Rate My Professor Thierry Chekouo

Thierry Chekouo, PhD, serves as Associate Professor and Medtronic Faculty Fellow in the Division of Biostatistics and Health Data Science at the University of Minnesota Twin Cities School of Public Health. He obtained his PhD in Statistics from the Université de Montréal in 2013, MS in Statistics and Economics from the National Higher School of Statistics and Applied Economics (ENSEA) in Côte d’Ivoire in 2007, MSc in Mathematics from the Université de Yaounde I in Cameroon in 2004, and BS in Mathematics from the Université de Yaounde I in 2001. His professional appointments include Assistant Professor in the Department of Mathematics and Statistics at the University of Calgary from 2018 to 2022, with adjunct roles in Biochemistry & Molecular Biology and Mathematics and Statistics from 2019 to present; Assistant Professor of Statistics in the Department of Mathematics and Statistics at the University of Minnesota Duluth from 2016 to 2018; and Postdoctoral Fellow in the Department of Biostatistics at The University of Texas MD Anderson Cancer Center from 2013 to 2016.

Chekouo's research centers on developing new statistical frameworks for high-dimensional datasets with complex structures, such as high-throughput genomic, epigenomic, transcriptomic, proteomic, and imaging data. His academic interests include Bayesian statistical methods, variable selection, clustering and bi-clustering, functional data analysis, computational statistics, integromics, and integrative Bayesian models that incorporate prior biological knowledge for biomarker discovery and clinical prediction. Key publications feature "Bayesian Integrative Analysis and Prediction with Application to Atherosclerosis Cardiovascular Disease" (Biostatistics, 2021, co-authored with Sandra E. Safo), "Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater" (Scientific Reports, 2022, with Xiaotian Dai et al.), "A Bayesian predictive model for imaging genetics with application to schizophrenia" (The Annals of Applied Statistics, 2016, with Francesco Stingo et al.), and "miRNA-Target Gene Regulatory Networks: A Bayesian Integrative Approach to Biomarker Selection with Application to Kidney Cancer" (Biometrics, 2015). He has received the Medtronic Faculty Fellowship and grants including the Natural Sciences and Engineering Research Council Discovery Grant (2019–2023) for Bayesian integrative approaches to imaging and genomic data.