Machine Learning at Macaulay Library
Powering an intelligent wildlife media archive.
Using millions of photos, audio recordings, and videos collected from citizen scientists around the world, we are building datasets and models to power tools like Merlin Bird ID. These datasets and models will power the future of engaging identification apps, scientific research, and biodiversity conservation.
Grant Van Horn, PhD
Grant uses data in the Macaulay Library to prototype machine learning applications that can be utilized and deployed throughout the Cornell Lab of Ornithology. His research focuses on detection and identification of wildlife in images, audio, and video. His passion lies at the intersection of human-machine collaboration, where the collective strengths of humans and machines can be used to answer questions from data. Grant received his PhD from Caltech in 2018, advised by Dr. Pietro Perona.
Benjamin Hoffman, PhD
Since receiving his PhD in mathematics from Cornell, Benjamin has focused on applications of machine learning to biology and conservation. On a given day, you might find him implementing custom layers for audio signal processing, or designing metrics for model robustness to environmental noise. He is excited to see how Merlin Sound ID can be used by conservation organizations, and how the techniques developed by the machine learning team may help answer biological questions.
Jess is an avid birder and enthusiastic software developer. She is passionate about advancing the role of technology in conservation, and cherishes working alongside a wonderful and like-minded team!