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About

Katherine A. Skinner is a pioneering researcher at the intersection of underwater robotics, computer vision, and machine learning, with a particular focus on enabling autonomous marine systems to perceive and navigate complex aquatic environments. Her work addresses some of the most persistent challenges in underwater perception — including color distortion, depth estimation, and scene reconstruction — caused by the unique optical properties of water. Skinner's most influential contributions include UWStereoNet (2019, 42 citations), an unsupervised deep learning framework for simultaneous depth estimation and color correction in underwater stereo imagery, and WaterNeRF (2023, 39 citations), which extends neural radiance field technology to model water column effects for faithful underwater scene rendering. Her early work on plenoptic cameras for real-time 3D underwater reconstruction (2016, 25 citations) and automatic color correction for underwater mapping (2017, 24 citations) helped establish foundational pipelines still referenced today. Beyond underwater domains, Skinner has advanced multimodal sensor fusion through CLONeR (2023, 23 citations), combining camera and LiDAR data for robust neural scene representations. Her commitment to community-driven progress is evident in her benchmark dataset for shipwreck segmentation from sonar imagery (2024, 28 citations), lowering barriers for marine machine learning research. With over 200 cumulative citations, her work is shaping the future of intelligent underwater robotics.