Xiang Liu

Xiang Liu is a Visiting Assistant Professor in the Department of Mathematics at Michigan State University, located in Wells Hall. His research focuses on integrating topological data analysis with machine learning to address challenges in molecular modeling, drug design, and complex systems analysis. He specializes in applying advanced mathematical frameworks like persistent homology, hypergraph theory, and sheaf models to study biological systems, protein interactions, and climate-related ecological networks.

Key research interests include topological approaches for predicting protein-ligand binding affinities, modeling high-order interactions in complex systems, and analyzing networks in aging and microbiota dynamics. His work bridges algebraic topology, computational biology, and physics-informed modeling to develop novel algorithms for drug discovery and systems biology.

Xiang’s recent publications highlight themes such as torsion graph neural networks for molecular property prediction, hypernetwork modeling for complex systems, and topological analysis of soil microbiota under global warming. His methods combine multiscale topological indices with machine learning to tackle problems in virology, metabolomics, and environmental science.

No scientific awards or grants are explicitly listed in the provided information. He currently advises no students, though his research team likely collaborates on interdisciplinary projects within the Department of Mathematics and affiliated institutions.