Deep Learning for Biodiversity Conservation
Recent advances in sensor network technology, machine learning, and Big Data analytics can provide rigorous and cost-effective tools for monitoring biodiversity at scale. Conservation Metrics leverages these tools to monitor endangered species and ecosystems around the globe, and provides clients with the information needed for a data-driven approach to conservation. Matthew and David will discuss their technical approach and present several working case studies that show how deep learning can empower biologists to analyze petabytes of sensor data from microphones and cameras in remote corners of the world.
Matthew McKown is co-founder and CEO of Conservation Metrics. He is an ecologist whose research and work have focused on leveraging new technologies to improve wildlife monitoring and conservation outcomes. Matthew holds a PhD from the University of North Carolina at Chapel Hill and was a Postdoctoral Research Fellow at the University of California Santa Cruz where he focused on ways to monitor elusive species on remote islands. He has previously worked ways to improve Citizen Science projects with technology at the National Audubon Society and as a consultant with Environmental Resources Management in Southeast Asia.