Raymond Young is a researcher in marine robotics. He received his B.S. and M.S. in Mechanical Engineering from UC San Diego and his Ph.D. in Oceanography from Scripps Institution of Oceanography, forming an interdisciplinary skillset across robotics, control theory, and physical oceanography. His research aims to create scalable autonomous systems for ocean observation by developing algorithms for optimal path planning and adaptive sampling. The overall goal is to enable scientific data collection in under-sampled and highly energetic areas of the ocean where observations by traditional research vessels are unsafe, expensive, and limited by human resources.
As a Schmidt AI in Science Fellow, Raymond will lead a project titled “Information-Based Path Planning for Scalable Autonomy in the Marine Environment.” The project is scientifically focused on sampling turbulent flows over rough topography and will leverage AI techniques for environmental mapping and estimation, in combination with information-theoretic and geometric optimization techniques for adaptive sampling in dynamic and uncertain ocean environments. Its goal is to algorithmically optimize multi-vehicle deployments that maximize information gain subject to dynamical constraints on each vehicle navigating through a moving fluid. This project will produce a closed-loop autonomy framework in which platforms can estimate the environment from sparse and unstructured observations, reason about which new areas are most informative, and efficiently navigate to them.