Waris Rashid

Developed a Retrieval Augmented Generation (RAG) application for HR use cases, enabling recruiters to retrieve and query candidate information efficiently using natural language. Built AI workflows integrating LLMs, semantic search, and vector databases to improve the accuracy and relevance of candidate information retrieval.

Developed a deep learning-based medical image segmentation system for Hippocampal Sclerosis detection using brain MRI images. Implemented a U-Net architecture to accurately segment affected hippocampal regions and generate pixel-level diagnostic masks, achieving a Dice Coefficient of 80%.

Developed a machine learning based Wine Quality Prediction model using Logistic Regression for classification of wine quality. Performed data preprocessing, feature analysis, and model optimization to improve prediction performance, achieving 97% accuracy.

Activities and societies: Tech Lead at Google Developer Groups (GDG) NMIT; Vice President Education and Management at Toastmasters NMIT; Best Paper Award at COMPSIF 2026.

Pursuing a Bachelor of Engineering in Information Science, with expected graduation in June 2026.

Grade: 91%

Completed 12th grade with 91%.

Grade: 90%

Completed 10th grade with 90%.