“Green Button” technology, which can quickly answer clinical questions—such as which drug is most effective for certain cancer patients—was developed by the Nigam Shah lab and led to raising $33M to scale AI-powered real-world evidence.
EchoNet, developed by the Zou Lab, is advanced image post-processing analysis software designed to aid diagnostic review, analysis, and reporting of echocardiographic DICOM images for cardiac function, and was just approved by the FDA. Congrats, team!
The purpose of the NRSA Individual Predoctoral Fellowship to Promote Diversity in Health-Related Research (F31) fellowship award is to enhance the diversity of the health-related research workforce by supporting the research training of predoctoral students from population groups that have been shown to be underrepresented in the biomedical research workforce.
Congratulations, Kristy!
A team led by senior BMI PhD students, Gautam Machiraju and Alex Derry, is excited to announce a new paper accepted for publication in the 2024 International Conference on Machine Learning (ICML) in Vienna, Austria. This work advances new modes of inference for Foundation Models, allowing them to efficiently and accurately identify class-specific features in various data modalities (sequences, images, graphs). This work was advised by multiple Stanford-based faculty including Parag Mallick, Christopher Ré, James Zou, and Russ Altman. Congrats to the team!