🛠️ Skills

  • Programming: Python (PyTorch, Lightning, NumPy, Pandas, Matplotlib), Rust, C, R
  • Machine Learning & AI: Deep Learning, Computer Vision, NLP, Reinforcement Learning, Transfer Learning, Transformers, GANs
  • Frameworks & Tools: PyTorch, Scikit-Learn, Lightning, LangChain, Hugging Face Transformers, Docker, Git, VS Code, Jupyter, UNIX/Linux
  • Research & Development: Retrieval-Augmented Generation (RAG), Multimodal Learning, Generative AI, Time Series Forecasting
  • Languages: English (C1), French (C1), Arabic (Native)

🏆 Projects

  • Self-Supervised Image Classification
    Implemented a self-supervised contrastive learning pipeline for image representation learning and downstream classification.
    Trained models on unlabeled datasets and evaluated their features using linear probing.
    Tools Used: Python, PyTorch, torchvision, NumPy, Matplotlib.

  • Kurio – AI Learning App for Children
    Developed an interactive educational app that uses large language models to generate personalized stories and quizzes for kids.
    Awarded 3rd place at the GAIA Hackathon for innovation in AI-driven education.
    Tools Used: Python, LangChain, OpenAI API.

  • Reinforcement Learning for Atari Games
    Designed and trained reinforcement learning agents to learn optimal policies in Atari 2D environments.
    Focused on policy improvement and exploration strategies for enhanced training efficiency.
    Tools Used: Python, Gymnasium (OpenAI Gym).

  • Protein Role Classification with ProtBERT
    Fine-tuned ProtBERT embeddings for supervised protein role classification in a bioinformatics challenge organized by InstaDeep.
    Achieved competitive performance using transfer learning techniques for biological sequence understanding.
    Tools Used: Python, PyTorch, ProtBERT.

🧩 Research Interests

I tried listing them, but honestly — I’m just a curious guy.
I’m interested in anything interesting (how’s that for an infinite loop?).

  • Large Multimodal Models
  • Spatial–Temporal Representation Learning
  • Reinforcement Learning
  • Learning Theory
  • Robust and Biologically-Inspired Neural Architectures
  • Generative AI and Self-Supervised Learning
  • Retrieval-Augmented Reasoning and Multimodal Understanding