Today, the U.S. Department of Energy (DOE) announced $1 million for a one-year collaborative research project to develop artificial intelligence (AI) and machine learning (ML) algorithms for biomedical, personal healthcare, or other privacy-sensitive datasets. This funding is in response to congressional direction for DOE to expand its successful collaborative research efforts with the National Institutes of Health (NIH) in the areas of data and computation. Privacy-preserving AI research is a topic of mutual interest to DOE and NIH and joint research will encourage their respective research communities to work more closely on common scientific challenges.
The project is led by Argonne National Laboratory in collaboration with Lawrence Livermore National Laboratory, the University of Chicago, the Broad Institute, and Massachusetts General Hospital. The project is entitled “PALISADE-X: Privacy Preserving Analysis and Learning in Secure and Distributed Enclaves and Exascale Systems.” This project pursues innovative research to explore the development and use of privacy-preserving artificial intelligence and machine learning for key, grand challenge datasets such as those that are the focus of the NIH Bridge2AI program. A potential demonstration of AI capabilities includes predicting the severity of COVID-19 using radiological datasets from multiple organizations.
“DOE national laboratories have world-class computing and data management resources,” said Barbara Helland, Associate Director for Advanced Scientific Computing Research, DOE Office of Science. “Coupling privacy-preserving artificial intelligence and algorithms and DOE’s high-performance computers with NIH data will accelerate biomedical research.”