How to more efficiently study complex treatment interactions
A new approach for testing multiple treatment combinations at once could help scientists develop drugs for cancer or genetic disorders.
A new approach for testing multiple treatment combinations at once could help scientists develop drugs for cancer or genetic disorders.
In a new study, researchers discover the root cause of a type of bias in LLMs, paving the way for more accurate and reliable AI systems.
Read MoreA new book from Professor Munther Dahleh details the creation of a unique kind of transdisciplinary center, uniting many specialties through a common need for data science.
Read MoreThe system automatically learns to adapt to unknown disturbances such as gusting winds.
Read MoreThe winning essay of the Envisioning the Future of Computing Prize puts health care disparities at the forefront.
Read MoreThrough collaborations with organizations like BREIT in Peru, the MIT Institute for Data, Systems, and Society is upskilling hundreds of learners around the world in data science and machine learning.
Read MoreTrained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.
Read More“IntersectionZoo,” a benchmarking tool, uses a real-world traffic problem to test progress in deep reinforcement learning algorithms.
Read MoreUsing diagrams to represent interactions in multipart systems can provide a faster way to design software improvements.
Read MoreBy eliminating redundant computations, a new data-driven method can streamline processes like scheduling trains, routing delivery drivers, or assigning airline crews.
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