Research Interests

I study the mathematical foundations of routing and decision-making problems under uncertainty, with applications to multi-agent and hybrid systems. A central focus of my work is Learning-to-Defer — a framework in which predictive models learn not only to make decisions, but also to strategically abstain and defer to external experts when confidence is low.

AI-based model diagram

My research focuses on formally characterizing when deferral is beneficial and how models can learn to defer in a provably optimal way, drawing on tools from statistical learning theory and decision theory.

TLDR: In essence, I teach models not only what to decide, but when to ask for help

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