Emily Dorne
Emily Dorne is a lead data scientist at DrivenData where she develops machine learning models for social impact. Her expertise lies in classifying animals in camera trap videos to support conservationists, identifying harmful algal blooms to support water quality managers, and helping data scientists consider the ethical implications of their work. She is passionate about using data for social good and has previously worked at the Gates Foundation, Stanford Center for International Development, and the Brookings Institution.
Sessions
Camera traps are an essential tool for wildlife research. Zamba is an open source Python package that leverages machine learning and computer vision to automate time-intensive processing tasks for wildlife camera trap data. This talk will dive into Zamba's capabilities and key factors that influenced its design and development. Topics will include the importance of code-free custom model training, Zamba’s origins in an open machine learning competition, and the technical challenges of processing video data. Attendees will walk away with a better understanding of how machine learning and Python tools can support conservation efforts.