Welcome to my AI Engineering portfolio! This repository showcases my journey implementing various AI algorithms, with a special focus on search algorithms and their real-world applications. Each project demonstrates different aspects of AI engineering, from fundamental algorithms to practical implementations.
- Overview
- Tech Stack
- Projects
- AI Search Algorithms
- Getting Started
- Roadmap
- Contributing
- License
- Contact
This portfolio demonstrates my expertise in AI engineering through practical implementations of various algorithms and techniques. My projects focus on creating scalable, efficient, and production-ready AI solutions that solve real-world problems.
"The measure of intelligence is the ability to change." - Albert Einstein
Here are my featured AI Engineering projects:
Status: In Development
A simulation environment for testing various pathfinding algorithms in autonomous robots, implementing D* and LRTA* for dynamic obstacle avoidance.
Status: In Development
A recommendation system that uses stochastic gradient descent and genetic algorithms to continually optimize user recommendations based on interaction data.
Status: In Development
Implementation of simulated annealing to predict protein structures by efficiently searching through possible conformations in 3D space.
Status: In Development
A framework for training multiple AI agents that cooperate or compete using advanced POMDP modeling and belief state tracking.
Status: In Development
Object tracking system using online search algorithms and gradient-based optimization for real-time video processing.
Status: In Development
Implementation of genetic algorithms and Bayesian optimization for automatically discovering optimal neural network architectures.
Status: In Development
A logistics optimization tool using tabu search and local beam search for efficient vehicle routing with real-time traffic updates.
Status: In Development
A modular game AI system featuring expectimax and minimax with chance nodes for creating intelligent opponents in games with uncertainty.
Status: In Development
A diagnostic support system using belief state search and POMDPs to reason with incomplete patient information.
Status: In Development
Energy distribution optimization system using MDPs and evolutionary strategies to balance supply and demand in renewable energy networks.
This section showcases implementations of various AI search algorithms with practical applications. Each algorithm is implemented with clear documentation, performance analysis, and real-world use cases.
The repository includes implementations of:
- Hill Climbing (Robotics path optimization, ML feature selection)
- Stochastic Hill Climbing (Game AI movement, Recommendation systems)
- Simulated Annealing (VLSI design, Airline scheduling, Protein folding)
- Local Beam Search (Multi-robot coordination, Resource allocation)
- Genetic Algorithms (Financial trading strategies, Neural architecture search)
- Tabu Search (Network design, Hospital scheduling)
- Random Restart Hill Climbing (Neural network training, NLP optimization)
- Gradient Descent (Image classification, Language models)
- Stochastic Gradient Descent (Large language models, E-commerce recommendations)
- Momentum-based Gradient Descent (Deep neural networks, Speech synthesis)
- Adam Optimizer (Machine translation, GAN training)
- Evolution Strategies (Robot gait optimization, Game AI agents)
- CMA-ES (Bipedal robotics, Signal processing)
- Bayesian Optimization (Drug discovery, Chip design)
- Expectimax (Games with chance, Risk-aware finance)
- Markov Decision Process (Robot motion planning, Energy management)
- Partially Observable MDP (Limited-sensor navigation, Medical diagnosis)
- Minimax with Chance Nodes (Digital card games, Cybersecurity planning)
- Belief State Search (SLAM robotics, Disease prediction)
- Online DFS/BFS (Web crawling, Network security)
- Real-Time A* (Game NPC pathfinding, Mobile robot navigation)
- Learning Real-Time A* (Adaptive navigation, Self-improving game AI)
- D* Algorithm (Mars rovers, Urban navigation)
- T* Algorithm (Planetary exploration, Disaster response)
See detailed implementations in the search algorithms directory.
To explore this repository:
# Clone the repository
git clone https://github.com/yourusername/AI-Engineer.git
# Navigate to the project directory
cd AI-Engineer
# Set up a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Explore individual projects
cd projects/[project-name]- Q2 2025: Complete first 3 projects with comprehensive documentation
- Q3 2025: Implement cloud deployment options for select projects
- Q4 2025: Add benchmarking suite to compare algorithm performance
- Q1 2026: Develop educational tutorials based on project implementations
- Q2 2026: Integrate explainable AI components across projects
Contributions, issues, and feature requests are welcome! Feel free to check the issues page for open problems or suggest new features.
This project is licensed under the MIT License - see the LICENSE file for details.
Your Name - @yourtwitter - [email protected]
Project Link: https://github.com/yourusername/AI-Engineer
