Kent Laboratory for Trustworthy and Responsible AI (KeTRAI)

My team focuses on the rigorous causal validation and system-level accountability of AI deployed in high-stakes socio-technical environments. Moving beyond post-hoc interpretability, I develop geometrically-aware and causally-robust frameworks to ensure that AI decisions are fundamentally auditable, resilient, and verifiable against infrastructure-level failures. A central thrust is designing platform-agnostic validation schemas for agentic AI, detecting systemic vulnerabilities before they propagate, directly informed by my ongoing policy work at the Mila Quebec AI Institute and the European Commission's AI Office. I bridge advanced geometric representation learning, causal inference, learning theory with translational impact, ensuring that AI systems in healthcare, finance, and governance are safe, sovereign, and institutionally trustworthy.