Artificial Intelligence student interested in Machine Learning, Natural Language Processing, Generative AI, Computer Vision, and data-driven applications.
I enjoy building AI projects that connect theoretical understanding with practical implementation. My interests focus on developing intelligent systems that are not only functional, but also meaningful, interpretable, and grounded in strong analytical reasoning.
I am particularly interested in the mathematical foundations behind AI, including probability, statistics, optimization, modeling, and the way theoretical concepts can be transformed into practical machine learning solutions.
A deployed NLP application for English-Arabic news summarization using a hybrid pipeline that combines extractive and abstractive summarization techniques.
A deployed smart campus AI application for reporting and searching lost items using ResNet18 image classification, CLIP + FAISS retrieval, location filters, and fallback keyword search.
A deployed machine learning demo that predicts research paper acceptance probability using a leakage-safe two-stage pipeline, ensemble score prediction, calibrated classification, and uncertainty estimation.
A computer vision project focused on American Sign Language image classification using deep learning techniques.
Projects and practice work from the KAUST Bioinformatics Bootcamp, covering Python, R, Linux, and data analysis workflows.
- Machine Learning
- Natural Language Processing
- Generative AI
- Computer Vision
- Data Science
- Bioinformatics
- AI Applications
- Mathematical Modeling
- Probability, Statistics, and Optimization
- Theoretical Foundations of AI
- LinkedIn: https://www.linkedin.com/in/maramalhajri
- Hugging Face: https://huggingface.co/vmcii