About Me

I'm a postdoctoral fellow in the Department of Epidemiology & Population Health at the Stanford University School of Medicine. I completed my PhD in the Department of Biomedical Data Science at Stanford and obtained a BS in Statistics at the University of Chicago. I've also worked as a data scientist for the World Health Organization and Médecins Sans Frontières. In Fall 2023, I will start as an Assistant Professor in the Department of Environmental Health and Engineering at the Johns Hopkins Bloomberg School of Public Health.

My research agenda broadly involves data science and planetary health. I have a particular interest in populations most vulnerable to climate change, such as refugees and those in humanitarian contexts. Methodologically, I develop and deploy algorithmic approaches to advance justice in public health, drawing from techniques in machine learning, causal inference, and simulation modeling.

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

Selected Publications

For a full list of publications, see here: Google Scholar.

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. [Invited commentary] [Nature research highlight] [Stanford press release] [BBC] [CNN] [The Economist] [The Guardian] [Al Jazeera] [The New Yorker] [The World]

Routine asymptomatic testing strategies for airline travel during the COVID-19 pandemic: a simulation analysis
The Lancet Infectious Diseases, 2021
Kiang MV, Chin ET, Huynh BQ, Chapman LAC, Rodríguez-Barraquer I, Greenhouse B, Rutherford G, Bibbins-Domingo K, Havlir D, Basu S, & Lo NC. (Editor's choice.) [UCSF press release] [NPR] [ABC News] [SF Chronicle] [Cited in CDC report] [Cited in UK guidance]

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. [Cited in CDC guidance] [Cited in Africa CDC Guidance] [Cited in UC System-wide testing recommendations]

Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study
BMC Medicine, 2020
Chin ET*, Huynh BQ*, Lo NC, Hastie T, & Basu S. *Co-first author. [Cited in WHO report]

Forecasting internally displaced population migration patterns in Syria and Yemen
Disaster Medicine and Public Health Preparedness, 2019
Huynh BQ & Basu, S. [Pre-print PDF] [Cited in UN report]

Breast lesion classification based on dynamic contrast enhanced magnetic resonance images sequences with long short-term memory networks
Journal of Medical Imaging, 2018
Antropova NO, Huynh BQ, Li H, & Giger ML.

Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning
SPIE Medical Imaging, 2017
Huynh BQ, Antropova NO, & Giger ML.

A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets
Medical Physics, 2017
Antropova NO*, Huynh BQ*, & Giger ML. *Co-first author. (Editor's choice.)

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks
Journal of Medical Imaging, 2016
Huynh BQ, Li H, & Giger ML.