Aqeel Anwar

Papers

Total Citations

H-Index

About

Aqeel Anwar is a leading researcher at the intersection of autonomous systems, fault-tolerant computing, and bio-inspired vision. His work is driven by a fundamental question: how can we make learning-based navigation systems both robust and efficient enough for real-world deployment? In his highly cited 2021 paper (22 citations), Anwar systematically analyzed and improved the fault tolerance of neural network accelerators used in drones and unmanned vehicles, addressing critical vulnerabilities to transient and permanent hardware faults—a key step toward reliable autonomy. He further pushed the boundaries of edge intelligence with his 2022 work (11 citations) on bio-mimetic target localization, fusing frame-based and event-based vision to achieve high-speed, low-latency tracking inspired by predatory biological systems. By combining deep learning with neuromorphic sensing, Anwar’s research enables robotic systems to localize fast-moving targets with unprecedented speed and efficiency, all while operating on resource-constrained edge devices. His contributions are shaping the next generation of resilient, high-performance autonomous systems, bridging the gap between biological inspiration and practical engineering for applications in robotics, unmanned vehicles, and beyond.