Architecting AI. Production Engines.
Engineering high-throughput computer vision engines, asynchronous OCR extraction pipelines, and PyTorch deep neural recommendation models for enterprise production.
GitHub Contribution Heatmap
Education & Skillset
Computer Science scholar at COMSATS University Islamabad specializing in end-to-end Machine Learning pipelines, model optimization, and high-throughput data extraction engines.
Hafiz Muhammad Noor
Dedicated to solving complex engineering challenges through mathematical modeling and machine learning. Deeply engaged in the Python data ecosystem—building predictive architectures, computer vision models, asynchronous scraping systems, and chess logic engines from scratch.
COMSATS University Islamabad
Technical Mastery & Frameworks
Core Languages
ML Frameworks & Libraries
Infrastructure & MLOps
IBM Professional Certifications
AI Engineering Professional Certificate
Mastered deep learning models, generative AI architectures, neural network training with PyTorch & TensorFlow, NLP, and model deployment strategies.
View Verified CertificateMachine Learning Specialization
Comprehensive training in supervised & unsupervised learning, gradient boosted decision trees (XGBoost), model validation metrics, and Scikit-learn pipelines.
View Verified CertificateProjects & ML Systems
A collection of open-source models, data pipelines, Kaggle research, and algorithmic experiments.
Featured Work
Kaggle Competitions & ML Notebooks
Frequently Asked Questions
BBQ-RAG implements a custom Rust AVX2/FMA SIMD compute kernel (`maxsimd`) with 4-way query token unrolling and in-register maximum tracking. By computing late-interaction MaxSim dot-products directly inside 256-bit YMM registers without intermediate matrix allocations, it cuts memory traffic by 27x and scales linearly with Rayon adaptive work pools.
I leverage PyTorch for deep neural network prototyping, custom loss formulation, and GPU model optimization. For structured tabular predictive challenges, I rely on Scikit-learn, XGBoost, and LightGBM cross-validation pipelines.
Peeler uses Playwright's asynchronous event loop to execute multi-threaded browser sessions in parallel. Extracted image regions undergo adaptive threshold binarization before passing through EasyOCR models.
I apply robust outlier detection via IQR/Isolation Forests, categorical target encoding, imputation strategies based on feature distributions, and synthetic balance (SMOTE) where class imbalance degrades precision-recall metrics.
Models are serialized via TorchScript or ONNX runtime and wrapped inside asynchronous FastAPI endpoints. Docker containers are deployed to cloud infrastructure with automated health probes and latency monitoring.