Mohammed Alshiekh

The University of Texas at Austin, Saudi Aramco (United States)

Papers

Total Citations

167

H-Index

About

Mohammed Alshiekh is a researcher whose work bridges cutting-edge materials science with practical robotics and artificial intelligence. His most impactful contribution is the development of an imperceptible electrooculography (EOG) graphene sensor system for human–robot interfaces, a paper that has garnered 155 citations. This innovation addresses a critical limitation in traditional EOG sensors—their bulkiness and discomfort—by creating a flexible, nearly invisible sensor that records eye movements to control robots, opening new avenues for assistive technologies and seamless human-machine interaction. Beyond sensor technology, Alshiekh has advanced formal methods in robotics through Salty, a domain-specific language for GR(1) specifications and designs. This work enables correct-by-construction synthesis of robot controllers, reducing the time and errors inherent in manual design. More recently, he has tackled the data scarcity challenge in the energy industry by exploring synthetic data generation for machine learning applications, a vital step toward deploying AI in critical infrastructure. With a research portfolio spanning wearable sensors, formal verification, and applied machine learning, Alshiekh demonstrates a rare ability to move from fundamental material science to high-level system design, making his work both innovative and broadly impactful.