Charles Herbst is a Postgraduate Student specializing in machine learning for data-scarcity and bioacoustics under the supervision of Dr. Emmanuel Dufourq. His research focuses on developing innovative machine learning approaches for analyzing acoustic data in environments with limited data availability, particularly for biodiversity monitoring and conservation.
As a researcher at the intersection of machine learning and ecology, Herbst works on computational methods to extract meaningful information from bioacoustic recordings, developing algorithms that can effectively identify and classify animal sounds even with limited training data.
His work contributes to conservation efforts through advanced technological solutions, using machine learning to monitor biodiversity, track wildlife populations, and understand ecosystem dynamics through acoustic data analysis in challenging data-scarce environments.