Rate My Professor Thierry Chekouo

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.