π Alexandria, Egypt (Open to Alexandria/Cairo Hybrid, Global Remote & Visa-Sponsored Relocation)
- π Senior Data Science & Artificial Intelligence Student at Alexandria National University (Cumulative Standing: Very Good | Expected Graduation: Jun 2027).
- π§ 30+ End-to-End Engineering Projects spanning Autonomous Multi-Agent Graph RAG, Streaming Fraud Detection with Drift Guard, Two-Stage Recommender Systems, Quantum-Hybrid Portfolio Optimization, and Computer Vision.
- π‘οΈ Technical Internship Experience:
- Malware Analysis Intern @ STRA-City β Analyzed malicious software samples, identified system vulnerabilities, and supported threat detection protocols.
- Trainee Intern @ AEDC β Completed core engineering systems and operational workflow training.
- π IBM Certified Professional (Coursera) across Machine Learning with Python, Deep Learning with Keras & TensorFlow, and Neural Networks with PyTorch.
| Project | Domain | Key Highlights |
|---|---|---|
| π€ Autonomous-Multi-Agent-Graph-RAG | GenAI β’ Multi-Agent β’ Graph-RAG |
Autonomous multi-agent orchestration over knowledge graphs for high-precision retrieval and complex LLM reasoning. |
| π‘οΈ Streaming-Fraud-Engine-Drift-Guard | MLOps β’ FinTech β’ Anomaly Detection |
Real-time credit card & transaction fraud classification pipeline with automated data/concept drift detection. |
| π§ NLP-DeepLearning-Suite | NLP β’ BiLSTM β’ Word2Vec/GloVe β’ BERT |
End-to-end NLP suite: BiLSTM neural spam classifier, Word2Vec vs. GloVe manifold study (97.75% accuracy), pure-NumPy Multinomial Naive Bayes, and BERT sentiment API. |
| π― Two-Stage-Recommender-Engine | RecSys β’ Deep Learning |
Production-style two-stage candidate generation and deep ranking architecture in Python. |
| βοΈ Quantum-hybrid-portfolio-engine | Quant Finance β’ Quantum ML |
Hybrid quantum-classical asset allocation and financial portfolio risk optimization engine. |
| ποΈ Computer-Vision-for-Road-Sign-Detection | Computer Vision β’ Autonomous Systems |
Deep learning road sign detection (YOLOv5) and motion-aware smart surveillance vehicle/pedestrian counter. |
| π Socket-Email-Client-Network-Engine | Networking β’ TCP Sockets β’ SMTP/IMAP |
Secure SSL desktop email client with live TCP socket telemetry, multi-threaded concurrent TCP command server, and Wireshark packet captures. |
| π‘ California-Housing-Price-Predictor | Data Science β’ Streamlit β’ Regression |
Interactive Streamlit real estate valuation web app with Variance Inflation Factor (VIF) multicollinearity diagnostics. |
| 𧬠Biomedical-Diabetes-Multivariate-Analysis | Healthcare AI ⒠CCA ⒠SVM |
Canonical Correlation Analysis (CCA), multivariate statistics, and SVM diagnostic modeling on the Pima Indians Diabetes dataset. |
| π Customer-Segmentation-Unsupervised-ML | Unsupervised ML β’ Clustering β’ Power BI |
Customer behavioral segmentation via Agglomerative Hierarchical Clustering, K-Medoids, Fuzzy C-Means, and Power BI retail dashboards. |
| π SIS_GradutionProject | Full-Stack β’ TypeScript |
Comprehensive Student Information System (SIS) supporting academic workflows, records, and portal management. |
| π Egypt-Stocks | Data Science β’ Time-Series |
Egyptian Exchange (EGX) market data analytics, feature engineering, and stock trend modeling. |
| π» Operating-Systems-Project | Systems β’ Algorithms |
Interactive simulator for CPU Scheduling, Memory Allocation, and Page Replacement with visual benchmarking. |
| β‘ Maximum-Subarray-Algorithm | Algorithms β’ Graph Theory |
Divide-and-Conquer Maximum Subarray solver and Dynamic Single-Source Shortest Path (Dynamic Dijkstra) engine. |
| ποΈ Exam-Scheduling- | Genetic Algorithms β’ Optimization |
Evolutionary computation conflict-free timetable optimizer and interactive schedule GUI. |
- IBM Certified Professional (Coursera):
- Machine Learning with Python
- Introduction to Deep Learning & Neural Networks with Keras
- Deep Learning with Keras and TensorFlow
- Introduction to Neural Networks and PyTorch & Deep Learning with PyTorch
- Languages: Arabic (Native) β’ English (Fluent β C1) β’ German & French (Working Knowledge)