ML Engineer & Researcher
Building Intelligent Systems · Advancing AI Research
A driven Computer Science student at University of Education, Lahore — engineering machine learning systems at the intersection of deep learning, computer vision, and cybersecurity research.
I am a 6th semester BS Computer Science student at the University of Education, Lahore — Vehari Campus, with a strong academic record and a clear, ambitious trajectory toward becoming a Machine Learning Engineer.
My journey spans classical ML, deep learning, and computer vision — with hands-on projects already built and more in active development. But my vision does not stop there. I am steadily advancing toward the frontier of Agentic AI — building systems that don't just predict, but reason, plan, and act autonomously.
Beyond coursework, I am actively conducting research in cybersecurity — applying adaptive machine learning to detect insider behavioral threats under real-world conditions of concept drift. I believe intelligence, when engineered responsibly, makes both systems and societies safer.
An advanced deep learning system using convolutional neural networks to detect bone fractures from radiographic X-ray images. Trained on medical imaging datasets to assist diagnostic workflows — pushing the boundaries of AI in healthcare.
Real-time face mask detection using deep learning, identifying masked and unmasked individuals from live video streams and static images.
ML-powered model for detecting anomalous network traffic and classifying intrusion types — supporting proactive cybersecurity threat response.
Intelligent email classification using NLP to categorize emails with high accuracy, filtering spam and organizing communication pipelines.
Automated news article classification system categorizing content into relevant topics using machine learning and text feature engineering.
This research investigates how machine learning models can be made adaptive and incrementally updatable to detect insider behavioral threats in dynamic environments — particularly under concept drift, where the statistical properties of data shift over time. Traditional static models fail in such settings. The work explores drift detection algorithms, online learning, and incremental model updates to ensure sustained detection accuracy against evolving insider threat patterns — contributing to behavioral analytics, adaptive ML, and enterprise cybersecurity.