SMART: Smarter Manual Annotation for Resource-constrained Collection of Training Data
This webinar is a part of the DataBytes Webinar Series, a space for National Consortium for Data Scientist members to talk about data problems and solutions and new data science research.
September's webinar, presented by RTI research data scientist and program manager Robert Chew, showcases the newly developed SMART application. SMART is an open source annotation software that leverages elements of active machine learning, reliability metrics, Ul/UX design to help data scientists and researchers reduce manual coding time and effort, making machine learning classification tasks more affordable and widely accessible.
Rob’s research interests broadly lie at the intersection of data science and public health, with a recent focus on computational social science. Currently, Rob is developing machine learning models to classify user types, communities and latent attributes on Twitter; using deep learning on satellite images to support survey sampling efforts in developing countries; developing dynamic data visualizations to help policymakers better understand results of a Bayesian meta-regression for program evaluation; and creating a software application to allow police departments to quantify and assess local “near repeat” phenomena.