Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.

MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.

New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.

“Co-LLM” algorithm helps a general-purpose AI model collaborate with an expert large language model by combining the best parts of both answers, leading to more factual responses.

“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.

Computer scientist who specializes in database management systems joins the leadership of the Department of Electrical Engineering and Computer Science.

A new algorithm solves complicated partial differential equations by breaking them down into simpler problems, potentially guiding computer graphics and geometry processing.


Building on a landmark algorithm, researchers propose a way to make a smaller and more noise-tolerant quantum factoring circuit for cryptography.

With extensive international outreach experience as a faculty member and program leader, Boning brings a spirit of curiosity and collaboration to his new role.

An AI team coordinator aligns agents’ beliefs about how to achieve a task, intervening when necessary to potentially help with tasks in search and rescue, hospitals, and video games.

AI agents could soon become indistinguishable from humans online. Could “personhood credentials” protect people against digital imposters?

In controlled experiments, MIT CSAIL researchers discover simulations of reality developing deep within LLMs, indicating an understanding of language beyond simple mimicry.

A new algorithm helps robots practice skills like sweeping and placing objects, potentially helping them improve at important tasks in houses, hospitals, and factories.

CSAIL researchers introduce a novel approach allowing robots to be trained in simulations of scanned home environments, paving the way for customized household automation accessible to anyone.

Genomics and lab studies reveal numerous findings, including a key role for Reelin amid neuronal vulnerability, and for choline and antioxidants in sustaining cognition.

MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.

Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.

The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.

New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.

This new tool offers an easier way for people to analyze complex tabular data.

Twelve faculty members have been granted tenure in six units across MIT’s School of Engineering.

These models, which can predict a patient’s race, gender, and age, seem to use those traits as shortcuts when making medical diagnoses.

The dedicated teacher and academic leader transformed research in computer architectures, parallel computing, and digital design, enabling faster and more efficient computation.

LLMs trained primarily on text can generate complex visual concepts through code with self-correction. Researchers used these illustrations to train an image-free computer vision system to recognize real photos.

Combining natural language and programming, the method enables LLMs to solve numerical, analytical, and language-based tasks transparently.

The method uses language-based inputs instead of costly visual data to direct a robot through a multistep navigation task.

DenseAV, developed at MIT, learns to parse and understand the meaning of language just by watching videos of people talking, with potential applications in multimedia search, language learning, and robotics.

MIT CSAIL’s frugal deep-learning model infers the hidden physical properties of objects, then adapts to find the most stable grasps for robots in unstructured environments like homes and fulfillment centers.

With generative AI models, researchers combined robotics data from different sources to help robots learn better.

A new approach could streamline virtual training processes or aid clinicians in reviewing diagnostic videos.

“Alchemist” system adjusts the material attributes of specific objects within images to potentially modify video game models to fit different environments, fine-tune VFX, and diversify robotic training.

Fifteen new faculty members join six of the school’s academic departments.

MIT CSAIL researchers enhance robotic precision with sophisticated tactile sensors in the palm and agile fingers, setting the stage for improvements in human-robot interaction and prosthetic technology.

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.

A new technique that can automatically classify phases of physical systems could help scientists investigate novel materials.

The Trustnet browser extension lets individuals assess the accuracy of any content on any website.

Guoping Feng, Piotr Indyk, Daniel Kleitman, Daniela Rus, Senthil Todadri, and nine alumni are recognized by their peers for their outstanding contributions to research.

A new “consensus game,” developed by MIT CSAIL researchers, elevates AI’s text comprehension and generation skills.

A new algorithm learns to squish, bend, or stretch a robot’s entire body to accomplish diverse tasks like avoiding obstacles or retrieving items.

MIT CSAIL and Project CETI researchers reveal complex communication patterns in sperm whales, deepening our understanding of animal language systems.

Associate Professor Jonathan Ragan-Kelley optimizes how computer graphics and images are processed for the hardware of today and tomorrow.

Together, the Hasso Plattner Institute and MIT are working toward novel solutions to the world’s problems as part of the Designing for Sustainability research program.

Undergraduates Ben Lou, Srinath Mahankali, and Kenta Suzuki, whose research explores math and physics, are honored for their academic excellence.

Three neurosymbolic methods help language models find better abstractions within natural language, then use those representations to execute complex tasks.

An expert in robotics and AI, Shah succeeds Steven Barrett at AeroAstro.

For the first time, researchers use a combination of MEG and fMRI to map the spatio-temporal human brain dynamics of a visual image being recognized.

A new technique can be used to predict the actions of human or AI agents who behave suboptimally while working toward unknown goals.

A communication system whose users reveal only a few verified aspects of their identity can empower less confident participants to speak up, researchers report.

By providing plausible label maps for one medical image, the Tyche machine-learning model could help clinicians and researchers capture crucial information.

Researchers create a curious machine-learning model that finds a wider variety of prompts for training a chatbot to avoid hateful or harmful output.