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Nina Mahmoudian is a prominent robotics researcher whose work spans autonomous underwater vehicles, multi-robot systems, STEM education, and energy-efficient mission planning. She has made foundational contributions to underwater glider locomotion, most notably developing motion control frameworks that enhance locomotive efficiency through perturbation theory—work that has garnered 85 citations and shaped subsequent research in marine robotics. Her 2009 follow-up on efficient motion planning for underwater gliders (44 citations) further solidified her expertise in buoyancy-driven autonomous systems. Mahmoudian's research increasingly focuses on enabling persistent robotic operation by overcoming energy limitations. Her work on multi-robot mission planning with static charging stations (43 citations) and collaborative long-term operation strategies demonstrates her systems-level thinking about real-world deployment challenges. Her underwater docking research extends this vision into marine environments, where energy sustainability remains a critical barrier. Beyond hardware and algorithms, Mahmoudian has invested significantly in robotics education. Her co-robotics hands-on STEM initiative (74 citations) and the open-source GUPPIE underwater robot platform reflect a commitment to accessible, context-driven engineering education. More recently, her team has explored deep reinforcement learning for obstacle avoidance in unmanned surface vehicles, highlighting her evolution toward AI-integrated marine autonomy. With over 300 cumulative citations, her interdisciplinary impact across robotics, education, and autonomy is both broad and enduring.