How Nigerian Statistician Olasehinde Omolayo Is Advancing Survival Analysis Research in the United States

In the quiet research spaces of Georgia State University, a Nigerian scholar is pioneering innovative approaches in survival analysis that could redefine how statisticians and researchers interpret censored data.

His name is Olasehinde Omolayo, a statistician, data engineer, and graduate assistant whose current work focuses on Empirical Likelihood Inference on Residual Life, a crucial contribution in the field of nonparametric statistics and survival analysis. His research is crucial because residual life estimation helps clinicians understand how much time a patient is likely to live after surviving a certain period, which is vital for treatment planning and patient counselling in diseases like cancer, HIV/AIDS, and hepatitis. Using empirical likelihood methods provides more accurate and robust survival insights even when data is censored or incomplete, a common challenge in long-term disease studies.

While breakthroughs in statistics often conjure images of theoretical concepts or big data applications in tech, much of the foundational advancement happens within university research labs. These environments serve as critical incubators for methodological innovation, where scholars like Olasehinde develop tools that improve real-world decision-making across fields such as healthcare, engineering, and public policy.

Olasehinde’s academic journey began in Akure, Nigeria, where he earned a Bachelor of Technology in Statistics from the Federal University of Technology. With a strong foundation in theoretical statistics and years of practical experience as a data engineer, he returned to academia with a focused intent: to apply rigorous statistical inference techniques to complex, real-world problems.

At Georgia State University, where he is completing a Master’s degree in Statistics, Olasehinde holds a graduate assistantship that allows him to work closely with faculty members on advanced research topics. His current project explores empirical likelihood methods to estimate residual life.

He provides an alternative to the traditional method which have always been used for the estimation of residual lifetimes.  With Olasehinde’s work, he leverages empirical likelihood within a quantile framework to eliminate assumptions about data’s underlying distribution and variance estimation requirement. His research’s methodology was validated through simulations and real-world datasets, with promising results showing that it can outperform mean-based approaches. His research paved the way by addressing challenges encountered in survival analysis and reliability engineering. Olasehinde explains. “Using quantile-based empirical likelihood allows us to retain flexibility while gaining precision.” With his research, there are great implications for a wide range of fields, including clinical survival studies, public health research, actuarial science, and industrial reliability testing.

Olasehinde’s contribution is not only technical but also cross-cultural. As a Nigerian scholar immersed in U.S.-based academic research, he brings a global perspective to statistical challenges, often informed by his understanding of under-resourced environments. This insight enhances the relevance of his models, especially in fields where data scarcity, infrastructure limitations, or uneven healthcare access distort inference.

Despite the complexity of his work, Olasehinde remains committed to accessibility. He assists peers with programming challenges, mentors students on statistical modeling, and is exploring opportunities to open-source parts of his methodology for broader academic use.

“For me,” he reflects, “statistical inference is more than a technical task. It’s a bridge between uncertainty and understanding, and sometimes, it’s the key to asking better questions.”

“The journey from a statistical bureau in Ibadan to a research lab in Atlanta wasn’t straightforward,” he says. “But every step taught me something about what data can tell us and how much more it can do if we listen closely.”

That diligence, combined with deep technical insight, is what sets Olasehinde apart. In an era where data-driven decision-making transcends borders and disciplines, his work reflects the growing importance of globally informed, nonparametric methods in statistical research. His hands may be on the keyboard in Georgia, but the impact of his models, built to handle censored and incomplete data, reverberates across academic and applied domains, reminding us that innovation is not confined by geography.

As his program at Georgia State University nears completion, there is little doubt that Olasehinde’s contributions will continue to resonate, whether in the publication of academic papers, the expansion of his residual life work, or the next public health challenge that demands clarity from complexity.

For now, he remains focused on his immediate goals: refining his quantile-based empirical likelihood framework, training undergraduates in applied statistics, and making sure that every line of R code speaks as clearly and accurately as the data demands.

Because in Olasehinde’s world, numbers are more than values; they are voices. And he’s determined to make sure those voices are heard, understood, and used for good.