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About

Valentina Vasco is a robotics and computer vision researcher whose work spans neuromorphic sensing, event-driven perception, and socially assistive robotics. She is perhaps best known for her pioneering contributions to event-based vision, particularly her 2016 paper on fast event-based Harris corner detection, which has accumulated 167 citations and remains a foundational reference in the field. By exploiting the unique asynchronous, low-latency properties of event-driven cameras, Vasco demonstrated how classical computer vision tasks — corner detection, independent motion detection, and vergence control — could be reimplemented with dramatically improved speed and efficiency on neuromorphic hardware, including the iCub humanoid platform. Her development of an event-driven software library for YARP further lowered barriers for the broader robotics community to adopt this emerging technology. In parallel, Vasco has made meaningful contributions to human-robot interaction and rehabilitation robotics, investigating how physical and virtual robotic agents can support healthcare tasks and how sensory factors influence rehabilitation outcomes. Her research on sign language generation in humanoid robots further reflects her commitment to inclusive, assistive technology. Across these diverse areas, Vasco's work consistently bridges cutting-edge sensing paradigms with real-world human-centered applications.