Departmental Affiliations
Center & Institute Affiliations
Benjamin Huynh, PhD, uses data science and AI to address public health issues related to environmental injustices and disasters.
Research Interests
environmental justice; planetary health; health equity; climate justice; disaster risk management; humanitarian health; refugee health; data science; machine learning; causal inference; algorithmic fairness; forecasting; explainable AI; sustainable AI; small area estimation; probabilistic modeling
Additional Links
Experiences & Accomplishments
My research involves policy-relevant issues at the intersection of data science and planetary health. I study how data science can be used to advance environmental justice both locally and internationally, addressing health issues for populations most vulnerable to climate change and environmental exposures. Locally, I investigate how to address environmental exposures, policies, and algorithms that further environmental injustices. Internationally, I develop approaches in collaboration with local organizations, NGOs, and UN agencies to advance health equity and disaster resilience for populations such as refugees and those in humanitarian contexts.
I completed my PhD in Biomedical Data Science at Stanford and obtained a BS in Statistics at the University of Chicago. I have also worked in data science roles for the World Health Organization and Médecins Sans Frontières.
Select Publications
Selected publications
Public health impacts of an imminent Red Sea oil spill (Nature Sustainability, 2021)
Huynh BQ, Kwong LH, Kiang MV, Chin ET, Mohareb AM, Jumaan AO, Basu S, Geldsetzer P, Karaki FM, & Rehkopf DH.Potential for allocative harm in an environmental justice data tool (Pre-print, 2023)
Huynh BQ*, Chin ET*, Koenecke A, Ouyang D, Ho DE, Kiang MV, & Rehkopf DH. *Co-first author.Forecasting internally displaced population migration patterns in Syria and Yemen (Disaster Medicine and Public Health Preparedness, 2019)
Huynh BQ & Basu, S.AI for Anticipatory Action: Moving Beyond Climate Forecasting (Pre-print, 2023)
Huynh BQ & Kiang MV.Frequency of routine testing for COVID-19 in high-risk environments to reduce workplace outbreaks (Clinical Infectious Diseases, 2020)
Chin ET*, Huynh BQ*, Chapman LAC, Murrill M, Basu S, & Lo NC. *Co-first author.