Shadi Khawandi

Lebanese University, Laboratoire Interuniversitaire des Systèmes Atmosphériques

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About

Shadi Khawandi is a researcher whose work sits at the intersection of artificial intelligence and robotics, with a particular focus on solving one of the field's most persistent computational challenges: inverse kinematics. His research explores how neural network architectures can be applied to determine joint configurations that produce desired end-effector positions in robotic systems — a problem that grows exponentially more complex as robot degrees of freedom increase. Khawandi's most recognized contribution, "Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics" (2010), has garnered 67 citations, establishing it as a meaningful reference point in the robotics and machine learning communities. In this work, he demonstrated that neural networks offer a powerful alternative to traditional geometric and analytical methods, which often struggle with computational demands in complex robotic configurations. His complementary study on neural network systems for inverse kinematics in 3 DOF robotics further reinforced this research direction by addressing the specific challenge of translating Cartesian motion into coordinated joint sequences. Through these contributions, Khawandi helped advance the case for data-driven approaches in robotic control, providing fellow researchers and engineers with practical frameworks for tackling kinematic problems in increasingly sophisticated robotic systems.