Turn complex data into intelligent action. I build production-ready AI systems that see, understand, and act — combining Computer Vision, Deep Learning, LLMs, and Agentic AI to solve real-world problems.
I'm an AI & Machine Learning Engineer with a Computer Engineering foundation, specializing in Computer Vision, Large Language Models, and Agentic AI. I build intelligent systems ranging from real-time detection and tracking and medical imaging to RAG pipelines and multi-agent LLM applications, using Python, PyTorch, LangChain, LangGraph, FastAPI, Docker, and AWS.
My work combines deep learning, computer vision, NLP, and AI system design to turn complex problems into scalable, production-ready solutions. I’ve built and deployed end-to-end AI systems, reproduced and improved established deep learning architectures, and developed MLOps workflows for reliable model experimentation and deployment.
A mix of production systems and from-scratch paper reproductions — each tagged with its live confidence, i.e. the reported result.
Real-time, sensor-fused counter-UAS platform: dual YOLOv8 detection across RGB + thermal feeds fused via calibrated similarity-transform alignment, an NMSE-based threat-prioritization engine, and full PID visual servoing with an optional online Q-learning tuner. Ranked 5th of 320 teams at ITC Egypt ADC 2026.
Multi-agent system with specialized agents for question generation, summarization, and feedback-driven refinement, plus a RAG pipeline over course materials with async FastAPI microservices, Whisper transcription, and PyMuPDF parsing.
Re-implemented a Hierarchical Relational Network on 4,830 frames from 55 videos via a custom Graph Relational Layer — 91.55% test accuracy, pushed to 92.00% by adding a Graph Attention (GAT) mechanism with test-time augmentation.
Hierarchical Temporal Deep Model with a two-stage LSTM to capture multi-person temporal dynamics — 93% accuracy on benchmark data, with ablations isolating each component against 8 baseline models.
Three progressively stronger baselines — U-Net → improved U-Net → DeepLabV3+ with ASPP — on CamVid across 12 classes, taking mean IoU from 0.34 to 0.59 via Dice+CrossEntropy loss and mixed-precision training.
Failure-forecasting system across 10+ sensors predicting equipment failures 24 hours in advance — 84.7% recall, 96% accuracy, tuned to minimize false alerts and deployed via containerized MLOps workflows.
MultiCheXNet: a multi-task DenseNet-121 performing simultaneous classification, detection, and segmentation through a shared encoder, trained across datasets with mismatched label schemas via a custom dataloader.
Tackled extreme class imbalance on 284,807 transactions with a voting-classifier ensemble and a focal-loss-enhanced PyTorch network — 86% F1, 85% PR-AUC, outperforming standard classifiers on rare-event detection.
6th International Telecom Conference (ITC Egypt), hosted by the Egyptian Air Defense College. Represented Team Skyhawks, building and presenting the Multi-Layer Anti-Drone Defense System as the team's competition entry — ranked in the Top 5 among 320 competing teams, advancing past an initial shortlist of 45 qualified teams.
ITC Egypt ADC · 2026