As associate professor of information and computer science at UMass Amherst, Sheldon has studied bird migration routes for the last 15 years. This allows him to assess any avian role in the spread of infectious diseases and to help promote aviation safety. As the principal investigator of BirdFlow, a three-year, $830,000 National Science Foundation-funded bioinformatics project, he develops models of migration routes across large ranges using data collected via the free app eBird and compiled at the Cornell Lab of Ornithology.

“Birds are important ecological indicators,” says Sheldon. “Our models show us how birds are moving across broad ranges and help inform risk assessments for poultry farmers and how avian influenza might be spreading.”

The math major pursued a graduate degree in computer science at Cornell but found the theory too abstract. He pivoted his doctoral studies toward health and safety issues related to bird migration, and he quickly realized that machine learning and data science could help. “Dan’s career is a model connecting computer science with the real world in ways that continue to inspire countless birders, conservationists, and policy makers,” says Andrew Farnsworth, a senior researcher at the Cornell lab.

Sheldon has also developed projects that use weather radar across the United States to detect and measure bird migration. One tracks flocks of swallows and martins as they leave nighttime roosting locations, which has implications for aviation safety. “We’re going to continue to use all these different sources of big data,” Sheldon says, “to try to piece together these mysteries of what birds are doing.”