Weihan Jiang

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About

Weihan Jiang is a researcher advancing the theory and application of inverse optimal control (IOC), with a focus on data-driven decision-making and human behavior modeling. Their key research areas include optimal control, inverse reinforcement learning, and stochastic systems. In their highly regarded work, "Statistically Consistent Inverse Optimal Control for Linear-Quadratic Tracking with Random Time Horizon" (2022, 3 citations), Jiang tackles the fundamental challenge of recovering an agent’s underlying objective function from observed optimal trajectories. This contribution is significant because it provides a statistically rigorous framework for inferring expert intent—a critical step in designing autonomous systems that learn from human demonstrations. By ensuring consistency in the presence of random time horizons, Jiang’s approach enhances the reliability of IOC in real-world applications such as robotics, autonomous driving, and human-robot interaction. Their work bridges the gap between theoretical control theory and practical, data-driven policy design, offering a principled method for modeling complex behaviors. Jiang’s research is particularly valuable for students and researchers seeking to understand how machines can learn optimal objectives from observation, making them a rising voice in the intersection of control and learning.