Tobias Oketch is a Statistician and Data Scientist whose work focuses on developing statistical methodology for complex and high-dimensional data. His research lies at the intersection of computational statistics, Bayesian inference, and survival analysis, with an emphasis on flexible and computationally efficient modeling frameworks.
He holds a Ph.D. in Mathematical Sciences (Statistics), an M.S. in Statistics from Mississippi State University (2025), an M.S. in Mathematical Sciences from East Tennessee State University (2017), and a B.S. in Applied Statistics from Maseno University (2008).
His work spans Bayesian methods, high-dimensional inference, computational simulations, and time-to-event modeling. He develops adaptive and semi-parametric Bayesian frameworks for failure time analysis and hazard rate estimation, alongside simulation-based approaches for studying complex statistical behavior.
His research is motivated by applications in biocomputing and biomedical research, including missing data imputation in genomics and proteomics and the analysis of high-dimensional biological systems, with the goal of advancing robust and flexible methods for data-intensive scientific discovery.
Tobias serves as an Instructor of Statistics at Columbus State Community College, where he supports students in developing strong foundations in statistical reasoning, data analysis, and quantitative problem solving, while continuing his research in statistical methodology and interdisciplinary applications.