Projects
Quantum Computing Simulation
A simulation of quantum computing algorithms using Python and Qiskit, demonstrating quantum supremacy in specific computational tasks.
Project Details
This project explores the fascinating world of quantum computing through simulation. Using Python and IBM's Qiskit framework, I developed a comprehensive simulation environment that allows users to experiment with various quantum algorithms including Shor's algorithm, Grover's search, and quantum teleportation. The simulation demonstrates quantum supremacy by solving specific computational problems exponentially faster than classical algorithms. I implemented a user-friendly interface that visualizes quantum states and circuit operations, making quantum computing concepts more accessible to students and researchers. The project includes detailed documentation and tutorials on quantum computing principles, making it a valuable educational resource. Performance benchmarks show our quantum simulations achieving significant speedups on certain problems compared to classical approaches.
Technologies Used
Challenges
The main challenge was accurately simulating quantum decoherence and error correction techniques that would be present in real quantum hardware.
Outcomes
Created an educational tool now used in undergraduate physics courses at UIUC. Published findings in a student research journal.
Physics Visualization Tool
An interactive web application for visualizing complex physics concepts, built with Three.js and React.
Project Details
The Physics Visualization Tool is an interactive web application designed to make complex physics concepts more intuitive through 3D visualizations. Using Three.js for rendering and React for the UI, this tool allows users to manipulate parameters and observe real-time changes in physical simulations. The application covers various physics domains including classical mechanics, electromagnetism, fluid dynamics, and basic quantum mechanics. Each simulation is accompanied by explanatory text and equations, creating a comprehensive learning experience. The tool features responsive design for use on different devices and includes accessibility considerations to ensure physics education is available to all students. User testing with physics students showed significant improvements in conceptual understanding after using the tool.
Technologies Used
Challenges
Balancing visual fidelity with performance across different devices while maintaining physical accuracy was particularly challenging.
Outcomes
The tool has been adopted by several physics professors for their courses and has received positive feedback for making abstract concepts more tangible.
Machine Learning for Particle Physics
Applied machine learning techniques to analyze particle physics data from CERN, improving classification accuracy by 15%.
Project Details
This project applies cutting-edge machine learning techniques to the analysis of particle physics data from CERN's Large Hadron Collider. I developed a suite of deep learning models that can identify and classify subatomic particles from detector data with unprecedented accuracy. The system uses convolutional neural networks and transformer architectures to process the complex, high-dimensional data generated by particle collisions. By incorporating physics-informed constraints into the neural network architecture, the models achieve both high accuracy and consistency with known physical laws. The project improved classification accuracy by 15% compared to traditional statistical methods, potentially enabling the detection of rare physics events that might otherwise be missed. The models were trained on simulated data and validated against real experimental data, demonstrating robust generalization capabilities.
Technologies Used
Challenges
Working with the extremely large datasets from CERN required implementing distributed training across multiple GPUs and optimizing for memory efficiency.
Outcomes
Published a paper on the approach in a student journal and presented findings at an undergraduate research symposium.
Algorithmic Trading System
Developed a backtesting framework for algorithmic trading strategies using historical market data.
Project Details
The Algorithmic Trading System is a comprehensive framework for developing, testing, and optimizing trading strategies using historical market data. Built with Python, the system incorporates financial analysis libraries and machine learning to identify profitable trading opportunities. The backtesting engine simulates trading with historical data, accounting for factors like transaction costs, slippage, and market impact. It provides detailed performance metrics including Sharpe ratio, maximum drawdown, and risk-adjusted returns. The system includes several pre-built strategies based on technical indicators, statistical arbitrage, and machine learning predictions. Users can easily implement and test their own custom strategies through a flexible API. Risk management features include position sizing algorithms, stop-loss mechanisms, and portfolio diversification tools to protect against market volatility.
Technologies Used
Challenges
Ensuring the backtesting environment accurately reflected real-world trading conditions while maintaining computational efficiency was a significant challenge.
Outcomes
The system demonstrated consistent profitability in backtests across different market conditions and timeframes.
AR Physics Education App
Created an augmented reality application for physics education, allowing students to interact with virtual physics experiments.
Project Details
The AR Physics Education App transforms how students learn physics by bringing virtual experiments into the real world through augmented reality. Developed using Unity and ARKit/ARCore, this mobile application allows students to conduct physics experiments that would be impossible, dangerous, or too expensive in a traditional classroom setting. Users can manipulate gravity, create electromagnetic fields, observe quantum phenomena, and explore relativistic effects through intuitive touch controls. The app overlays informative visualizations on the camera feed, showing force vectors, field lines, and particle trajectories. Each experiment is accompanied by interactive lessons that explain the underlying physics principles and mathematical formulations. The app tracks student progress and adapts content difficulty based on performance, creating a personalized learning experience. Collaboration features allow students to share their experimental setups and results with classmates and teachers, facilitating group learning and discussion.
Technologies Used
Challenges
Creating physically accurate simulations that run in real-time on mobile devices while maintaining AR tracking stability was technically demanding.
Outcomes
The app has been downloaded by over 500 students and received an award for innovation in educational technology at a university showcase.
Computational Fluid Dynamics
Implemented numerical methods for solving fluid dynamics equations, with visualizations of flow patterns.
Project Details
This Computational Fluid Dynamics (CFD) project implements advanced numerical methods to solve the Navier-Stokes equations governing fluid flow. Using C++ for performance-critical computations and Python for visualization, the system can simulate complex fluid behaviors in both 2D and 3D environments. The implementation includes finite difference, finite volume, and spectral methods with various boundary conditions to accommodate different simulation scenarios. Parallel computing techniques using OpenMP and CUDA accelerate the computations, enabling high-resolution simulations in reasonable timeframes. The visualization component renders fluid flow patterns with streamlines, vector fields, and color-mapped pressure distributions. Time-evolution animations help users understand the dynamic nature of fluid systems. Applications of the system include aerodynamics studies, weather pattern simulation, and microfluidics design. The modular architecture allows researchers to easily implement and test new numerical schemes and physical models.
Technologies Used
Challenges
Ensuring numerical stability while maintaining accuracy across different Reynolds numbers and boundary conditions required implementing adaptive time-stepping and mesh refinement.
Outcomes
Successfully simulated complex flow phenomena including turbulence, vortex shedding, and boundary layer separation with results matching experimental data.