Data-Driven Modeling of Population Dynamics

Professor: Siting Liu

Description: The student will simulate simple population models (logistic growth, predator–prey) and learn to fit data-driven models (e.g., polynomial regression or small neural nets) that predict future behavior. They will explore how different parameter ranges affect stability and accuracy. Learning outcomes: Differential equations, data fitting, dynamical systems visualization.

Preferred Qualifications: Preferred skills: Python or MATLAB; basic calculus/ODEs.