Kehinde Olobatuyi

I am a Computational Statistician specializing in Bayesian statistics, machine learning, and public health data science. My work focuses on developing advanced statistical and computational methods, such as hybrid MCMC and high-dimensional optimization, to solve complex, real-world problems in infectious disease modelling and health analytics.

With a strong foundation in both theory and application, I am particularly interested in bridging methodological innovation with impactful public health outcomes. My research has addressed critical challenges such as COVID-19 under-reporting, contributing to evidence-based decision-making and policy. In addition to my research, I am deeply committed to teaching and mentorship. I support students and trainees in building strong analytical skills, emphasizing reproducible research, hands-on data analysis, and the integration of statistical theory with practical applications in Python and R.

I actively collaborate across disciplines and institutions, and I am passionate about developing scalable, data-driven solutions that advance healthcare systems. My long-term goal is to contribute to a research-intensive academic environment while continuing to drive innovation at the intersection of statistics, machine learning, and public health.

Research Topics of Interest:

• Bayesian computation and scalable inference
• Deterministic and stochastic approximate methods
• Hidden Markov, state-space and latent-variable models
• Capture-recapture and population enumeration
• Statistical machine learning
• Infectious-disease modelling and under-reporting
• Administrative health data, health systems and health inequities

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