I’m a PhD student in Artificial Intelligence at Université de Thiès. I work on applied machine learning and research problems at the intersection of deep learning, explainability, and real-world applications.
- 🎓 PhD student in Artificial Intelligence
- 🔬 Research interests: machine learning, deep learning, explainable AI, causal inference, and data-driven modeling
- 🔭 Currently focused on: developing robust and interpretable ML models for applied problems
- 🌱 Always learning: probabilistic modeling, advanced deep learning techniques, and reproducible research practices
- 🤝 Open to collaboration on interesting machine learning and AI projects
- Languages: Python (primary), Bash
- Libraries & Frameworks: PyTorch, TensorFlow, scikit-learn, NumPy, pandas, JAX (familiar)
- Tools: Git, Docker, Jupyter, ML experimentation tools (Weights & Biases, TensorBoard)
I contribute to and build projects that focus on practical machine learning, reproducibility, and interpretability. Check my GitHub repositories for up-to-date code and demos.
If you’d like a guided tour of any project, open an issue or mention me in a discussion and I’ll gladly explain design choices, results, and how to reproduce experiments.
Publications, preprints, and technical reports will be listed here — feel free to request copies or links to specific works.
- Open an issue on the relevant repository to propose an idea or ask for help
- Send a short email describing the project and possible contributions: oumar.kane@univ-thies.sn
- I’m happy to mentor student projects or co-author research when there’s a clear scope and dataset
- Email: oumar.kane@univ-thies.sn
- GitHub: https://github.com/Oumar199