Unpacking the “black box” to build better AI models
Stefanie Jegelka seeks to understand how machine-learning models behave, to help researchers build more robust models for applications in biology, computer vision, optimization, and more.
Stefanie Jegelka seeks to understand how machine-learning models behave, to help researchers build more robust models for applications in biology, computer vision, optimization, and more.
The role-playing game “On the Plane” simulates xenophobia to foster greater understanding and reflection via virtual experiences.
A new method can produce a hundredfold increase in light emissions from a type of electron-photon coupling, which is key to electron microscopes and other technologies.
Built on recent advances in machine learning, the model predicts how well individuals will produce and comprehend sentences.
MIT researchers are discovering which parts of the brain are engaged when a person evaluates a computer program.
But the harm from a discriminatory AI system can be minimized if the advice it delivers is properly framed, an MIT team has shown.
CAST Visiting Artist Andreas Refsgaard engages the MIT community in the ethics and play of creative coding.
This year's fellows will work across research areas including telemonitoring, human-computer interactions, operations research, AI-mediated socialization, and chemical transformations.
Researchers used a powerful deep-learning model to extract important data from electronic health records that could assist with personalized medicine.
Researchers at the Center for Theoretical Physics lead work on testing quantum gravity on a quantum processor.
Dan Huttenlocher is a professor of electrical engineering and computer science and the inaugural dean at MIT Schwarzman College of Computing.
New technique significantly reduces training and inference time on extensive datasets to keep pace with fast-moving data in finance, social networks, and fraud detection in cryptocurrency.
New technique could diminish errors that hamper the performance of super-fast analog optical neural networks.
MIT undergraduate researchers Helena Merker, Harry Heiberger, and Linh Nguyen, and PhD student Tongtong Liu, exploit machine-learning techniques to determine the magnetic structure of materials.
The MIT senior will pursue postgraduate studies in computer science in Ireland.