Trystan is an early-career scholar whose work bridges cognitive theory and advanced mathematical modeling to make latent learning processes visible and actionable. Currently an AI Researcher and Lead Data Scientist at Edulwise, and formerly a Research Data Scientist at the University of Fribourg, his research centers on interpretable diagnostic architectures. By analyzing real-world classroom interactions, he translates fine-grained learner behavior into transparent, auditable signals for adaptive scaffolding. 

This formal systems thinking drives his design of explainable, teacher-in-the-loop AI that shifts educational technology from opaque automation to equitable, process-aware support that empowers educators. 

Invited to present his findings across Europe and Asia, he was recently awarded the 1st Prize in the ASEM LLL Global Award (2025–2026) for Best AI in Education.