Samuel Akingbade is an applied mathematician whose fellowship research focuses on developing and applying mathematical and computational methods for understanding complex biological dynamics. His work combines Topological Data Analysis and machine learning to extract interpretable structure from high-dimensional time-dependent data.
Within the fellowship, his research focuses on neural dynamics, particularly the organization of gamma-band neural oscillations. He studies how mathematical representations of neural time series can be used to identify differences in the underlying neural mechanisms that generate apparently similar oscillatory activity. By combining delay coordinate representations, topological features, and machine learning, this work connects mechanistic models of neural activity with experimental electrophysiological recordings.
A central goal of this research is to develop methods that can identify dynamical structure that may not be apparent from conventional signal characteristics alone. More broadly, his work explores how geometry and topology can provide interpretable tools for studying complex temporal organization in neuroscience and other biological systems.