Abubakar Yamin, PhD

Dr. Yamin’s research focuses mainly on the development of machine learning-based methods for the analysis of brain functional and structural connectivity. These methods address the problem of group detection/classification and neuro-biomarker identification in different neurological disorders. In his work, Dr. Yamin emphasizes the use of geodesic methods to analyze brain connectivity, which follows the geometrical structure of the Riemannian manifold of symmetric and positive definite matrices.

Specifically, he works on a method to classify different phenotypes of multiple sclerosis (MS) and identify the neuro-biomarkers that characterize these phenotypes. He successfully extended his method for analysis of prognosis (worsening/stabilizing) in MS diseases using functional neuroimaging data along with clinical information. His research on machine learning and pattern recognition methods is also linked with applications to various domains, like structured data analysis, computer vision, neuroimaging, and biomedical imaging.

Currently, Dr. Yamin is working on a positive cognition project to understand brain neural activity during character-strength intervention. The ultimate goals of his research are to help develop an advanced understanding of the effect of character strength and its usage on brain mental health. His research will facilitate and validate neuro-biomarkers that are sensitive to character-strength interventions and their usage in treatments.