• Data Science, Markets, Organizations & Strategies
• Specialized in Machine Learning and Artificial Intelligence
• Selected for a double degree program

• Specialized in Artificial Intelligence & Data Analytics
• Developed a strong foundation in data structures, algorithm and software engineering

Model Risk Analyst

• Joined for an end-of-studies internship. Designed and implemented an Automated Machine Learning (AutoML) tool to challenge internal models. Enabled the team to standardize analysis processes and increase efficiency (~5x faster)
• Currently working full-time as Model Risk Management Analyst

Translating Tax Law to Code with LLMs

• Paper accepted for the NLLP workshop at the EMNLP 2025 conference
• Built a dataset for legal code generation by collecting online Catala code
• Defined evaluation metrics for the Catala programming language (e.g., CodeBleu, Tree-Edit distance)
• Evaluated performance across different LLM families (e.g., Llama, Phi) using custom metrics

ONNX Runtime using Rust

• Created a parser to extract information to build a network from an ONNX file
• Implemented a subset of ONNX operations
• Used Rust libraries for efficient parallel execution of ONNX models

Web Scraping using BeautifulSoup

• Scraped ~230,000 products from 18 stores across 10 regions in France
• Analyzed correlation between regional wages and product prices, providing insights for pricing strategies
• Conducted a brief analysis of the Nutri‑Score

Fault Aware Training using PyTorch

• Trained CNNs with intentional faults to improve error tolerance
• Used fault injection to enhance CNN accuracy during training

Biometric Identity Verification using Python

Booking airplane seats using React and JS