Welcome.

Hi, I’m Kiegan!

Photo of Kiegan in the shape of a hexagon.

I’m a Statistician specializing in static and interactive data visualization, computational reproducibility, and scientific communication. I believe that data-driven decision making can have a hugely positive impact on the world, particularly when those decisions inform policy.

On this site, I share a bit about the work I do and how I do it: applications and websites I’ve built, publications and presentations I’ve authored, and the tools I use to get things done. This is my personal website and the content here does not reflect the viewpoint of my employer or project clients.

Current Role

I am a Senior Statistican in the Statistics and Data Science department at NORC at the University of Chicago. I work on projects in higher education, public health, and health care. You can find out more about what I do on NORC’s website.

Education

I earned my Ph.D. in Statistics from Iowa State University in 2020, working with Dr. Heike Hofmann and Dr. Ulrike Genschel. My primary research topic was computational and statistical reproducibility of data science pipelines. I previously earned an M.S. in Statistics from Iowa State University and a B.A. in Mathematics and Spanish from St. Olaf College.

I build tools to help people explore data and social science research.

Bringing data to the public in a user-friendly way is an important part of modern data communication. I use a mix of tools (with R as my home base) to create interactive graphics and websites exploring social science and public policy data.

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Professional Associations

American Statistical Association
member since 2018

ASA Section on Statistical Graphics
Secretary/Treasurer, 2022-2024

Data Visualization Society
member since 2021


I study data visualization design and reproducibility in scientific research.

Understanding how users view, interact with, and interpret data visualizations is a key part of effective data communication. One of my key research areas is studying how elements of data visualization design impact a viewer’s perception and interpretation of a chart or graphic.

Ensuring reproducibility in scientific research is critical to building a robust, reliable, and trustworthy body of scientific literature. Whenever code is applied to data to complete a task, it is important to document the steps taken, how and why those steps were taken, and the environment in which those steps were taken. I study reproducibility over time in research that uses open-source software, primarily R.





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