Zaid Tasneem

Papers

Total Citations

H-Index

About

Zaid Tasneem’s research lies at the intersection of computational imaging, robotics perception, and privacy-aware sensing, with a focus on advancing depth-sensing technologies. His major contributions include pioneering adaptive foveation for scanning depth sensors, inspired by human vision—enabling robots to dynamically allocate high angular resolution to regions of interest while maintaining a wide field of view. This work, published in 2020, has garnered 16 citations and is foundational for efficient perception in autonomous systems. Tasneem also demonstrated directionally controlled time-of-flight ranging for mobile platforms, achieving 15 citations, where he showed that modulating scanning patterns in the angular domain unlocks novel algorithms and applications, such as energy-efficient 3D mapping. More recently, his 2022 work on learning phase masks for privacy-preserving passive depth estimation (14 citations) addresses critical concerns in surveillance and human-robot interaction by enabling depth recovery from optically encoded images, preventing unauthorized visual reconstruction. Through these innovations, Tasneem has established himself as a rising figure in computational sensing, bridging hardware design and algorithmic intelligence to create smarter, safer, and more efficient perception systems for real-world deployment.