An economist-engineer who builds AI systems grounded in observable reality — where every inference is traceable, every output is measurable, and every decision is auditable.
I am an economist-engineer specializing in evidence-driven artificial intelligence systems. My work focuses on AI grounded in empirical evidence and physical reality — where inference chains are traceable, outputs are measurable, and decisions are auditable.
The defining thread across my career is the emphasis on observed actions and measurable outputs rather than self-reported metrics, opinion-based scoring, or opaque black-box models. This makes my systems particularly suitable for government, mission-critical, and industrial deployments where accountability is non-negotiable.
I hold a PhD in Economics from UBC, a B.Tech in Chemical Engineering from IIT Kanpur, and executive certificates from Harvard Kennedy School and Columbia Business School.