Papers
6
Total Citations
74
H-Index
5
About
Naman Patel is a leading researcher in the intersection of deep learning, autonomous systems, and adversarial robustness. His work primarily focuses on ensuring the safety and reliability of autonomous vehicles and robots, particularly unmanned ground vehicles (UGVs) and unmanned aerial systems (UAS). Patel’s major contributions include pioneering the study of adaptive adversarial attacks on autonomous driving systems, such as his 2019 paper on modifying vehicle trajectories via roadside billboards (19 citations), and developing novel temporal attention architectures for robust sensor fusion in UGV navigation (16 citations). He has also advanced the field of assured autonomy with learning-based real-time anomaly monitoring (14 citations), addressing critical safety concerns in black-box AI systems. Patel’s research on reducing operator workload through multimodal sensor fusion (7 citations) and his analysis of vulnerabilities in perception-based UAS control (5 citations) further underscore his impact. With a career spanning from visual motor control for robotic manipulators to cutting-edge adversarial defense, Patel’s work has garnered significant attention, making him a key figure in the quest for trustworthy autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6