Daniel Mork, Ph.D.

Statistician | Data Scientist
Research Scientist | Dept. of Biostatistics
Harvard T.H. Chan School of Public Health

     

Curriculum vitae

Welcome to my personal webpage. I am a research scientist in the Department of Biostatistics at Harvard T.H. Chan School of Public Health working with National Studies on Air Pollution and Health. My statistical interests include structured machine learning, effect heterogeneity, functional regression, causal inference, Bayesian methodology and computation, and analysis of large, complex data. My work has been motivated and applied to a range of scientific questions in environmental and public health. Simultaneously, I create user-friendly software for efficient application and reproducibility in epidemiological studies. In addition, I enjoy consulting and collaborating in new areas of research and scientific exploration.

I received my PhD in Statistics from Colorado State University, where my dissertation focused on developing machine learning methods to understand the relationships between air pollution exposures during gestation and children’s birth and health outcomes. During graduate school I also worked as a statistical consultant with the Graybill Statistics and Data Science Laboratory. Prior to my work as a statistician, I taught high school math at Greeley West High School in Greeley, CO where I implemented a personalized learning system; worked for Target Corporation where I managed a range of workforce and procurement analytics and created data-driven tools for workflow optimization and cost efficiency; and was a web developer specializing in database-driven web services.

In my free time I enjoy biking, skiing, camping, and gardening.

Selected Statistical Publications

Policy-induced air pollution health disparities: Statistical and data science considerations
D Mork, S Delaney, F Dominici. Science (2024).

Incorporating prior information into distributed lag nonlinear models with zero-inflated monotone regression trees
D Mork, A Wilson. Bayesian Analysis (2024).
(arXiv) (code)

Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution
D Mork, M-A Kioumourtzoglou, M Weisskopf, B A Coull, A Wilson. Journal of the American Statistical Association (2024).
(arXiv) (code)

Estimating Perinatal Critical Windows of Susceptibility to Environmental Mixtures via Structured Bayesian Regression Tree Pairs
D Mork, A Wilson. Biometrics (2023).
(arXiv) (3MC video) (code)

Treed Distributed Lag Nonlinear Models
D Mork, A Wilson. Biostatistics (2022).
(arXiv) (code)

Selected Applied Publications

Applying a multistate survival model to explore the role of fine particles in promoting frailty in the Medicare cohort
N Fann, A Zanobetti, D Mork, W Steinhardt, A Rappold. Environmental Epidemiology (2024).

Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States
S Woodward, D Mork, X Wu, Z Hou, D Braun, F Dominici. PLOS Global Public Health (2023).

Time-lagged relationships between a decade of air pollution exposure and first hospitalization with Alzheimer’s disease and related dementias
D Mork, D Braun, A Zanobetti. Environment International (2023).

Software for Reproducible Research

R package: dlmtree

Teaching

Colorado State University