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Get to know Benjamin Q. Huỳnh

Benjamin Q. Huỳnh joins EHE as an Assistant Professor with his primary appointment in the Bloomberg School of Public Health.

Published
By
Nicole Hughes

New Faculty Q&A: Ben Huỳnh, PhD 

Ben Huynh received his PhD in biomedical data science at Stanford and has worked as a data scientist for the World Health Organization and Médecins Sans Frontières. He completed his postdoctoral training in the Department of Epidemiology & Population Health, also at Stanford University. Huynh’s research has involved developing and using cutting-edge data science techniques to address public health and health equity issues, planetary health, and climate change.

Ben Huynh

Where are you from?
I was born and raised in the city of Chicago. I loved living there, and my experiences as a Chicagoan particularly inform my research and teaching.

Tell us about your research and teaching interests.
I'm primarily interested in how to use data science to advance environmental justice, both locally and internationally. I work broadly across different locations, environmental exposures, and health outcomes, but the underlying theme is advancing health equity for marginalized populations. Examples range from using machine learning to identify water injustice in the United States to proactively providing aid to climate refugees before disasters strike.

I'm a big fan of EHE's cross-divisional design. Universities can sometimes be very siloed across schools, and I think having a shared mission between the engineering and public health schools is a great way to foster interdisciplinary work.

What interests you the most about data science and environmental justice?
Having grown up in a refugee community in Chicago, I've seen how the environment, and more broadly, structural determinants of health, can cause adverse health outcomes. It's an honor and a joy to be able to contribute to advancing environmental justice and to help train the next generation of researchers.

What's something about your research that you're excited to share with students?
Data science/AI methods are becoming both more impactful and easier to use every day but can often seem intimidating or even boring. I hope to show students and trainees how we can use these tools in an accessible way that meaningfully informs policy considerations.

What attracted you to the department? 
I'm a big fan of EHE's cross-divisional design. Universities can sometimes be very siloed across schools, and I think having a shared mission between the engineering and public health school is a great way to foster interdisciplinary work.