October 12, 2021
A group of Iowa State University College of Veterinary Medicine researchers are part of a team that has been selected from the Convergence Accelerator’s 2020 cohort to advance to phase 2 of the $50 million investment by the National Science Foundation (NSF).
A total of 10 research teams across the country were selected for year two of the project. The Iowa State team will receive a $1,996,701 grant after receiving a grant of $944,875 in year one.
The research teams are developing sustainable solutions for real-world application in quantum technology and AI innovation.
The “Precision Epidemiology (pEPI)” team is creating an online platform that converges data, AI models and expertise across the livestock production and health space for animal health management.
Dr. Maria Jose Clavijo Michelangeli, research assistant professor of veterinary diagnostic and production animal medicine, is the co-principal investigator on the project. Other College of Veterinary Medicine faculty members on the team include Dr. Rodger Main, director of the Veterinary Diagnostic Laboratory, Dr. Daniel Linhares, associate professor of veterinary diagnostics and production animal medicine; and Dr. Ganwu Li, associate professor of veterinary diagnostic and production animal medicine.
The research team also includes faculty members from the University of California-Davis, Carnegie-Mellon University and veterinarians at Pipestone Veterinary Services, Seaboard, Tosh and Hanor.
Over the next two years, the Iowa State team will participate in an innovation and entrepreneurial curriculum that includes product development, intellectual property, financial resources, sustainability planning, and communications and outreach.
“Animal health and food safety is a continuous challenge,” Clavijo said. “If something goes wrong it has a tremendous impact in our society.
“The maintenance of high swine health, high productivity and efficiency is key to guarantee the sustainability of the industry,” Clavijo said. “This requires informed and timely decisions using scientific-based analytical tools and prediction models, based on reliable and current data to better manage swine health.”