Making genetic prediction models more inclusive
MIT computer scientists developed a way to calculate polygenic scores that makes them more accurate for people across diverse ancestries.
MIT computer scientists developed a way to calculate polygenic scores that makes them more accurate for people across diverse ancestries.
StructCode, developed by MIT CSAIL researchers, encodes machine-readable data in laser-cut objects by modifying their fabrication features.
Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.
Five MIT faculty, along with seven additional affiliates, are honored for outstanding contributions to medical research.
The Middle East Entrepreneurs of Tomorrow (MEET) program uses an MIT-inspired curriculum and MISTI student instructors to help young Palestinians and Israelis find common ground.
MIT engineers develop a long, curved touch sensor that could enable a robot to grasp and manipulate objects in multiple ways.
Designed to ensure safer skies, “Air-Guardian” blends human intuition with machine precision, creating a more symbiotic relationship between pilot and aircraft.
Open-source software by MIT MAD Fellow Jonathan Zong and others in the MIT Visualization Group reveals online graphics’ embedded data in the user’s preferred degree of granularity.
By analyzing epigenomic and gene expression changes that occur in Alzheimer’s disease, researchers identify cellular pathways that could become new drug targets.
Inspired by physics, a new generative model PFGM++ outperforms diffusion models in image generation.
The program supports “outstanding theoretical scientists.”
Researchers use multiple AI models to collaborate, debate, and improve their reasoning abilities to advance the performance of LLMs while increasing accountability and factual accuracy.
Brad Pentelute and his lab compel the anthrax delivery system to deliver antibody and peptide variants into cells to treat cancer.
With Style2Fab, makers can rapidly customize models of 3D-printable objects, such as assistive devices, without hampering their functionality.
Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values.