I recently completed a Master's in Aerospace Engineering where my research is around AI-driven remote sensing, developing and employing classical computer vision (Threshold-Based & HSV) and lighter ML algorithms (EfficientDet) for cloud detection and classification, operable on a Raspberry Pi Module 4B for CubeSats (cube satellites) deployment. I also hold a Master's in Computer and Information Science, during which I trained a Deep Convolutional Generative Adversarial Network (DCGAN) to generate synthetic images and trained on these a Convolutional Neural Network (CNN) for automatic cloud detection and classification. These qualifications deepened my expertise in Python, TensorFlow, and Machine Learning techniques.
Beyond my academic experience, I have hands-on experience in AI-powered analytics and predictive modelling, and have a considerable foundation in software development, problem solving and collaborative teamwork to support AI implementation.
