Tyler McCormick

Tyler McCormick is a Professor in both the Department of Statistics and Department of Sociology at the University of Washington. He also serves as a Senior Data Science Fellow at the eScience Institute and maintains affiliations with the Center for Statistics and the Social Sciences, the Center for Studies in Demography and Ecology, and the Responsible AI Systems & Experiences (RAISE) initiative.

Dr. McCormick earned his Ph.D. in Statistics from Columbia University in 2011. His academic journey has established him as a leading researcher at the intersection of statistical methodology and social science applications.

McCormick's research program focuses on developing innovative statistical approaches to address complex societal challenges:

• Bayesian methods for modeling high-dimensional dependence structures in social networks
• Estimating vital demographic rates from sparse data sources
• Developing interpretable predictive models with proper uncertainty quantification
• Creating methodological frameworks for verbal autopsy analysis in global health

His publication record reveals a consistent trajectory of methodological innovation with practical impact. Recent work demonstrates increasing sophistication in handling network interference, integrating machine learning with statistical theory, and addressing data scarcity challenges in global health contexts. His research bridges theoretical advances with applications that inform public health policy and social science understanding.

McCormick has received significant recognition for his scholarly contributions:

• NIH Director's New Innovator Award (2019)
• Election as Fellow of the American Statistical Association (2023)

As an educator, McCormick teaches advanced graduate courses including Hierarchical Modeling for the Social Sciences and Quantitative Techniques in Sociology. His research has been supported by competitive grants from NICHD (2015-2020) focused on vital rate estimation in developing countries and NSF (2016-2018) funding for compact Bayesian models of social networks. His work has influenced policy discussions through media coverage in the Wall Street Journal and Washington Post.

McCormick leads the OpenVA initiative, providing open-source tools for verbal autopsy analysis, and has developed multiple R packages implementing his methodological contributions to network analysis and causal inference. His research continues to address critical challenges at the intersection of statistical theory, computational methods, and societal impact.