2022-23 Takeda Fellows: Leveraging AI to positively impact human health
New fellows are working on health records, robot control, pandemic preparedness, brain injuries, and more.
New fellows are working on health records, robot control, pandemic preparedness, brain injuries, and more.
MIT Visiting Scholar Alfred Spector discusses the power of data science and visualization, as well as his new textbook on the subject.
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.
Startups founded by mechanical engineers are at the forefront of developing solutions to mitigate the environmental impact of manufacturing.
Seven faculty and alumni are among the winners of the prestigious honors for electrical engineers and computer scientists.
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.
Lincoln Laboratory’s Agile MicroSat will be the first small satellite to demonstrate long-duration, low-altitude flight with autonomous maneuvering.
A new algorithm for automatic assembly of products is accurate, efficient, and generalizable to a wide range of complex real-world assemblies.
New research enables users to search for information without revealing their queries, based on a method that is 30 times faster than comparable prior techniques.
Researchers used a powerful deep-learning model to extract important data from electronic health records that could assist with personalized medicine.
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.