Maryam Kouzeghar
Papers
2
Total Citations
47
H-Index
2
About
Maryam Kouzeghar is a rising researcher in the field of multi-agent systems and autonomous robotics, with a primary focus on decentralized coordination and reinforcement learning for unmanned aerial vehicle (UAV) swarms. Her most notable contribution lies in addressing the notoriously difficult problem of multi-target pursuit-evasion, where intelligent evasive targets must be tracked by a team of UAVs. In her highly cited 2023 paper (44 citations), she pioneered the use of deep multi-agent reinforcement learning to enable a heterogeneous swarm of UAVs to learn coordinated behaviors without centralized control. This work demonstrates how individual agents can autonomously decide their roles and actions to effectively surround and pursue multiple targets, a breakthrough with applications in surveillance, search-and-rescue, and defense. Her research bridges the gap between theoretical multi-agent coordination and practical deployment, showcasing how reinforcement learning can solve complex, real-time spatial tasks. With her innovative approach to decentralized swarm intelligence, Kouzeghar is establishing herself as a key contributor to the next generation of autonomous aerial systems, inspiring further work in scalable, adaptive multi-robot teams.
Research Focus
Key Achievements
Top Papers
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- 2