Thierry Chekouo, PhD

Assistant Professor, Division of Biostatistics

Thierry Chekouo

Contact Info

Office Address:
A454-1 Mayo

Mailing Address:
420 Delaware St SE
MMC 303
Minneapolis, MN 55455

Assistant Professor, Division of Biostatistics

PhD, Statistics, Université de Montréal, 2013

MS, Statistics and Economics, National Higher

School of Statistics and Applied Economics, Côte d’Ivoire, 2007

MSc, Mathematics, Université de Yaounde I, 2004

BS, Mathematics, Université de Yaounde I, 2001


My research interests are in developing new statistical frameworks for analyzing datasets characterized by high dimensionality and complex structures such as high-throughput genomic, epigenomic, transcriptomic, proteomic, and imaging data. I have been broadly interested in Bayesian statistical methods, variable selection, clustering and bi-clustering, functional data analysis, and software development. A special focus is on developing integrative Bayesian models combining different sources of data for biomarker discovery and clinical prediction. Those models can take into account prior biological knowledge in order to better predict clinical outcomes.


Bayesian statistics, clustering and bi-clustering, computational statistics, integromics, variable selection, high-dimensional data, imaging data, omics data.



See all my publications on my Google Scholar page

  • Xiaotian Dai, David Champredon, Aamir Fazil, Chand S. Mangat, Shelley W. Peterson, and Edgard M. Mejia, Xuewen Lu, and Thierry Chekouo. Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater. Scientific Reports, 12, 13490 (2022).
  • Weibing Li and Thierry Chekouo. (2022). Bayesian Group Selection With Non-Local Priors. Computational Statistics. 37: 287–302.
  • Thierry Chekouo and Sandra E. Safo. (2021). Bayesian Integrative Analysis and Prediction with Application to Atherosclerosis Cardiovascular Disease. Biostatistics,
  • Thierry Chekouo, Shariq Mohammed and Arvind Rao. (2020). A Bayesian 2D Functional Linear Model for Gray-Level Co-occurrence Matrices in Texture Analysis of Lower Grade Gliomas. NeuroImage: Clinical. 18:102437.