Electrical engineering and computer science (EECS) Archives

November 11, 2025

Leading quantum at an inflection point

Danna Freedman is seeking the early adopters.

She is the faculty director of the nascent MIT Quantum Initiative, or QMIT. In this new role, Freedman is giving shape to an ambitious, Institute-wide effort to apply quantum breakthroughs to the most consequential challenges in science, technology, industry, and national security.

The interdisciplinary endeavor, the newest of MIT President Sally Kornbluth’s strategic initiatives, will bring together MIT researchers and domain experts from a range of industries to identify and tackle practical challenges wherever quantum solutions could achieve the greatest impact.

“We’ve already seen how the breadth of progress in quantum has created opportunities to rethink the future of security and encryption, imagine new modes of navigation, and even measure gravitational waves more precisely to observe the cosmos in an entirely new way,” says Freedman, the Frederick George Keyes Professor of Chemistry.

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November 10, 2025

Charting the future of AI, from safer answers to faster thinking

Adoption of new tools and technologies occurs when users largely perceive them as reliable, accessible, and an improvement over the available methods and workflows for the cost. Five PhD students from the inaugural class of the MIT-IBM Watson AI Lab Summer Program are utilizing state-of-the-art resources, alleviating AI pain points, and creating new features and capabilities to promote AI usefulness and deployment — from learning when to trust a model that predicts another’s accuracy to more effectively reasoning over knowledge bases. Together, the efforts from the students and their mentors form a through-line, where practical and technically rigorous research leads to more dependable and valuable models across domains.

Building probes, routers, new attention mechanisms, synthetic datasets, and program-synthesis pipelines, the students’ work spans safety, inference efficiency, multimodal data, and knowledge-grounded reasoning. Their techniques emphasize scaling and integration, with impact always in sight.

Learning to trust, and when

MIT math graduate student Andrey Bryutkin’s research prioritizes the trustworthiness of models.

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November 5, 2025

Teaching robots to map large environments

A robot searching for workers trapped in a partially collapsed mine shaft must rapidly generate a map of the scene and identify its location within that scene as it navigates the treacherous terrain.

Researchers have recently started building powerful machine-learning models to perform this complex task using only images from the robot’s onboard cameras, but even the best models can only process a few images at a time. In a real-world disaster where every second counts, a search-and-rescue robot would need to quickly traverse large areas and process thousands of images to complete its mission.

To overcome this problem, MIT researchers drew on ideas from both recent artificial intelligence vision models and classical computer vision to develop a new system that can process an arbitrary number of images. Their system accurately generates 3D maps of complicated scenes like a crowded office corridor in a matter of seconds.

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October 23, 2025

A “seating chart” for atoms helps locate their positions in materials

If you think of a single atom as a grain of sand, then a wavelength of visible light — which is a thousand times larger than the atom’s width — is comparable to an ocean wave. The light wave can dwarf an atom, missing it entirely as it passes by. This gulf in size has long made it impossible for scientists to see and resolve individual atoms using optical microscopes alone.

Only recently have scientists found ways to break this “diffraction limit,” to see features that are smaller than the wavelength of light. With new techniques known as super-resolution microscopy, scientists can see down to the scale of a single molecule.

And yet, individual atoms have still been too small for optical microscopes — which are much simpler and less expensive than super-resolution techniques — to distinguish, until now.

In an open-access paper appearing today in Nature Communications,

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October 22, 2025

Charts can be social artifacts that communicate more than just data

The degree to which someone trusts the information depicted in a chart can depend on their assumptions about who made the data visualization, according to a pair of studies by MIT researchers.

For instance, if someone infers that a graph about a controversial topic like gun violence was produced by an organization they feel is in opposition with their beliefs or political views, they may discredit the information or dismiss the visualization all together.

The researchers found that even the clearest visualizations often communicate more than the data they explicitly depict, and can elicit strong judgments from viewers about the social contexts, identities, and characteristics of those who made the chart.

Readers make these assessments about the social context of a visualization primarily from its design features, like the color palette or the way information is arranged, rather than the underlying data. Often, these inferences are unintended by the designers.

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October 18, 2025

New software designs eco-friendly clothing that can reassemble into new items

It’s hard to keep up with the ever-changing trends of the fashion world. What’s “in” one minute is often out of style the next season, potentially causing you to re-evaluate your wardrobe.

Staying current with the latest fashion styles can be wasteful and expensive, though. Roughly 92 million tons of textile waste are produced annually, including the clothes we discard when they go out of style or no longer fit. But what if we could simply reassemble our clothes into whatever outfits we wanted, adapting to trends and the ways our bodies change?

A team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Adobe are attempting to bring eco-friendly, versatile garments to life. Their new “Refashion” software system breaks down fashion design into modules — essentially, smaller building blocks — by allowing users to draw, plan,

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October 17, 2025

Method teaches generative AI models to locate personalized objects

Say a person takes their French Bulldog, Bowser, to the dog park. Identifying Bowser as he plays among the other canines is easy for the dog-owner to do while onsite.

But if someone wants to use a generative AI model like GPT-5 to monitor their pet while they are at work, the model could fail at this basic task. Vision-language models like GPT-5 often excel at recognizing general objects, like a dog, but they perform poorly at locating personalized objects, like Bowser the French Bulldog.

To address this shortcoming, researchers from MIT and the MIT-IBM Watson AI Lab have introduced a new training method that teaches vision-language models to localize personalized objects in a scene.

Their method uses carefully prepared video-tracking data in which the same object is tracked across multiple frames. They designed the dataset so the model must focus on contextual clues to identify the personalized object,

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October 12, 2025

Using generative AI to diversify virtual training grounds for robots

Chatbots like ChatGPT and Claude have experienced a meteoric rise in usage over the past three years because they can help you with a wide range of tasks. Whether you’re writing Shakespearean sonnets, debugging code, or need an answer to an obscure trivia question, artificial intelligence systems seem to have you covered. The source of this versatility? Billions, or even trillions, of textual data points across the internet.

Those data aren’t enough to teach a robot to be a helpful household or factory assistant, though. To understand how to handle, stack, and place various arrangements of objects across diverse environments, robots need demonstrations. You can think of robot training data as a collection of how-to videos that walk the systems through each motion of a task. Collecting these demonstrations on real robots is time-consuming and not perfectly repeatable, so engineers have created training data by generating simulations with AI (which don’t often reflect real-world physics),

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October 10, 2025

MIT Schwarzman College of Computing and MBZUAI launch international collaboration to shape the future of AI

The MIT Schwarzman College of Computing and the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) recently celebrated the launch of the MIT–MBZUAI Collaborative Research Program, a new effort to strengthen the building blocks of artificial intelligence and accelerate its use in pressing scientific and societal challenges.

Under the five-year agreement, faculty, students, and research staff from both institutions will collaborate on fundamental research projects to advance the technological foundations of AI and its applications in three core areas: scientific discovery, human thriving, and the health of the planet.

“Artificial intelligence is transforming nearly every aspect of human endeavor. MIT’s leadership in AI is greatly enriched through collaborations with leading academic institutions in the U.S. and around the world,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “Our collaboration with MBZUAI reflects a shared commitment to advancing AI in ways that are responsible,

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October 4, 2025

AI maps how a new antibiotic targets gut bacteria

For patients with inflammatory bowel disease, antibiotics can be a double-edged sword. The broad-spectrum drugs often prescribed for gut flare-ups can kill helpful microbes alongside harmful ones, sometimes worsening symptoms over time. When fighting gut inflammation, you don’t always want to bring a sledgehammer to a knife fight.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and McMaster University have identified a new compound that takes a more targeted approach. The molecule, called enterololin, suppresses a group of bacteria linked to Crohn’s disease flare-ups while leaving the rest of the microbiome largely intact. Using a generative AI model, the team mapped how the compound works, a process that usually takes years but was accelerated here to just months.

“This discovery speaks to a central challenge in antibiotic development,” says Jon Stokes, senior author of a new paper on the work,

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