About me

I'm an Assistant Professor in the Department of Environmental Health and Engineering at Johns Hopkins University. My research involves policy-relevant issues at the intersection of AI/data science, climate justice, and health equity. I completed my PhD in Biomedical Data Science at Stanford and obtained a BS in Statistics at the University of Chicago. I've also worked in data science roles for the World Health Organization and Médecins Sans Frontières.

If you are a prospective PhD student interested in working with me, consider applying to Hopkins EHE through the track on Environmental Sustainability, Resilience, and Health and mentioning my name in your application. For potential postdocs or students already at Hopkins, please ping via email. I welcome trainees with a passion for environmental justice at all levels of technical proficiency.

Feel free to reach out if you'd like to discuss any shared interests or potential collaborations. My email is bhuynh {at} jhu {dot} edu. My last name can approximately be pronounced "hwin" or "win," and my pronouns are he/him.

Research themes

Environmental justice and policy

Planetary health and humanitarian operations

In collaboration with local organizations, NGOs, and UN agencies, we develop computational analyses to advance health equity for populations most vulnerable to climate change, such as refugees and those in humanitarian contexts. Approaches involve tools to facilitate operations and resource allocation, disaster risk reduction, and epidemiological modeling.

Data science and social inequity

Modern data science advances often translate poorly to marginalized populations in environments with scarce and low-quality data. We tailor computational methods to address and circumvent such data scarcity. Relevant areas include machine learning for causal inference, algorithmic fairness, small area estimation, probabilistic modeling, green AI, and explainable AI.

Selected publications

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

AI for Anticipatory Action: Moving Beyond Climate Forecasting
Forthcoming in AAAI Fall Symposium, 2023
Huynh BQ & Kiang MV.

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.

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]

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.