A comprehensive reimagining of the college football playoff ranking system. This project provides a more accurate and transparent ranking methodology, ensuring fairer outcomes for teams. Leveraging advanced analytics and custom algorithms, it delivers improved insights compared to traditional approaches.

• CFP_Multiprocessing Implements parallel computing concepts to enhance the efficiency of ranking calculations. Uses multiprocessing for tasks like team scoring and schedule strength evaluations. Optimized for large datasets to ensure faster execution without compromising accuracy.
• CFP_Playoffs Focuses on conference champions and reseeding for the top 12 teams. Automatically grants auto-bids to conference champions while maintaining competitive fairness. Simulates the playoff structure, including first-round matchups and byes. Provides a transparent view of playoff seeding, matchups, and results.
• CFP_Rankings Produces top 50 rankings based on custom algorithms. Combines record points, offense and defense performance, strength of schedule (SoS), and other metrics. Designed to ensure accurate and transparent evaluations of team performances. Additional Files rules.txt Description: A detailed text file outlining the rules and criteria used in the ranking and playoff systems. Contents: Conference champion auto-bid requirements. Scoring breakdown: Record, SoS, offense, defense, and bonus points. Tie-breaking criteria for rankings and playoff seeding. Detailed playoff structure and matchup rules.

A comprehensive system demonstrating best practices in data encryption, key management, and PII protection. This project is organized into Asymmetric Encryption (RSA) for secure key exchange and Symmetric Encryption (Fernet) for data confidentiality. It includes a mock KeyVault for key management and a mock database for encrypted PII storage.

• Asymmetric Encryption (RSA): Securely encrypts and exchanges keys or small amounts of data.
• Symmetric Encryption (Fernet): Encrypts larger datasets for storage or transmission.
• Key Management: Simulated KeyVault ensures secure and isolated key handling.
• Mock Database: Stores encrypted sensitive data, ensuring privacy and security.

A secure system for transmitting, encrypting, and storing Social Security Numbers (SSNs) to prevent data loss. This project uses TLS/SSL for secure transmission and AES encryption for robust storage in a SQLite database.

• Secure Transmission: TLS/SSL ensures encrypted communication using self-signed certificates.
• AES Encryption: Encrypts sensitive data before storage in a secure database.
• Random SSN Generation: Creates fake SSNs for testing workflows without handling real data.
• DLP Practices: Protects sensitive data at rest and in transit, preventing unauthorized access.

I led a significant upgrade of the FIZ Makerspace in Dana Hall, transforming it into a state-of-the-art facility with Industry 4.0 compatibility. Key accomplishments include:

• RFID Authentication: Integrated RFID-based security to ensure only trained users can access machinery, enhancing safety and accountability.
• Data Logging: Implemented a robust data-logging system to monitor machine usage, enabling proactive maintenance and maximizing operational efficiency.
• Web Application & Database: Developed a comprehensive web application and database system to automate safety protocols and manage user and machine data seamlessly.

This upgrade not only improved operational workflows but also aligned the Makerspace with the latest technological standards.

An innovative app designed to assist individuals with speech impediments and public speaking fears. Leveraging Microsoft AI, this app provides:

• Real-time feedback on speech clarity and delivery.
• Customizable training modules tailored to the user's unique needs.
• Tools to help users build confidence and improve their communication skills.

This app aims to empower users by breaking barriers and fostering effective communication.

Oreo Computer Vision is a real-time object detection project that uses a custom-trained YOLOv5 model and OpenCV. The application processes live video from a camera, identifies objects, and focuses on detecting a specific class, such as an Oreo Container.

The project demonstrates:

• The integration of computer vision techniques with live video feeds.
• The use of a pre-trained YOLOv5 model fine-tuned for custom object detection.
• Real-time performance with automated responses to specific object detections.

This project is ideal for showcasing the potential of computer vision in solving specific real-world tasks like product recognition or targeted object detection.