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About

John-Ross Rizzo is a pioneering researcher at the intersection of assistive technology, computer vision, and rehabilitation engineering. His work centers on developing intelligent systems to empower people with blindness and low vision (pBLV), with major contributions spanning vision-based navigation, multi-modal AI assistance, and soft robotics for rehabilitation. His most influential work, "UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low Vision" (2022, 16 citations), introduced a novel approach that eliminates the need for costly pre-installed sensor infrastructure, making navigation assistance more accessible. Building on this, his multi-modal foundation model (2024, 16 citations) addresses critical challenges in scene recognition and hazard identification for pBLV in unfamiliar environments. Rizzo’s impact extends beyond assistive navigation; his research on Pneu-Net soft robotic actuators (2023, 8 citations) advances safer, lower-cost rehabilitation robots, while his work on semantic SLAM (2023, 7 citations) enables real-time localization for both robots and visually impaired users. Notably, his investigation into low-frequency motor cortex EEG (2024, 4 citations) explores brain-computer interfaces for predicting force development, opening new avenues for motor rehabilitation. Through these interdisciplinary contributions, Rizzo is shaping the future of accessible, intelligent assistive technologies.