UC Davis Researchers Receive $1.2 Million NIH Grant to Study Synthetic Data Use in Healthcare
Three researchers from UC Davis have been awarded a total $1.2 million grant over four years from the National Institutes of Health (NIH) to generate high-quality synthetic data using artificial intelligence and machine learning (AI/ML) that may potentially help physicians predict, diagnose and treat diseases.
The interdisciplinary research team involves principal investigator (PI) Thomas Strohmer, director of the Center for Data Science and Artificial Intelligence Research (CeDAR) and professor in the Department of Mathematics, and two UC Davis Health PIs Rachael Callcut, professor of surgery and chief research informatics officer, and Jason Adams, associate professor and physician of pulmonary, critical care and sleep.
For Strohmer and his team, the challenge is to balance the privacy concerns with data access, and to answer the overarching question: How to develop privacy-preserving machine learning techniques to make the data accessible for analytics?
“The goal ... is to define privacy in a rigorous, mathematical way –– known as differential privacy in the literature –– and design privacy-preserving machine learning techniques that will not break even when additional information becomes available.” — Thomas Strohmer, professor of data science mathematics
Read the rest of this article at UC Davis Office of Research.