Hongsong Feng

Hongsong Feng is a Visiting Assistant Professor in the Department of Mathematics at Michigan State University (MSU), located in C309 Wells Hall. His research focuses on interdisciplinary applications of mathematics and machine learning to problems in computational biology, drug discovery, and topological data analysis. He specializes in combining advanced mathematical frameworks like persistent homology, algebraic topology, and differential geometry with machine learning techniques to analyze complex biological systems and molecular interactions.

Key research interests include protein flexibility analysis, drug-target interaction networks, and the development of AI-driven methods for predicting protein-ligand binding affinities. His work often bridges theoretical mathematics with practical biomedical challenges, such as opioid use disorder treatment and cocaine addiction drug development. Recent projects involve leveraging transformers, knot theory, and generative network models to advance drug discovery and systems pharmacology.

Notable contributions include methodologies like Persistent Sheaf Laplacian analysis, CAML (Commutative Algebra Machine Learning), and TIDAL (Topology-Inferred Drug Addiction Learning). These approaches integrate multiscale geometric learning and topological signal processing to model molecular dynamics and network complexities. His research also extends to numerical methods for solving elliptic interface problems and parabolic partial differential equations, reflecting a strong foundation in applied mathematics.

Despite his extensive publication record, no specific academic awards or grants are explicitly listed in the provided materials. No formal advisees are mentioned, suggesting he may be early in his academic career or focused primarily on research collaborations.