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ouaelesi/README.md

Hi there, I'm Ouael! 👋

Software Engineer | AI & Machine Learning Researcher | Full-Stack Developer

🎓 Master's Research Student in Software Engineering at Université de Sherbrooke, Canada

🔬 Researching Federated Learning, Transfer Learning, Privacy-Preserving AI, and Explainable AI at MEDomicsLab.

💻 Passionate about building intelligent, scalable, and user-friendly software solutions, bridging the gap between Software Engineering and Artificial Intelligence.


🚀 About Me

  • 🎓 Master's Research Student in Software Engineering at Université de Sherbrooke.
  • 🔬 Working on MEDfl, a framework for Federated Machine Learning.
  • 🤖 Interested in Machine Learning, Deep Learning, Federated Learning, and Agentic AI.
  • 🔐 Exploring Differential Privacy, Explainable AI (SHAP), and Privacy-Preserving Machine Learning.
  • 🛠️ Experienced in Full-Stack Web Development, DevOps, MLOps, and Distributed Systems.
  • 🌐 Former Google Developer Groups (GDG) Algiers organizer and Microsoft Learn Student Ambassador.
  • 📚 Passionate about research, open-source development, and building impactful technologies.

🛠️ Tech Stack

🤖 Artificial Intelligence & Machine Learning

Python PyTorch Scikit-learn XGBoost Flower Optuna SHAP Opacus

🌐 Full-Stack Development

JavaScript TypeScript React Next.js Node.js Express.js Vue.js Laravel PHP HTML5 CSS3 Tailwind CSS Bootstrap

📱 Mobile & Desktop Development

React Native Kotlin Android Electron

⚙️ Programming Languages & Systems

C C++ Java Go CUDA OpenMP MPI

☁️ DevOps, Databases & Tools

Docker Git GitHub Actions Linux PostgreSQL MySQL SQLite Prisma


🔬 Research & Projects

🧠 MEDfl — Federated Machine Learning Framework

Contributing to the development of MEDfl, a framework designed to simplify federated machine learning experimentation and deployment.

Key features and research areas:

  • Federated Learning in simulation and real-world environments.
  • Support for neural networks and federated XGBoost.
  • Aggregation strategies including FedAvg, FedAdam, FedYogi, FedProx, and others.
  • Transfer Learning and Differential Privacy.
  • Federated SHAP for explainable AI.
  • Hyperparameter optimization using Grid Search and Optuna.
  • Distributed training and orchestration across multiple machines.
  • User-friendly desktop interface for creating and executing federated learning pipelines.

Technologies: Python, PyTorch, Flower, XGBoost, Opacus, SHAP, Go, Next.js, Electron.


🎯 Research Interests

Federated Learning · Privacy-Preserving AI · Distributed Machine Learning · Explainable AI · Transfer Learning · Deep Learning · MLOps · Agentic AI · Software Engineering


📫 Let's Connect!

I'm always interested in collaborating on innovative software engineering, artificial intelligence, and open-source projects.

📧 Email: ouael.nedjem.eddine.sahbi@usherbrooke.ca


⭐ Building intelligent systems that are scalable, collaborative, and privacy-aware.

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