Jinane Mounsef
Papers
6
Total Citations
58
H-Index
4
About
Jinane Mounsef is a rising researcher at the intersection of robotics, artificial intelligence, and education, whose work is shaping how autonomous systems perceive, navigate, and interact with dynamic environments. Her most impactful contribution is a comprehensive survey of Visual SLAM methods (36 citations), which provides a critical roadmap for enabling robots to map and localize themselves in real-world settings—a foundational challenge in modern robotics. Mounsef’s research extends into multi-robot collaboration, where she has developed frameworks for coordinated manipulation in obstacle-dense spaces using deep learning, and into adaptive navigation, proposing deep reinforcement learning solutions for emergency response and crowd dynamics. She also addresses practical industrial challenges, such as optimizing event-based Visual Inertial Odometry for high dynamic range scenarios. Beyond technical robotics, Mounsef is pioneering personalized education through her CARE framework, which integrates AI and robotics to create adaptive tutoring systems for elementary students. With a growing portfolio of recent publications in 2025, her work is gaining traction for its practical focus on real-world deployment, from logistics to classrooms, establishing her as a versatile innovator in autonomous systems and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1A Survey of Visual SLAM Methods36 citations · 2023
- 2CARE: towards customized assistive robot-based education9 citations · 2025
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