Babak Rahmani

École Polytechnique Fédérale de Lausanne

Papers

Total Citations

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About

Babak Rahmani is a researcher at the forefront of intelligent control systems, with a focus on robust nonlinear dynamics and the intersection of machine learning with physical hardware. His most cited work, "Competing Neural Networks for Robust Control of Nonlinear Systems" (2019), introduces a novel framework where multiple neural networks are pitted against one another to enhance the stability and adaptability of complex systems—from robotic arms with multiple actuators to optical systems like laser speckle pattern manipulation. This approach addresses a critical challenge: controlling systems where only output measurements are accessible, bypassing the need for detailed internal models. While his citation count is still building, Rahmani’s work is pioneering in its application of competitive learning to real-world control problems, offering a scalable and robust alternative to traditional methods. His research bridges theoretical advances in neural network design with practical engineering, promising significant impact in robotics, adaptive optics, and beyond. For students and researchers, Rahmani’s contributions exemplify how cutting-edge AI can be harnessed to tame the complexity of physical systems.