Modeling relationships to solve complex problems efficiently
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
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.
By enabling users to chat with an older version of themselves, Future You is aimed at reducing anxiety and guiding young people to make better choices.
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.
A new method called Clio enables robots to quickly map a scene and identify the items they need to complete a given set of tasks.
The program will invite students to investigate new vistas at the intersection of music, computing, and technology.
The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.
Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.
MIT researchers speed up a novel AI-based estimator for medication manufacturing by 60 times.
By analyzing X-ray crystallography data, the model could help researchers develop new materials for many applications, including batteries and magnets.
Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.
“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.
Researchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias.
The three-day, hands-on conference hosted by the MIT RAISE Initiative welcomed youths and adults from nearly 30 countries.