Ksenia Shubina

Papers

Total Citations

126

H-Index

About

Ksenia Shubina is a leading researcher in the intersection of robotic vision, visual attention, and active perception. Her work focuses on developing computational models that enable mobile robots to perform efficient visual search in complex, three-dimensional environments. Shubina’s foundational study, "Visual search for an object in a 3D environment using a mobile robot" (2010, 98 citations), established key algorithms for integrating attention-driven gaze control with physical robot movement, demonstrating how machines can mimic biological search strategies. Expanding on this, her 2019 monograph "Attention and Visual Search: Active Robotic Vision Systems that Search" (28 citations) provides a comprehensive theoretical framework, arguing that visual attention is fundamentally a mechanism for optimizing search processes. This perspective unifies disparate studies of attention under a single, powerful principle. Shubina’s contributions are critical for advancing autonomous systems in applications like surveillance, exploration, and assistive robotics, where efficient, real-time object detection is paramount. Her work bridges cognitive science and engineering, offering both practical algorithms and a deeper understanding of how vision systems—natural or artificial—can actively seek and find.