Bhavesh Patel completed his Ph.D. in Mechanical Engineering at the University of California Berkeley where he specialized in computational modeling. He joined the California Medical Innovations Institute (Calmi2) right after graduating in 2015 where he has been developing computational models for various organs and medical devices. He has been involved in Findable, Accessible, Interoperable, and Reusable (FAIR) data practices since 2019. He founded and leads the FAIR Data Innovations Hub, a division at Calmi2 where he and his team are developing various computer tools that make it easier for biomedical researchers to make their data FAIR. These include tools such as SODA (Software to Organize Data Automatically) for the NIH SPARC program and fairhub.io for the NIH Bridge2AI program.
Optimizing AI-based discoveries through FAIR data practices
The Findable, Accessible, Interoperable, and Reusable (FAIR) Principles provide a widely accepted framework for curating and sharing research data in a way that optimizes reuse by humans and machines. Here, I will present our solutions for FAIRifying biomedical research data, which is crucial to increase the pace of AI-based discoveries.
William Oh, Mount Sinai