Kayode Okunola is a Teaching Assistant in the Department of Economics at the University at Buffalo, State University of New York, where he is also pursuing a Ph.D. in Economics.

He holds a Bachelor of Technology (B.Tech.) in Statistics from the Federal University of Technology Akure (FUTA), NG, and a Master of Science (M.S.) in Mathematics from Georgia State University, Atlanta, Georgia.

His research interests lie at the intersection of Econometrics, Health Economics, Biostatistics, and Fair Machine Learning. He is particularly focused on developing and applying advanced statistical and machine learning methods to address real-world challenges in healthcare, public policy, and economic decision-making.

Kayode is proficient in a broad range of statistical and analytical software, including R, Python, SAS, Stata, SPSS, EViews, and SQL. His work centers on predictive modeling, data visualization, model selection techniques, fairness-aware machine learning, and evidence-based decision support, with contributions to research on grouped variable selection methods, healthcare analytics, and forecasting models.

Prior to Buffalo, he served as a Teaching and Laboratory Assistant at Georgia State University Commons Mile, supporting undergraduate and graduate instruction in statistics and data analysis. He is passionate about leveraging data-driven insights to improve outcomes across academic and professional settings, and continues to explore the intersection of economics, statistics, and data science through rigorous research and applied analytics.