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Research Interests

My work lies at the intersection of environmental change, health & wellbeing, and data science. “GeoHealth” and “planetary health” are distinct but related fields which seek not only to understand the impact of environmental changes on humans and vice-versa, but also to develop “solutions” or strategies for addressing environmental and public health challenges. Trained as an applied statistician, I focus on the application of innovative data science methods in this realm, with an emphasis on identifying pragmatic and just solutions. To make this more concrete, some questions my work has explored include:��

• What are the impacts of plastic waste policies (and subsequent changes in the amounts of waste and open burning of waste) on air quality? Can we use remotely sensed data paired with causal inference methods to credibly estimate this?
• When should we issue heat alerts to reduce the public health impacts of extreme heat? Can we use reinforcement learning (a type of machine learning / AI) to optimize this?
• Where [spatially] should we place low-cost air quality sensors to increase both accuracy and equity of real-time air quality reporting? Can we use Monte Carlo simulations to illustrate practical tradeoffs, such as between the spatial density of sensors and the error (noisiness) in the sensor measurements?
• To what extent do [existing] machine learning-derived air pollution datasets capture spatiotemporal variation in population exposure to wildfire smoke?��

More Info

I will be recruiting 1-2 graduate students (PhD or master’s) to start in Fall 2026.��If you are interested in working with me, please fill out��.��

The ideal candidate will have:

• Strong background in data science, econometrics, applied math, and/or computer science
• Genuine interest in GeoHealth / planetary health (environment <> humans), as demonstrated by past experiences in higher education, employment, and/or service
• Strong coding skills in R and/or Python
• Excellent writing and interpersonal communication skills
• Nice to have: experience with GIS, including remotely sensed data

What being in my lab would look like:

• Full funding for your degree, through a combination of research and TAing
• Mentorship on professional development in addition to more traditional research skills – for a taste of this, see�� of my blog
• An interdisciplinary research community spread between CIRES, the Department of Geography, the growing Public Health community at CU, and beyond
• Easy access to hiking, bike paths, and more in 51�Թ��� and the Rocky Mountains

Select Honors and Awards

• American Statistical Association Student Paper Competition Section on Statistical Learning & Data Science Applied Track Winner, 2024
• National Science Foundation Graduate Research Fellowship, 2020
• 51�Թ��� Outstanding Graduate of the College of Engineering & Applied Science, 2020��

Publications

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