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About

Omer San is a researcher whose work spans autonomous systems, robotics guidance, and computational mechanics. His most influential contribution lies in the development of intelligent navigation frameworks for unmanned surface vehicles (USVs), particularly his highly cited 2020 work on COLREG-compliant collision avoidance using deep reinforcement learning, which has garnered 89 citations and represents a significant advancement in maritime autonomy. This research addressed a long-standing challenge in autonomous vehicle guidance by integrating international maritime collision regulations directly into a learning-based framework, bridging the gap between theoretical robotics and real-world operational constraints. Beyond autonomous systems, San has also explored the mechanical behavior of soft functionally graded materials, investigating phenomena such as radial expansion, cavitation, and eversion in spherical shells — work with direct relevance to emerging technologies including soft robotics, flexible electronics, and prosthetics. His research profile reflects a notably interdisciplinary orientation, connecting machine learning, control theory, and nonlinear solid mechanics. Students and researchers working on autonomous maritime systems, reinforcement learning applications in navigation, or advanced material modeling will find San's contributions particularly valuable as foundational references in their respective fields.