Amir Dehbi is a data scientist and applied machine learning researcher at the University of Colorado Boulder, specializing in audio signal processing and deep learning for acoustic analysis. He applies these methods to large-scale bioacoustics datasets in partnership with Yellowstone National Park to support their gray wolf conservation efforts. Amir is developing supervised models for species detection and unsupervised models for identifying structure in animal vocalizations. Prior to his fellowship, Amir spent eight years in the software industry — including five years at Google — advising enterprise organizations on cloud infrastructure, data systems, and ML implementations. He brings this applied engineering perspective to his research, focusing on building robust, scalable systems for real-world acoustic monitoring.
AI Acoustic Monitoring for Gray Wolves in Yellowstone National Park
Amir’s work aims to develop novel technologies that support wildlife conservation and monitoring while deepening our understanding of animal communication, ecology, and evolution. Early research in wolf bioacoustics has already demonstrated that AI can help decode wolf communication patterns. His work builds on these foundations, specifically advancing our ability to use bioacoustics technologies as a non-invasive monitoring tool for highly persecuted and federally listed endangered species.