Featured Projects
Quantum Chess
Quantum Chess is a Python-based chess engine and GUI that integrates quantum mechanics principles into traditional chess. The game allows players to perform quantum moves such as superposition and entanglement, making for a unique and educational chess experience.
Key Features:
- Classical chess gameplay with all standard rules
- Quantum moves: superposition, entanglement, and measurement
- PyQt5-based graphical user interface
- Asynchronous quantum simulation using Qiskit
- Move validation and basic game loop
Technologies: Python 3.9+, PyQt5, Qiskit
Quantum Nash Equilibrium Solver (WIP)
A quantum computing simulation environment for exploring strategic optimization in nonlocal games. This research project investigates how quantum entanglement and superposition can provide strategic advantages in competitive scenarios, particularly focusing on CHSH games.
Research Applications:
- Simulation of quantum strategies in Bell games and CHSH scenarios
- Analysis of quantum vs. classical strategic advantages
- Optimization of quantum circuit parameters for maximum strategic benefit
- Investigation of noise effects on quantum strategic performance
Technologies: Qiskit, Python, NumPy, Matplotlib, Jupyter Notebooks
Dynamic Tackler Identification System
An advanced machine learning system designed to predict the most likely tackler in NFL plays using real-time player positioning data. The system employs convolutional neural networks to process complex spatial-temporal relationships and provide tactical insights for coaching staff and analysts.
Key Features:
- Real-time processing of multi-dimensional player tracking data
- CNN architecture optimized for spatial pattern recognition
- Integration with NFL play-by-play data for comprehensive analysis
- Interactive visualization of prediction confidence and contributing factors
Technologies: TensorFlow, Python, Selenium, Pandas, NumPy, Matplotlib
Spectroscopy Data Analysis Suite
A comprehensive Python-based analysis platform for materials characterization using photoluminescence, absorption, and Raman spectroscopy data. The suite includes automated data processing, statistical analysis, and interactive visualization tools.
Features:
- Automated peak detection and fitting algorithms
- Statistical analysis and uncertainty quantification
- Interactive dashboards for real-time data exploration
- Integration with LabVIEW acquisition systems
- Batch processing capabilities for large datasets
Impact: 80% improvement in data collection and analysis efficiency, enabling higher throughput materials research.
Technologies: Python, LabVIEW, Pandas, SciPy, Plotly, Dash
Poster: View project poster