JuliaCon 2023

Julia through the lens of Policy Analysis: Applications
07-26, 16:30–17:00 (US/Eastern), 32-123

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


The first use case will be centered around agent based modeling to understand misinformation spread and mitigations. The second use case will be focused on the practicality of adversarial attacks in machine learning systems, specifically from the financial, biology and health domains, which is meant to inform public policy and drive mitigations.

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.