Alborz Geramifard

Papers

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About

Alborz Geramifard’s research lies at the intersection of multi-agent systems, reinforcement learning, and real-world AI deployment. His most influential work, “Cooperative Mission Planning for Multi-UAV Teams” (68 citations), addresses the critical challenge of coordinating multiple unmanned aerial vehicles to achieve shared objectives—a foundational contribution to autonomous swarm operations. In “Reinforcement Learning with Misspecified Model Classes” (21 citations), Geramifard tackles a fundamental problem in robotics: how agents can learn effectively when their internal models of the world are inevitably incomplete or incorrect. This work provides a rigorous framework for robust decision-making under model uncertainty, directly impacting the design of adaptive robots. His forward-looking perspective is evident in “The Future of Artificially Intelligent Assistants” (9 citations), which explores the evolution of AI from science-fiction tropes to practical, trustworthy companions. Geramifard’s contributions bridge theoretical rigor and applied systems, offering both algorithmic insights and scalable solutions for autonomous teams. His work continues to influence researchers developing resilient, cooperative AI for dynamic, uncertain environments.