I’m an AI scientist with 8+ years of experience in applied machine learning, mostly in research roles. I now work as a consultant, helping teams design and prototype early-stage AI systems — with a focus on upstream thinking: defining the right ideas, testing feasibility, and building with technical clarity.

This upstream mindset also drives my independent research into micro-scale environmental accounting — a systems-level challenge that sharpens my ability to clarify ambiguity and design from first principles.

LinkedIn Scholar

🧪 Background

• PhD in Machine Learning - focused on deep learning for EEG signals, with foundational work demonstrating how neural networks can improve signal analysis
• 5 years at Naver Labs Europe — worked across NLP, search, optimization, and reinforcement learning in applied research settings

🧠 How I Work

My most consistent contribution has been strategic foresight: spotting the crucial idea that unblocks a project or architecting a system with the clarity to evolve. I thrive on upstream thinking, which is why I focus on early-stage AI.

• Exploring non-obvious ideas
• Designing lean, high-leverage architectures
• Prototyping core components to prove what’s possible
• Supporting clear thinking and technical growth within the team

🎓 Education

• PhD, Machine Learning — Université Grenoble Alpes
• MSc, Materials Science — Imperial College London
• Diplôme d’Ingénieur — École Polytechnique