Htet is a data analyst at MDRC’s Center for Data Insights. Since joining MDRC, in the fall of 2018, Zarni has co-created a reusable predictive analytics assessment tool (PAAT); currently, he is working on its methodological and codebase expansion on machine learning model fairness and interpretability. He is also co-authoring an internal guide on predictive modeling in the context of social policy programs. One of Htet’s favorite projects involved collaborating with researchers from a state agency to apply predictive modeling to improve the agency’s program outcomes while being rigorous about data and model ethics. He also worked on developing MDRC’s first public R Shiny app for statistical power estimation.
Before joining MDRC, Htet was a Data Science for Public Good Fellow at the Social Decision Analytics Lab in Ballston, MD, where he worked on data deduplication, probabilistic record linkage, and synthetic data creation, among other topics. Htet has a graduate degree in applied statistics from New York University, where his course work and independent projects included causal inference and multilevel models.