Firas Khasawneh

Firas Khasawneh is an Associate Professor at the Department of Computational Mathematics, Science and Engineering at Michigan State University. His research focuses on the intersection of topological data analysis, dynamical systems, and machine learning with applications in engineering, neuroscience, and biological systems.

• Topological Data Analysis - Persistent Homology, Zigzag Persistence, Data Assimilation
• Dynamical Systems - Bifurcation Detection, Delay Differential Equations, Nonlinear Dynamics
• Machine Learning - Applications in Signal Processing, Surface Texture Analysis, Neural Activity Modeling

His recent publications (2023-2025) demonstrate a strong emphasis on topological approaches for analyzing complex systems, including neural activity characterization, vibration analysis in manufacturing processes, and data assimilation techniques for forecasting. He has developed computational tools like the Teaspoon Python package for topological signal processing.