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Aly-azzam/README.md

Hi, I'm Ali Azzam 👋

🎓 Final-year Computer & Communication Engineering student at Université Saint-Joseph (USJ), Beirut.
💡 Interested in Applied Artificial Intelligence, intelligent systems, and automation.

I enjoy building end-to-end AI systems, from data pipelines and machine learning models to full backend services and automation workflows.


🔬 Final Year Project

AugMentor 2.0 — Vision–Language–Action System for Egocentric Video Augmentation

In collaboration with Mines Paris – PSL

An applied AI system designed to analyze instructional egocentric videos and generate augmented feedback for instructors and learners.

Key components:

  • Computer vision pipeline for hand, finger, and object detection
  • Extraction of kinematic motion indicators
  • Integration of vision, language, and action representations
  • Generation of interactive augmented video overlays

Focus: usability, modular architecture, and real-world applicability.


🚀 Selected Projects

Air Quality Prediction using Machine Learning

Machine learning pipeline to predict PM2.5 pollution levels using meteorological and temporal data.

Highlights:

  • Exploratory data analysis and feature engineering
  • Time-aware train/test splitting
  • Models: Random Forest, Gradient Boosting, Linear Regression
  • Evaluation using MAE and RMSE

Technologies: Python, Pandas, Scikit-learn


Air Quality Data Engineering Pipeline

End-to-end pipeline for processing and serving air quality data.

Highlights:

  • Data ingestion and transformation
  • SQL storage and API access
  • Backend services built with FastAPI

Technologies: Python, FastAPI, SQL, Docker


Automation Workflows

Production-style automation systems integrating AI and enterprise workflows.

Highlights:

  • Designed 15+ automation workflows
  • Document parsing and metadata extraction
  • Integration with enterprise platforms
  • Monitoring and error-handling mechanisms

Technologies: n8n, Power Automate, Python


Industry 4.0 Intrusion Detection (Academic Project)

Human detection system using IR-UWB radar signals and machine learning models.

Highlights:

  • Processing radar signals under low-SNR conditions
  • Trained CNN architectures (ResNet-18, AlexNet) and SVM classifiers
  • Evaluated detection accuracy in industrial environments

Note: code is currently under supervision and not publicly available.


💼 Experience

Automation Engineer — Germany-based Consultant (Remote)

Designed and deployed production-grade automation systems integrating AI components.

Key work:

  • Expense reimbursement automation platform
  • PDF parsing and metadata extraction
  • Enterprise workflow integration
  • Monitoring and reliability improvements

AI Engineer Intern — Data Consult

Developed AI-powered systems for document processing and medical chatbot automation.

Key work:

  • WhatsApp chatbot for appointment management
  • Document embedding and classification pipelines
  • AI-assisted tender compliance analysis

Software Developer Intern — Intalio

Worked on backend and frontend systems in an Agile environment.

Technologies:

  • ASP.NET Web API
  • Angular
  • REST services

🛠 Technical Skills

Programming
Python • C++ • Java • C# • MATLAB • SQL

Artificial Intelligence & ML
Supervised Learning • Random Forest • Gradient Boosting • CNN • SVM

Computer Vision
Video processing • Hand/Object detection • Feature extraction

Backend & Data Engineering
FastAPI • REST APIs • ETL pipelines • SQLite • Pandas

Automation Systems
n8n • Power Automate • Workflow orchestration

Tools
Docker • Git • Linux • Windows • macOS


🌍 Languages

English • French • Arabic


📫 Contact

Email: aliazzam2003@icloud.com
LinkedIn: https://www.linkedin.com/in/ali-azzam2003
GitHub: https://github.com/Aly-Azzam

Pinned Loading

  1. automation-workflows-demo automation-workflows-demo Public

    Production-oriented AI & automation workflows built with n8n and Power Automate

  2. FYP-Augmentor2.0 FYP-Augmentor2.0 Public

    Python

  3. air-quality-ml air-quality-ml Public

    Predicting PM2.5 air pollution levels using machine learning models (Linear Regression, Random Forest, Gradient Boosting) with meteorological and temporal data.

    Jupyter Notebook

  4. smart-task-manager smart-task-manager Public

    Python 1

  5. air-quality-data-engineering air-quality-data-engineering Public

    End-to-end ETL pipeline with FastAPI, SQLite and Docker using real Paris air quality data.

    Python