Neda Masoud
Papers
2
Total Citations
153
H-Index
2
About
Dr. Neda Masoud is a leading researcher at the intersection of intelligent transportation systems, robotics, and cyber-physical systems. Her work focuses on developing data-driven, adaptive frameworks for complex, dynamic environments, particularly in connected and automated vehicles (CAVs) and construction automation. A hallmark of her research is the integration of digital twins with deep reinforcement learning to enable real-time, optimal decision-making. Her most cited paper, "Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction" (2022, 127 citations), exemplifies this, proposing a novel method for coordinating construction robots in response to changing site conditions. Dr. Masoud is also a pioneer in simulation tools for connected vehicle research. Her work "V2XSim: A V2X Simulator for Connected and Automated Vehicle Environment Simulation" (2020, 26 citations) addresses a critical gap by providing researchers with a high-fidelity, accessible platform for testing CAV algorithms without requiring costly real-world testbeds. By bridging the gap between simulation, digital twins, and real-world deployment, Dr. Masoud’s contributions are accelerating the safe and efficient integration of automation into both transportation and construction sectors.
Research Focus
Key Achievements
Top Papers
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