Charles J Geyer is a Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. He has been an active researcher since at least 1988, with a sustained record of scholarly output in statistical theory and methodology. His research focuses on advanced statistical methods including maximum likelihood estimation, exponential families, Markov Chain Monte Carlo (MCMC), likelihood-free inference, and aster models. These methods are applied in interdisciplinary contexts such as evolutionary biology, genetics, and ecological modeling, particularly in life history analysis and phenotypic selection. His work bridges theoretical statistics with practical computational tools for complex data. The recent publications highlight a strong trend toward computationally efficient inference, especially in models where traditional maximum likelihood fails. He has contributed to the development of the R package glmm for generalized linear mixed models and has worked extensively on envelope methods and variance reduction techniques. His research outputs include numerous peer-reviewed articles, book chapters, and publicly shared datasets, reflecting a commitment to open science. Scientific contributions include: Development of MCMC methods for dependent data Foundational work on likelihood inference when MLE does not exist Integration of aster models with envelope methodology Applications in evolutionary and ecological statistics He has collaborated with researchers such as D. J. Eck, R. G. Shaw, and R. D. Cook. While formal advisee relationships are not listed, his collaborative work suggests mentorship and academic leadership. He has not received any explicitly mentioned awards in the provided text, but his sustained impact is evident through citations and methodological influence. His datasets are archived in the University of Minnesota Data Repository, supporting reproducible research.
