I'm Zahra Gharehmahmoodlee, a Computer Science graduate from Amirkabir University of Technology with research experience at the intersection of machine learning, large language models, and biomedical data science. My work focuses on designing robust and interpretable AI systems, particularly through transformer-based architectures, retrieval-augmented generation (RAG), and reproducible end-to-end ML pipelines.

I have developed adaptive LLM-driven summarization systems, fine-tuned transformer models, and implemented scalable retrieval pipelines for multilingual data. In parallel, I have worked on biomedical and neuroimaging projects, including fMRI-based classification pipelines and interpretable CNN models for medical imaging. These experiences strengthened my interest in Bioinformatics and AI in Health, where large-scale biological data, representation learning, and intelligent modeling intersect.

My research goal is to build reliable, interpretable, and data-efficient learning systems capable of extracting meaningful patterns from complex biological and clinical datasets. I am particularly motivated by applications in precision medicine, computational biology, and multimodal health data integration, where strong machine learning foundations can directly contribute to scientific discovery and healthcare impact.