JunJie Wee

JunJie Wee is a Visiting Assistant Professor in the Department of Mathematics at Michigan State University. His research focuses on integrating topological methods, machine learning, and computational biology to address challenges in molecular sciences, drug discovery, and virology. He specializes in developing models for protein-ligand binding affinity prediction, molecular similarity quantification, and pandemic response strategies using advanced mathematical frameworks.

Key research interests include topological data analysis, algebraic methods in machine learning, and the application of geometric and topological tools to biomolecular problems. His work spans from theoretical developments in functional analysis to practical applications in predicting drug resistance, viral evolution, and anticancer peptide design.

Recent publications highlight innovations in combining AlphaFold 3 with topological deep learning, curvature-based graph networks for biomolecular interactions, and persistent homology for RNA data analysis. These contributions underscore his interdisciplinary approach bridging mathematics, computer science, and life sciences.