My portfolio
Throughout the years, I have consistently combined my aerospace engineering expertise with software development, creating a portfolio that encompasses a diverse range of complex and innovative projects. My focus has been on developing efficient numerical simulations and high-performance software that address the needs of modern research, engineering, and technology. In this blog post, I will present some highlights from my portfolio, showcasing key projects that reflect my expertise in mathematics, physics, and computational modeling.
Personal website
The cornerstone of my online presence is my personal website. This custom-built, responsive website not only serves as my professional hub but also demonstrates my skills in web development. I leveraged Bootstrap to ensure a responsive design that delivers an optimal user experience on both desktop and mobile devices. Additionally, I developed custom JavaScript libraries to enhance functionality, such as syntax highlighting and dynamic content generation.
One of the primary challenges I encountered in building my website was ensuring that it adhered to the high standards of coding and performance that I maintain in all my projects. To address this, I implemented custom scripts for code minification, linting, and deployment automation. The result is a streamlined and efficient website that reflects the same level of attention to detail I apply to all of my work.
MA Libs
One of the most significant tools in my portfolio is the MA Libs, a versatile set of libraries in C++ and Fortran. These libraries have been developed to enhance functionality and enable seamless interoperability between C++, Fortran, and Python. My goal was to create a library that would not only solve specific computational problems but also integrate smoothly with other languages, allowing me to extend its use across multiple projects.
MA Libs includes bindings for BLAS/LAPACK, which are critical for high-performance numerical computations, as well as HDF5 utilities for reliable data serialization. The library also includes an OpenGL interface for visualizing numerical results in both 2D and 3D, which is essential for interpreting and presenting complex data sets.
Quantum mechanics spin simulation
Quantum mechanics has always been an area of particular interest to me, and this is reflected in my quantum mechanics spin simulation project. Using Python, I developed an interactive simulation that models single and two-spin systems, as well as entangled states. This project also includes a simulation of the EPR paradox, allowing me to explore the non-classical correlations predicted by quantum mechanics.
The simulation includes real-time visualizations that allow for a clear and intuitive understanding of quantum spin dynamics. It has been an excellent tool for illustrating the fundamental principles of quantum mechanics to students and researchers alike.
Schrödinger equation simulation
Another major project in my portfolio is the numerical simulation of the Schrödinger equation in both one and two dimensions. I developed this simulation to model the evolution of wavefunctions under various potential types, including free space, harmonic oscillators, and potential barriers. The simulation also includes a detailed model of the double-slit experiment, one of the most iconic demonstrations of quantum interference.
I utilized Crank-Nicolson and Runge-Kutta methods to achieve high accuracy in time evolution. The results of these simulations can be visualized in real-time, providing insights into the behavior of quantum wavefunctions under different physical scenarios.
Orbits simulation
In the field of celestial mechanics, I created an orbits simulation that models the orbital dynamics of planets around the Sun. This project uses real-world data and physics-based models to simulate accurate planetary orbits. The simulation is fully customizable, allowing users to modify initial conditions and physical parameters to study various orbital behaviors.
Visualization plays a key role in this project, as the orbital dynamics are graphically represented to aid in the interpretation of the results. This project reflects my commitment to precision and clarity in both modeling and presentation.
MNIST Classification
As part of my exploration of neural networks and machine learning, I developed a project focused on image classification using the MNIST dataset. This project leverages my custom neural network library, which is designed for high performance and efficient optimization. I implemented two approaches for training the neural network: Stochastic Gradient Descent (SGD) and a Genetic Algorithm. Each approach allows for a unique exploration of neural network behavior and optimization.
This project showcases my ability to apply advanced algorithms to solve real-world problems, such as image classification, and reflects my ongoing interest in machine learning.
Conclusion
These projects represent a sample of my portfolio. My work spans diverse domains, from quantum mechanics to machine learning, always with a focus on solving complex problems efficiently.
For more details, you can find my portfolio here.