
I'm a Computer Science & AI student at Al Alamein International University, graduating in 2027, focused on building AI systems that actually run — not just models in a notebook, but the full path from data to something deployed and usable.
My work spans three areas: parameter-efficient NLP (fine-tuning and distillation), full-stack ML applications (traffic prediction, routing, containerized deployment), and multi-agent LLM systems (agents that research, summarize, and verify each other's work). I'm currently building on this through DEPI, Egypt's national program for agentic and generative AI development.
What I care about most is the last step most student projects skip — turning a working model into something someone else can open, use, and trust. The details and results for each project are below.
Al Alamein International University (AIU) • Egypt
Specialization in Artificial Intelligence covering machine learning, deep learning, NLP, algorithms, and software engineering.
Digital Egypt Pioneers Initiative (DEPI)
City of Scientific Research and Technological Applications (SRTA-City)
Information Technology Institute (ITI)
Specialized focus in natural language processing, transformer architectures, parameter-efficient fine-tuning, and neural knowledge distillation.
Production and research frameworks for deep learning, scientific computing, and model evaluation.
Graph routing, concurrent network architectures, and containerized backend microservices.
Core languages for AI pipelines, concurrent systems, desktop GUIs, and simulation.
Game AI navigation engines, 3D animation modeling, and developer tooling.
ML-Powered Route Optimization & Traffic Planning
Containerized full-stack web application combining Scikit-Learn traffic prediction models with A*, Dijkstra, and Dynamic Programming algorithms to simulate adaptive routing across Greater Cairo.
GPT-2, LoRA & Knowledge Distillation
Parameter-efficient NLP pipeline combining LoRA adapter matrices with teacher-student knowledge distillation, training only 2.15% parameters while retaining 96.9% of full teacher F1 performance.
Autonomous Multi-Agent Orchestration & Source Verification
A multi-agent AI system where specialized agents (Search, Summarizer, Fact-Checker) collaborate under a Coordinator to research a question, synthesize a cited answer, and fact-check it — producing a grounded, source-verified report. The Fact-Checker agent caught and flagged an unsupported statistic during a live test run, demonstrating real self-verification.
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