Matthew Morena

Matthew Morena

Associate Professor of Mathematics at Christopher Newport University

Skills and Experience

Research Expertise

Nonlinear and chaotic dynamical systems, periodic orbit theory, cupolets and mutual stabilization in coupled systems, computational neuroscience, and applications to quantum mechanics and biological systems.

Technical Skills

Programming and Data: Python (pandas, NumPy, scikit-learn, Matplotlib, seaborn), MATLAB, Mathematica, Git/GitHub

Scientific Computing: Jupyter notebooks, LaTeX, numerical simulation, data visualization, statistical analysis

Program Design and Curriculum

Directed the design and development of CNU’s Data Science major and the Actuarial Science certificate program (pending SCHEV approval), both cross-disciplinary initiatives spanning mathematics, computer science, and statistics. Designed and taught HONR 310: Fractals and Infinity, an honors seminar bridging computational exploration and classical fractal geometry. Led a departmental overhaul of the differential equations course sequence to modernize content and pedagogy. Currently teaching MATH 447: Advanced Differential Equations with a unit on machine learning prediction applied to chaotic systems, demonstrating how long-term behavior in nonlinear dynamics connects to contemporary data-driven methods.

Community Engagement

Design and teach MATH 395: BIG (Business, Industry, and Government) Mathematics, a community-engaged learning course in which student teams partner reciprocally with local nonprofits and organizations on semester-long initiatives. Currently collaborating with Lynnhaven River NOW on water quality data analysis and mentoring undergraduate researchers through the collaboration. Developing fractals-and-patterns-in-nature outreach program with the Virginia Living Museum for K-12 and general audiences. These initiatives demonstrate the ability to translate research expertise into community impact, manage long-term partnerships with external organizations, and mentor students in meaningful, real-world contexts.

Reproducible Workflows

Maintain research and instructional materials in version-controlled repositories with clear documentation. Practice literate programming through Jupyter notebooks and self-contained labs that prioritize clarity and reusability for both pedagogical and research applications.