapped-ion quantum computers, an essential step toward making them practically useful.

Generative AI tool helps 3D print personal items that sustain daily use

January 15, 2026

“MechStyle” allows users to personalize 3D models, while ensuring they’re physically viable after fabrication, producing unique personal items and assistive technology.

Eighteen MIT faculty honored as “Committed to Caring” for 2025-27

January 12, 2026

The program recognizes outstanding mentorship of graduate students.

3 Questions: How AI could optimize the power grid

January 9, 2026

While the growing energy demands of AI are worrying, some techniques can also help make power grids cleaner and more efficient.

MIT researchers “speak objects into existence” using AI and robotics

January 9, 2026

The speech-to-reality system combines 3D generative AI and robotic assembly to create objects on demand.

A new lens on humanity

January 8, 2026

The inaugural MIT Human Insight Collaborative (MITHIC) Annual Event showcased the breadth of projects supported in the first year of the presidential initiative.

AI-generated sensors open new paths for early cancer detection

January 7, 2026

Nanoparticles coated with molecular sensors could be used to develop at-home tests for many types of cancer.

MIT scientists investigate memorization risk in the age of clinical AI

January 7, 2026

New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.

One pull of a string is all it takes to deploy these complex structures

January 5, 2026

A new method could enable users to design portable medical devices, like a splint, that can be rapidly converted from flat panels to a 3D object without any tools.

Guided learning lets “untrainable” neural networks realize their potential

December 22, 2025

CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.