Emily is a data scientist in the Data Science and Analytics group of the National Security Directorate. Her work focuses on applied machine learning and deep learning, with a particular interest in the development of fieldable and robust methods for application areas ranging from energy technologies to computational social science. Her work at PNNL has included the development of interactive machine learning systems, the discovery of causal mechanisms from observational data, and the design of evaluation strategies for social simulations. She is the machine learning integration lead for the Energy Storage Materials Initiative, where she develops machine learning methods to improve and accelerate the process of material discovery for energy storage technologies.
Prior to joining PNNL in January 2017, she worked as data scientist at the Blue Cross Blue Shield Association where she developed analysis methods for understanding patterns of community health. She was also a fellow in the University of Chicago Data Science for Social Good Fellowship program, where she developed predictive algorithms to help the World Bank detect fraud, corruption, and collusion in international development projects. She received her Ph.D. in physics from Princeton University in 2016, where her work focused on the development and application of calibration algorithms for microwave sensors for cosmological observations.
Doctor of Philosophy, Physics
Bachelor of Arts, Physics/Math