Justin Morales

Committed Aerospace Engineering student with proficient problem-solving skills, seeking an internship to become an asset for future autonomous vehicle system projects. interested in furthering my work experience and skills to lay a ground work for improvement in overseeing projects, optimizing system dynamics and control, propulsion technology, and coding.

Statistical Analysis of Flow-Induced Noise on a NACA 0012 Airfoil
Applied Experimental statistics Project | Python & MATLAB Fall 2025

Led a 3-member research team in conducting a statistical investigation of aeroacoustic performance using the NASA Airfoil Self-Noise Dataset, with direct relevance to airframe noise reduction in aerospace applications.

• Applied engineering-focused statistical methods—including linear regression, ANOVA, F- and chi-squared tests, and hypothesis testing—to quantify the influence of Reynolds number, angle of attack, and flow velocity on sound pressure levels.
• Integrated Python for automated data processing and MATLAB for advanced visualization and statistical modeling, enhancing analysis accuracy and efficiency.
• Demonstrated technical leadership by managing workflows, synthesizing findings for an engineering audience, and guiding the team through structured experimentation and analysis.

SIR Disease Spread Modeling | MATLAB | Advanced Mathematical Methods for Engineers
Collaborative project (3-member team) | Fall 2024

Developed a comprehensive numerical simulation framework to model the spread of infectious diseases using the Susceptible-Infected-Recovered (SIR) model, integrating multiple numerical analysis techniques in MATLAB without reliance on built-in solvers. Responsibilities and contributions included:

• Numerical ODE Solvers: Designed and implemented a custom fourth-order Runge-Kutta algorithm to solve the nonlinear SIR system for varying epidemiological parameters (β, γ) representing different diseases (Seasonal Influenza, COVID-19, Measles).
• Data Interpolation and Accuracy Assessment: Reconstructed missing time-series data using both linear and quadratic interpolation (Newton and Lagrange forms). Quantified model accuracy via L² norm error analysis and compared interpolation fidelity.
• Parameter Estimation via Least Squares: Linearized the SIR model under constant susceptibility assumptions and applied least squares regression to estimate infection rate and initial conditions from synthetic data, evaluating model sensitivity over 10-day and 30-day windows.
• Spectral Analysis using FFT: Investigated the effect of time-varying transmission rates by introducing sinusoidal modulation to β(t). Performed frequency-domain analysis using FFT to identify dominant infection cycles and interpret physical relevance of spectral peaks.
• Version Control and Team Collaboration: Coordinated development through GitHub, with clear task division, code integration, and commit-based contribution tracking.

• Fluid Mechanics
• Thermodynamics
• Computer Aided-Design
• Mechanics of Materials
• Applied Experimental Statistics
• Modern Differential Equations