Joshua Steier is a technical analyst at the RAND Corporation, focused on machine learning and modeling and simulation. He recently won an innovation award for investigating distributional shifts. He holds an M.S. degree in Applied Mathematics and Statistics from Stony Brook University.
20:30 UTC
Standard use cases for Julia appeal to the scientific community writ large. In contrast, Julia has not been widely adopted the public policy community. This talk is meant to demonstrate how Julia is useful for public policy through several use cases. These use cases are: Misinformation and Adversarial Machine Learning in decision critical systems