Specializing in Machine Learning, NLP, and LLM-based architectures. I transform raw data into intelligent, deployable solutions that solve real-world problems.
I'm an Artificial Intelligence graduate focused on building practical AI systems that bridge research and real-world applications. My work spans machine learning, NLP, and LLM-based solutions, with hands-on experience in predictive modeling and Retrieval-Augmented Generation (RAG) pipelines.
Through academic projects and industry internships, I've developed end-to-end ML workflows — from data preprocessing and feature engineering to model training, evaluation, and deployment using Python and modern AI tools.
I care about engineering clarity as much as model performance. Effective ML isn't just about accuracy — it's about clean implementation, reproducibility, and systems that deliver measurable value.
An AI-driven adaptive learning platform for HSSC Mathematics, personalizing content using student performance and mental-state data with predictive models and an AI chatbot.
An intelligent chatbot for international scholarship guidance. Helps students identify required documents, track completion, and discover scholarships using an LLM-powered RAG system.
Built a deep learning web app for brain tumor detection using MobileNetV2 (95.7% accuracy) with Grad-CAM explainability. Deployed on Hugging Face Spaces with Flask, PyTorch, user auth, and PDF reports.
A real-time object detection and tracking app using YOLOv8 with the SORT algorithm, detecting objects in images, webcam, or video feeds with consistent unique IDs across frames.
I'm actively looking to grow in AI/ML and contribute to exciting projects. If you're building something interesting, let's talk.