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About

Naveed Akhtar is a leading researcher at the intersection of artificial intelligence, robotics, and autonomous systems. His work spans three key areas: large language models (LLMs), adversarial robustness in deep learning, and reliable robotic task execution. Akhtar’s most impactful contribution is his comprehensive overview of large language models, which has already garnered over 465 citations since 2025, establishing itself as a foundational reference in the rapidly evolving field of natural language processing. In robotics, he has pioneered methods for detecting pixel-level adversarial attacks on neural networks, a critical challenge for deploying deep learning in real-world autonomous systems. His research on naive physics-based fault reasoning has advanced the reliability of mobile manipulators, enabling robots to handle unexpected environmental deviations without sensor or actuator failure. Akhtar has also contributed to high-definition LiDAR mapping for autonomous driving and developed UnLoc, a universal localization method that fuses LiDAR, radar, and camera data for robust vehicle navigation. His work on informative map point selection for visual-inertial SLAM further demonstrates his commitment to practical, deployable solutions. With a career spanning foundational theory and applied engineering, Akhtar continues to shape how intelligent systems perceive, reason, and act in complex environments.