Samuel Paul Gyamfi's research focuses on the application of mathematical modeling and machine learning to systems analysis, optimization, and data-driven decision-making. His work bridges the gap between theoretical frameworks and real-world challenges in finance, insurance, and structural analysis.

Samuel’s interests extend to advanced probabilistic modeling, particularly in leveraging machine learning algorithms to enhance predictive accuracy in risk assessment and decision-making. With a background in applied mathematics and actuarial science, he explores novel methodologies to improve efficiency and reliability in areas such as claims processing, underwriting, and portfolio optimization.

He is particularly drawn to the intersection of optimization and machine learning, seeking innovative ways to develop adaptable models that can address diverse challenges in dynamic systems. His research also includes a focus on data analysis and statistical modeling, key tools for understanding complex patterns and behaviors in systems.

Published Papers

Antimicrobial Resistance and Tuberculosis Prevalence in Africa: A Public Health Concern (Download Paper)

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Samuel plans to keep this page updated with the evolution of his research interests and academic journey. Check back for the latest insights into his work and contributions!

Photos from the 2024 INFORMS Annual Meeting