Gabriel Mesquida Masana

Artificial Intelligence/Machine Learning, integration and digital infrastructure expert innovating and delivering mission-critical, safe and regulated digital systems across global markets.

I work in AI strategy, capability creation, and research in Supervised ML and Reinforcement Learning for the safety-critical aeronautical domain. Real-world, mission-critical applications such as route optimisation, conflict detection and resolution, constrained by safety. I created the first Air Traffic Management Digital Twin in Singapore used for AI research and prototyping new capabilities (Continous Descent Operations).

I also work in Agentic AI for regulated environments that require predictability and norm copmpliance (financial transactions or private data kept available and confidential).

I am a teaching assistant at Stanford's executive education. In the Stanford’s AI Professional Program with XCS229 Machine Learning, XCS236 Deep Generative Models, XCS234 Reinforcement Learning, XCS224R Deep Reinforcement Learning & XCS221 Artificial Intelligence Principles and Techniques. Also the new Agentic AI program: XAG329A Agentic AI, Building Self-Improving AI Agents.