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Payam Varshovi-Jaghargh is a leading researcher in the kinematics and computational modeling of parallel robotic mechanisms. His primary contributions lie in solving the forward kinematic problem—a notoriously complex, nonlinear challenge for parallel robots due to their closed-loop structures. Varshovi-Jaghargh has pioneered hybrid approaches that combine algebraic methods, such as Gröbner bases and resultants, with machine learning techniques. Notably, his 2022 work introduced a parallel evolutionary neural network for the forward kinematic analysis of spatial parallel robots, achieving a robust and efficient solution (10 citations). Earlier, his 2015 paper applied Study’s kinematic mapping and resultant methods to reduce the forward displacement of 3-DoF planar parallel manipulators to a seven-dimensional kinematic space (8 citations). His 2018 study further demonstrated a neural-network-based method for a 3-PRR planar robot, effectively handling the system’s nonlinear equations (7 citations). Most recently, in 2025, Varshovi-Jaghargh extended his work to workspace analysis, using interval analysis to account for active joint ranges of motion. With a consistent focus on bridging theoretical kinematics and practical computational tools, his research is highly relevant for engineers designing precise, real-time control systems for parallel robots in manufacturing and automation.