Nuaima -

As a mentor at Akhuwat, I supported underprivileged students in preparing for university entrance exams by sharing effective test strategies, clarifying admission procedures, and providing motivation throughout the process. I guided them on selecting the right academic paths and helped them understand application requirements for top universities in Pakistan. Additionally, I assisted in organizing and conducting mock entry tests, helping students evaluate their readiness and improve their performance through structured feedback. This experience strengthened my leadership, communication, and mentoring abilities while contributing meaningfully to Akhuwat’s mission of providing equitable access to higher education. Akhuwat is a leading non-profit in Pakistan, dedicated to poverty alleviation and social empowerment through interest-free microfinance, education, and community development initiatives.

As a Computer Vision Intern at MCC, I focused on developing intelligent solutions for document and form processing. My work involved implementing algorithms for checkbox detection, edge detection, and form matching, enabling automated classification and alignment of forms with improved accuracy. This internship gave me practical experience in applying computer vision and image processing techniques to solve real-world challenges, while strengthening my skills in algorithm development, optimization, and problem-solving.

During my research internship, I focused on developing an AI-based hand gesture recognition system using deep learning and computer vision techniques. I worked extensively with a large custom dataset containing over 87,000 images across 29 gesture classes, including alphabets and control gestures such as space and delete. My responsibilities included data preprocessing, augmentation, and designing convolutional neural network (CNN) architectures optimized for accurate and efficient gesture classification. I leveraged Python, TensorFlow, and OpenCV to build and fine-tune models, ensuring high accuracy and robustness under varied conditions. The project culminated in authoring a peer-reviewed journal paper detailing the methodology, experiments, and significant findings. This experience deepened my expertise in AI, machine learning, and human-computer interaction, while honing my skills in research, data handling, and scientific communication.

During my internship at GIKI AI Lab, I developed NexGen Tutor, an AI-driven intelligent tutoring system designed to provide personalized, real-time learning support aligned with GIKI’s AI curriculum. The system combines semantic search with lightweight CNN-based natural language processing models to deliver precise and contextually relevant educational content. I leveraged Python, Flask, Scikit-learn, NLTK, and Pandas to design, build, and deploy this scalable solution. This experience strengthened my expertise in AI, NLP, and software development, and deepened my understanding of how AI can transform educational experiences.