I specialize in advanced computer vision and the development of lightweight deep learning models for Edge AI–based medical diagnosis and industrial inspection. My research focuses on designing efficient neural network architectures optimized for resource-constrained edge devices, including NVIDIA Jetson Nano and Raspberry Pi.

My expertise encompasses medical image analysis and machine vision, including image processing, object detection, image classification, and segmentation for real-time inference. I work on AI-powered inspection systems for applications such as printed circuit board (PCB) defect detection and automated medical image diagnosis. My research also explores model optimization, efficient deep learning architectures, and Explainable Artificial Intelligence (XAI) techniques to improve model interpretability, transparency, and reliability.

My teaching and research interests include Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Edge AI, and medical image analysis. I am particularly interested in bridging the gap between theoretical AI research and practical deployment by developing computationally efficient, reliable, and real-time intelligent systems for healthcare, Agriculture and industrial applications.