MSc Artificial Intelligence student at Université Paris-Saclay and École Polytechnique Math & Computer Science graduate.
AI Research intern @ ORAILIX École Polytechnique
I work on applied machine learning systems across scientific ML, neural operators, retrieval/RAG, and computer vision.
AI Research Intern, École Polytechnique
Developing a neural-operator-based architecture for approximating solutions of hyperbolic PDEs with shocks and discontinuities.
- Built models for the LWR traffic equation
- Benchmarked against WENO-5 numerical solvers
- Focus: neural operators, GNNs, PDEs, scientific ML
AI Research & Development Intern, Université Paris-Saclay
Led a student research team developing a PyTorch PINN for steady incompressible airflow prediction.
- Worked with limited data and computational resources
- Used physics-informed losses for Navier–Stokes constraints
- Explored frequency-based acceleration for inference
AI R&D Intern, Vlatacom Institute
Built real-time monocular perception pipelines for ship classification, detection, and distance estimation.
- Fine-tuned neural networks for maritime surveillance
- Developed pilot pipeline for real-time coastal monitoring
Implemented retrieval-augmented generation and patent prior-art retrieval systems.
- BM25, dense embeddings, reranking
- Reciprocal Rank Fusion
- MAP@100 and Recall@K benchmarking
Python, PyTorch, Pandas, C/C++, CUDA, Docker, SQL, Bash, R, Coq
Machine Learning · Scientific ML · Computer Vision · RAG · Information Retrieval · Neural Operators