Samuel Akingbade has a background in Applied Mathematics, with a strong interest in leveraging mathematical theory and data driven approaches to solve real world problems across disciplines. His research is motivated by the challenge of extracting meaningful insights from complex, high-dimensional datasets, and he is particularly passionate about developing innovative analytical tools that can be applied in both scientific and practical contexts.
His current research brings together Artificial Intelligence and advanced mathematical techniques, particularly Topological Data Analysis (TDA), to better understand complex patterns in behavioral datasets. By combining topological feature extraction with machine learning methods, he aims to improve the detection, classification, and prediction of behavioral states over time. This work has potential applications in understanding fundamental biological patterns, such as state transitions and circadian rhythms, which are relevant to both animal and human health.
While current research involves animal behavioral data, the methods under development are designed for generalizability and hold promise for transformative applications in health monitoring, behavioral analysis, and the early detection of abnormal patterns. Ultimately, these approaches could contribute to advancements in human-focused research, with the potential to impact healthcare, neuroscience, and personalized medicine.