Project Portfolio
Computational astrophysics, machine learning systems, and scientific simulations
Production-ready software demonstrating expertise in physics engines, distributed systems, neural networks, and interactive data visualization.
Featured Engineering Projects
Full-stack applications, scientific computing, and AI/ML systems built with modern technologies
Atomik - Celestial Calendar
Advanced astronomical calendar system integrating real-time celestial events, orbital mechanics, and time-based simulations for tracking cosmic phenomena.
Saturn's Rings Simulation
Interactive WebGL simulation of Saturn's ring system with accurate orbital mechanics, particle dynamics, and gravitational interactions. Demonstrates n-body physics in real-time.
Venus Atmospheric Simulation
Scientifically accurate model of Venus's extreme atmospheric conditions including greenhouse effects, pressure gradients, and temperature distribution across altitude layers.
Uranus Environment Simulation
Comprehensive simulation of Uranus's unique tilted rotation, extreme seasons, and atmospheric composition with scientifically referenced data and interactive controls.
S2 Star Orbital Analysis
Visualization and analysis of the S2 star's extreme elliptical orbit around Sagittarius A*, demonstrating general relativistic effects and orbital precession near a supermassive black hole.
Black Hole Gravitational Lensing
Real-time ray-traced simulation of gravitational lensing around a black hole, featuring accretion disk rendering, Schwarzschild geometry, and photon trajectory calculations.
MegaCrawler
High-performance distributed web crawler built for large-scale data extraction. Features async I/O, rate limiting, content parsing, and scalable architecture for AI training datasets.
Celestial Object & Star Simulator
Full-featured astrophysics simulation platform for modeling stellar evolution, planetary formation, and n-body gravitational systems with customizable parameters and scientific accuracy.
Tide Pool Ecosystem Simulator
Agent-based ecological simulation modeling predator-prey dynamics, resource competition, and environmental factors in tide pool ecosystems. Demonstrates emergent behavior and population dynamics.
Bot Detector PRO
Machine learning system for identifying automated bot behavior in web traffic. Uses statistical analysis, behavioral patterns, and neural networks for real-time bot detection and classification.
Neural Observatory Project
AI-powered astronomical observation platform combining computer vision, deep learning, and astrophysical data analysis for automated celestial object detection and classification.
Core Technical Competencies
Computational Physics
- ▸N-body gravitational simulations & orbital mechanics
- ▸General relativistic ray tracing & black hole physics
- ▸Atmospheric modeling & climate simulations
- ▸Real-time WebGL & GLSL shader programming
Machine Learning & AI
- ▸Neural networks for pattern recognition & classification
- ▸Computer vision for astronomical data analysis
- ▸Behavioral analysis & anomaly detection systems
- ▸Agent-based modeling & emergent behavior
Software Engineering
- ▸Full-stack TypeScript/React with Next.js
- ▸Distributed systems & scalable architecture
- ▸Async I/O, concurrency, & performance optimization
- ▸Production deployment & DevOps (Vercel, Docker)
Data Engineering
- ▸Large-scale data extraction & web crawling
- ▸Interactive data visualization (D3.js, Three.js)
- ▸Scientific computing with Python & numerical methods
- ▸Real-time data processing pipelines
Learn How These Projects Work
Dive deep into the technical implementation details, algorithms, and engineering decisions behind these projects on the blog.
Engineering Philosophy
These projects represent the intersection of rigorous scientific computing and production-quality software engineering. Each application is built with attention to performance, accuracy, and user experience.
From gravitational n-body simulations that solve differential equations in real-time to distributed web crawlers handling millions of requests, these systems demonstrate expertise in mathematical modeling, algorithmic optimization, and scalable architecture.
The machine learning projects showcase practical applications of neural networks and computer vision to real-world problems - from detecting bot behavior to classifying astronomical objects. These systems are designed for production deployment with considerations for latency, accuracy, and maintainability.
Interested in Collaboration?
These projects demonstrate capabilities in computational physics, machine learning, and full-stack engineering. Let's discuss how these skills can contribute to cutting-edge research and development.