Mark Fredrickson

Mark Fredrickson is a Lecturer in the Department of Statistics at the University of Michigan, affiliated with the College of Literature, Science, and the Arts. He holds a Ph.D. in Statistics from the University of Illinois at Urbana-Champaign (2018), an M.A. in Political Science from UIUC, and a B.A. in Government and Computer Science from Claremont McKenna College. His research focuses on causal inference, network analysis, computational methods, and statistical software development, with applications in social science and public policy. He has contributed to open-source tools like the Optmatch R package for optimal matching in observational studies.

His work bridges statistical methodology and real-world applications, addressing challenges in high-dimensional data, network-mediated effects, and policy evaluation. Fredrickson emphasizes reproducibility and has published on topics ranging from voter turnout dynamics to environmental biomass estimation using LiDAR technology. His interdisciplinary approach integrates computational techniques with rigorous statistical frameworks to tackle complex societal questions.

Notable contributions include developing permutation tests for network analysis, improving baseline equivalency measures in high dimensions, and applying machine learning to historical event analysis. He actively engages with reproducible research practices and has authored multiple technical tools for statistical computing.