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About

Soroush Zare is a robotics researcher whose work sits at the intersection of cable-driven parallel robots (CDPRs) and intelligent control systems. His primary research areas include the kinematic analysis and control of under-constrained cable robots, 3D model reconstruction, and the application of machine learning—particularly neural networks—to robotic systems. Zare’s most cited work (11 citations) introduces a novel approach to solving forward and inverse kinematics for under-constrained CDPRs using multilayer perceptrons, radial basis functions, and local linear model trees, demonstrating how neural networks can adapt to complex, nonlinear data. He further advanced the field with an experimental study on dynamic control for object tracking, comparing kinematic PID and dynamic PD approaches (7 citations). Notably, Zare pioneered a cost-effective method for reconstructing 3D graphical models of objects using under-constrained CDPRs (6 citations), offering a practical alternative to expensive scanning systems. His recent foray into biomedical engineering explores decoding human motion intention from motor imagery EEG using convolutional neural networks (2 citations). With a growing citation record and a focus on bridging theoretical robotics with real-world applications, Zare is establishing himself as a versatile researcher at the forefront of cable-driven robotics and human-robot interaction.