About Me

I'm a PhD candidate in the Department of Biomedical Data Science at Stanford, advised by Sanjay Basu and David Rehkopf. Broadly, my research interests involve using data science methods to help address social injustices manifesting as public health issues. My doctoral research specifically focuses on predictive modeling to inform decision-making in public health emergencies. Methodologically, I typically use machine learning, causal inference, and simulation methods (or any combination thereof) with heterogeneous data sources for forecasting and scenario planning. In terms of applications, I am particularly interested in refugee and humanitarian health.

Previously, I obtained a BS in Statistics at the University of Chicago and subsequently worked as a biostatistician there, where I worked on deep learning research for medical imaging in low-resource settings.

A preprint on our lastest work modeling the public health effects of an impending catastrophic oil spill in the Red Sea can be found here: [medRxiv]. Our paper on routine COVID-19 testing in high-risk environments can be found here: [PubMed]. Our paper on projected disparities in healthcare worker absenteeism from COVID-19 school closures can be found here: [PubMed]. Our work on forecasting forced displacement in Syria and Yemen can be found here: [PubMed] [pdf]. Full list of publications: [scholar] [cv]

Please feel free to reach out if you'd like to talk about any shared interests. My email is benhuynh {at} stanford {dot} edu. Twitter works too: @benqhuynh