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 Bill & Melinda Gates Foundation, Stanford Center for International Development, and the Brookings Institution.
Sessions
This talk illustrates how machine learning models to detect harmful algal blooms from satellite imagery can help water quality managers make informed decisions around public health warnings for lakes and reservoirs. Rooted in the development of the open source package CyFi, this talk includes insights around identifying when your model is getting the right answer for the wrong reasons, the upsides of using decision tree models with satellite imagery, and how to help non-technical users build confidence in machine learning models. The intended audience is those interested in using satellite imagery to monitor and respond to the world around us.