My journey into academia
From industry practice to university teaching
My decision to move into academia came after years of working with data outside the university environment. By the time I became a full-time lecturer, I already had practical experience with statistical analysis, programming and data-related projects.
Today, I am a Lecturer in Statistics at Adeleke University, Ede, Nigeria, where I teach Statistics, Data Science and related quantitative courses at undergraduate and postgraduate levels. I have also taught and worked with students and researchers in Nursing, Public Health, Biological Sciences and other disciplines where statistical methods are important.
I hold a PhD in Statistics from Olabisi Onabanjo University. My doctoral research developed two-parameter ridge-type estimators for handling multicollinearity in logistic regression models through theoretical development, extensive simulation and application to cancer diagnostic data.
I have authored and co-authored over 30 research publications and scholarly outputs. My interests include statistical modelling, regression methods, machine learning, artificial intelligence, time-series analysis, forecasting and econometrics, with a growing focus on Biostatistics, Medical Statistics and Epidemiology.
Beyond the classroom
Statistics applied to real problems
Becoming a lecturer has not separated me from practice. I continue to work with researchers and organisations across healthcare, public health, education, economics, finance, business, biological sciences, social sciences and public administration.
Depending on the problem, my work may involve conventional statistical analysis, predictive modelling, machine learning, forecasting or a complete data-driven solution. I begin by understanding the problem, the available data and what the researcher or organisation actually wants to achieve.
My consulting experience includes research design, advanced statistical analysis, predictive modelling, machine-learning applications, forecasting, econometric analysis and the development of data-based solutions that can move beyond experimentation into useful tools.
I work with Python, R, SAS, SPSS, EViews, KNIME and RapidMiner, among other analytical environments, and I am a SAS Certified Base Programmer. More important than the number of tools, however, is selecting an appropriate method, producing a trustworthy result and communicating it in a way that supports a better decision.
Research and collaboration
A stronger focus on health-related research
My research background is rooted in Statistics, particularly regression modelling and reliable estimation when conventional methods begin to struggle. Over time, multidisciplinary collaboration has also taken my work into several applied fields.
Collaborations with researchers in health sciences and public health have strengthened my interest in Medical Statistics, Biostatistics and Epidemiology. I am particularly interested in how modern statistical methods, machine learning and artificial intelligence can improve health research without losing interpretability and sound statistical reasoning.
I welcome multidisciplinary research where Statistics, Data Science or Machine Learning can contribute meaningfully to answering an important question.
Teaching and mentoring
Helping people become confident with data
I have taught students with very different backgrounds—from Statistics students learning the mathematics behind a method to researchers in Nursing, Public Health and Biological Sciences who need Statistics to answer an important research question. These experiences have taught me that the same concept cannot always be taught in the same way to everybody.
I also mentor emerging data professionals through Nigeria’s 3 Million Technical Talent (3MTT) initiative. My contribution to teaching and student development has received institutional recognition, including being honoured as a Lecturer of the Year.
Where it comes together
Practice, method, evidence and impact
I am a Statistician who has had the opportunity to experience the profession from both sides—practice and academia.
Industry taught me to look at the problem. Academia taught me to interrogate the method. Research taught me not to accept an answer simply because a model produced it. Consulting continues to remind me that, at the end of everything, somebody needs to use the result.
I continue to teach, conduct research, consult and collaborate on projects involving Statistics, Data Science, Machine Learning and Artificial Intelligence, while building a stronger focus around Biostatistics, Medical Statistics, Epidemiology and AI applications in healthcare.
I am open to research collaborations, consulting engagements, statistical advisory roles, data science and AI projects, training, multidisciplinary research and opportunities where my experience with data can contribute to solving a meaningful problem.
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