معرفی
Elizabeth Chin is an Assistant Professor in the Department of Biostatistics at the Bloomberg School of Public Health, Johns Hopkins University. Her work bridges biostatistics, causal inference, and public health, with a strong focus on equity and ethical decision-making systems.
- PhD in Biomedical Data Science, Stanford University (2022)
- BS in Applied Mathematics, UCLA (2017)
Her research interests include causal inference, algorithmic fairness, machine learning, and their application to public health challenges such as health inequities, environmental justice, and policy evaluation. She develops statistical methodologies to improve the robustness and fairness of data-driven decisions, particularly in vulnerable populations including incarcerated individuals.
Elizabeth Chin's recent publications span high-impact areas such as mental health care disparities, forensic statistics, environmental lead exposure, and pandemic response. Her work often appears in top journals like Nature Machine Intelligence, NEJM, JAMA Pediatrics, and PNAS. A key trend across her articles is the use of advanced statistical methods to address structural injustices and inform equitable policy.
- National Science Foundation Graduate Fellowship
- Stanford Graduate Fellowship
She has advised and collaborated with leading researchers in biostatistics and public health. Her work has received significant media attention and has been referenced in policy discussions, indicating real-world impact. Elizabeth Chin is actively contributing to national conversations on data ethics, health equity, and scientific rigor in public health.
