Mohamad Alameh

Papers

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About

Mohamad Alameh is a leading researcher at the intersection of robotics, tactile sensing, and embedded machine learning. His work focuses on creating intelligent, human-like perception systems for robots, with a particular emphasis on electronic skin and dexterous manipulation. Alameh’s major contribution lies in embedding convolutional neural networks (CNNs) directly into tactile sensing hardware, enabling real-time, on-sensor data decoding for texture classification and pattern recognition—a paradigm he terms “near-sensor computation.” His 2020 paper on smart tactile sensing systems, which has garnered 38 citations, provides a foundational comparison of CNN implementations for tactile data decoding across various hardware platforms. In his highly cited 2021 work (24 citations), Alameh bridges human demonstration and robot manipulation, analyzing hand-object interaction to develop more capable robotic hands. By studying how humans use their hands, he aims to replicate those skills in robots, advancing the field toward truly autonomous operation in human environments. His innovative approach to combining machine learning with tactile sensing is paving the way for more responsive, intelligent robotic systems.