Research Overview
Research focuses on graph theory, particularly algebraic and spectral graph theory, with emphasis on weighted and structured graphs. Current work develops graph-based mathematical models and spectral techniques for analyzing complex systems, including applications in geoscience and physical modeling. This includes the use of eigenvalues, Laplacian operators, and graph invariants to extract meaningful information from real-world data. In addition, research explores graph-based machine learning and chemical graph theory, where graph representations are integrated with statistical and learning models to predict properties of complex systems such as materials and energy-related structures.
Algebraic Graph Theory
Spectral Graph Theory (Eigenvalues & Laplacian Methods)
Weighted and Structured Graphs
Strongly Regular and Signed Graphs
Chemical Graph Theory
Graph-Based Machine Learning
Graph Modeling in Geoscience and Physical Systems