Ahmed Hatem

Papers

Total Citations

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About

Ahmed Hatem is a researcher whose work bridges the critical intersection of 3D perception and autonomous navigation. His primary research areas include point cloud processing, meta-learning for domain adaptation, and model predictive control (MPC) for autonomous vehicles. Hatem’s most notable contribution addresses a fundamental bottleneck in robotics: the poor quality of data from affordable 3D scanners. In his 2023 work on test-time adaptation for point cloud upsampling, he pioneered a meta-learning approach that allows neural networks to dynamically adjust to sparse, non-uniform point clouds at inference time—a significant leap over static architectures that fail in real-world conditions. This paper has already garnered 7 citations, signaling its growing influence in the field. Earlier, Hatem explored the control side of autonomy with his 2018 study on MPC for path tracking and obstacle avoidance, demonstrating how predictive control can safely navigate vehicles in complex environments. While this work has 3 citations, it laid the groundwork for his current trajectory. By tackling both the perception and control challenges of autonomous systems, Hatem is helping to make robust, low-cost robotics a practical reality.