Building intelligent systems through vision, data, & experimentation.
Information Technology student documenting applied work in artificial intelligence, machine learning, and computer vision. Projects focus on perception, decision-making, and system behavior under real-world constraints.
Authentic &
Project-Driven.
This portfolio documents experiments conducted within the Information Technology discipline, focused on artificial intelligence and applied machine learning.
Systems are built locally using Python, Scikit-learn, and OpenCV. Emphasis is placed on understanding model behavior, failure cases, and performance trade-offs rather than abstract accuracy metrics.
Particular attention is given to ethical boundaries and system responsibility, especially in monitoring and educational contexts.
Computer Vision
Surveillance, Gaze Tracking, & Object Detection.
Local ML
Scikit-learn implementation without external APIs.
Ethical AI
Responsible implementation of monitoring and surveillance systems.
Technical Stack
Tools and frameworks used in production.
Core & Data
Computer Vision
Frameworks
Selected Missions
Real-world systems deployed and tested.
Cheat GPT / Capstone
VisionA computer vision system designed to observe and flag behaviors associated with academic dishonesty. The pipeline processes video streams to detect eye-gaze direction, head movement, phone presence, and posture anomalies. Built using Python, OpenCV, and YOLO. Supports multi-person scenes and automated event logging. Ethical constraints are treated as design parameters, not afterthoughts.
QR Gate System
SecurityAn access control system using encrypted QR codes and real-time visual verification. Integrates OpenCV-based scanning with a Django backend for identity validation, access logging, and administrative control. Designed for controlled campus environments.
Student Path Predictor
Machine LearningA machine learning system that maps student interest signals to academic paths and skill domains. Implemented locally using Scikit-learn and Random Forest classification. Includes basic natural language processing and prioritizes explainability over automation.