I am an engineering student with a strong interest in embedded systems, machine learning, and edge AI applications. My work focuses on building practical, resource-efficient solutions that can operate reliably in real-world environments.

I am particularly interested in deploying machine learning models on constrained hardware, optimizing models for performance and memory efficiency, and designing systems that function without cloud dependency. My projects emphasize affordability, real-time operation, and field readiness rather than purely theoretical results.

This portfolio highlights my work on a low-cost tomato ripeness detection system using ESP32-CAM and TensorFlow Lite, where I handled dataset preparation, model optimization, embedded deployment, and performance validation. The project reflects my interest in applying engineering and AI to sustainable agriculture and socially impactful problems.