Cathy Wu

Massachusetts Institute of Technology, Decision Systems (United States)

Papers

Total Citations

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About

Cathy Wu is a leading researcher at the intersection of control theory, reinforcement learning, and intelligent transportation systems. Her work focuses on developing scalable, data-driven algorithms for the optimal control of emerging vehicular systems, particularly in mixed-autonomy environments where connected and automated vehicles (CAVs) interact with human-driven vehicles. Wu’s major contributions include pioneering the application of deep reinforcement learning (DRL) to nonlinear dynamical systems for traffic control, as demonstrated in her highly cited 2022 paper “Unified Automatic Control of Vehicular Systems With Reinforcement Learning” (54 citations). She has also advanced multi-agent pathfinding frameworks for cooperative autonomous driving and mixed-autonomy traffic coordination, addressing critical challenges in intersection management and urban mobility. Her research bridges robotics and transportation engineering, offering practical solutions for congestion mitigation and efficiency gains. With a growing citation impact and recognition for her innovative approaches, Wu is shaping the future of autonomous driving systems, making her work essential reading for students and researchers in control, robotics, and intelligent transportation.