Moomal Qureshi
Papers
1
Total Citations
32
H-Index
1
About
Moomal Qureshi is a rising researcher at the intersection of artificial intelligence, cybernetics, and autonomous robotics. Her work focuses on developing robust neural network models for attitude estimation and cyber-physical security in micro-aerial vehicles (MAVs). Her most-cited paper, "Feature Matching and Deep Learning Models for Attitude Estimation on a Micro-Aerial Vehicle" (2022, 32 citations), tackles the critical challenge of protecting robotic systems from both destructive and non-destructive cyber-attacks in the digital era. By integrating feature matching techniques with deep learning architectures, Qureshi has advanced the reliability of autonomous navigation under adversarial conditions. Her research addresses a pressing gap in cybernetics—how to safeguard aerial robots against malicious interference while maintaining precise attitude control. With her work gaining traction in the robotics and cybersecurity communities, Qureshi is establishing herself as a key contributor to safer, more resilient autonomous systems. Her findings hold significant implications for defense, surveillance, and commercial drone operations, where security and accuracy are paramount.
Research Focus
Key Achievements
Top Papers
- 1