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About

Ekta U. Samani is a robotics and computer vision researcher whose work sits at the compelling intersection of topological data analysis, visual perception, and autonomous systems. She is best known for pioneering the application of persistent homology — a mathematical framework for extracting shape-based features — to object recognition challenges faced by mobile robots in real-world indoor environments. Her 2021 paper introducing topologically persistent features for visual object recognition in unseen environments garnered 14 citations, establishing her as an emerging voice in topology-driven robot perception. Building on this foundation, her TOPS descriptor framework for occluded point clouds (2023, 9 citations) and the color-integrated THOR2 system (2024) demonstrate a sustained, evolving research program pushing robots toward more human-inspired recognition capabilities. Beyond robotics, Samani has also contributed to microscale robotic manipulation, developing accurate visual perception methods for optical tweezers operating in heterogeneous biological microenvironments (2017, 10 citations). Across these domains, her work consistently addresses the hardest perception scenarios — clutter, occlusion, and environmental variability — making her contributions particularly valuable for researchers advancing robust, generalizable robot autonomy.