Arsal K.

I am a Mathematical Physicist and Computational Specialist focused on bridging bleeding-edge theory with high-performance programming and verifiable simulation. ​With a foundation in Physics (B.S.), minors in Mathematics and Computer Science (AI focus), and a current M.S. in Mathematical Physics, I am adept at tackling the most challenging, interdisciplinary projects. My expertise lies in designing, implementing, and optimizing computational solutions for physics, mathematics and data science projects. ​My work is anchored in three high-demand areas, backed by demonstrated success in research and analytical projects: ​1. Quantum Systems & Information Theory: ​I offer expertise in the numerical modeling and simulation of complex quantum protocols, focusing on robustness and verification. ​Developing high-fidelity numerical models for complex quantum systems. ​Platforms & Tools: Experienced with quantum frameworks like Qiskit and Cirq for building and simulating discrete quantum circuits, as well as developing custom tools using Python/SciPy for specialized problems. ​2. Computational Physics & Advanced Modeling: ​I specialize in building physics-based simulations, performing rigorous mathematical modeling, and optimizing algorithms for performance: ​Physics Simulation: Building robust, custom simulators for problems in classical and quantum mechanics, electrodynamics, and statistical physics. I use C++ for performance-critical backends and Python/NumPy/SciPy for rapid prototyping and analysis. ​Numerical Methods: Expert application of advanced numerical techniques (e.g., finite difference, Runge-Kutta) to solve complex Differential Equations (ODEs/PDEs) in physical systems. ​Visual Communication: Skilled in creating dynamic, high-quality visual math animations and physics simulations using libraries like Manim and Plotly. This is crucial for clarifying abstract concepts, enhancing educational materials, or presenting complex research findings. ​3. Data Science and ML: ​I leverage my physics background to approach data science problems with analytical depth, particularly in handling challenging, noisy datasets: ​Scientific Data Analysis: Expertise in processing and analyzing complex, high-dimensional data, specifically proven in the domain of Astrophysics, CMP and Quantum Mechanics. ​Time-Series Classification: Designing, training, and optimizing ML classifiers (Keras/scikit-learn) for time-series tasks, achieving verified high accuracy. ​Feature Engineering: Advanced skills in data preprocessing, signal analysis (e.g., Fast Fourier Transform - FFT), and feature extraction. A few Example projects: Ray Tracer in C++: Wrote an advanced ray tracer program which use real world physics to create images. It has primary and secondary light sources, use ray and even path tracing. Core Skills: Advanced programming in C++, understanding of computer graphics algorithms (ray tracing), and computational efficiency. 🌌 Dark Matter Simulations (Python, Julia): Project Goal: Reviewed the missing mass problem in spiral galaxies and conducted simulations to compare Dark Matter and MOND (Modified Newtonian Dynamics) as potential solutions. Carried out basic simulations testing the two hypotheses using Python and Julia. High-Energy Physics Minimization Routine: Developed a program to minimize a multidimensional integral function for a High-Energy Physics (HEP) project. Wrote a wrapper class in C++ to carry out calculations using a function defined in FORTRAN. The final code used a FORTRAN function with minimization done in Python, utilizing MPI (Message Passing Interface) for execution on a computing cluster. Core Skills: High-performance computing (MPI), numerical analysis, and multi-language integration (FORTRAN/C++/Python).