Inez Khan

Inez Khan is a research assistant at the RAND Corporation. Her work at RAND primarily involves the intersection of machine learning and policy, including model estimation, simulations, and statistical analysis, to solve various problems in the defense and security space. Some of her current work includes modelling the security clearance process to identify bottlenecks, identifying how to counter adversarial AI, and simulating invasion scenarios for the Marine Corps. She also has interests in access issues due to socioeconomic and racial disparities. Her methodological interests include causal inference, non-parametric estimation, and regression modelling in addition to machine learning. Khan graduated from Carnegie Mellon University with a B.S in Statistics and Machine Learning.