Sarah El Kazdadi

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About

Sarah El Kazdadi is a leading researcher in optimization for robotics, whose work bridges the gap between high-performance algorithms and real-time control. Her primary research areas include trajectory optimization, convex quadratic programming (QP), and numerical methods for robotic motion planning and control. El Kazdadi is best known for her foundational work on "Equality Constrained Differential Dynamic Programming" (2021, 23 citations), which advanced trajectory optimization by providing robust convergence guarantees for task-based robot motion planning—a critical tool for smoothing probabilistic paths and enabling direct control. She is also the lead developer of ProxQP (2025, 9 citations), an efficient and versatile quadratic programming solver designed to meet the demanding real-time requirements of robotics, including whole-body controllers and estimation algorithms. ProxQP’s speed and reliability have made it a valuable resource for the broader engineering community, addressing the growing need for high-frequency QP solutions. El Kazdadi’s contributions are shaping the future of autonomous systems, making complex optimization accessible and practical for real-world robotic applications.