Zainab Husain

Khalifa University of Science and Technology

Papers

Total Citations

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About

Zainab Husain is a pioneering researcher at the intersection of tactile sensing, robotics, and biomedical imaging, with a primary focus on advancing Electrical Impedance Tomography (EIT). Her work addresses the critical challenge of equipping robots with human-like tactile capabilities—a key bottleneck in dexterous manipulation and human-robot interaction. Husain’s major contribution lies in developing machine learning-driven frameworks that overcome the inherent limitations of EIT-based tactile sensors, specifically the poor spatial resolution and image artifacts that have historically hindered practical deployment. Her most-cited paper (2021, 31 citations) introduces a modular framework that leverages machine learning to dramatically enhance tactile image quality, representing a significant leap forward in the field. Additionally, her 2019 work on a neural network-based local decomposition approach for EIT image reconstruction demonstrates her commitment to improving the core computational methods of this non-invasive, non-ionizing imaging modality. With applications spanning from medical diagnostics to soft robotics, Husain’s research is laying the groundwork for more sensitive, reliable, and intelligent tactile systems. Her innovative use of deep learning to solve fundamental imaging problems marks her as a rising leader in the growing field of intelligent sensing.