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About

Shuang Zhao is a researcher whose work bridges the cutting edge of reinforcement learning, robotics, and medical device design. Her primary research focus lies in developing sensing-aware, model-based reinforcement learning (MBRL) algorithms, where she tackles the critical challenge of enabling AI agents to learn efficiently from raw sensory inputs like images. Her most notable contribution is the **SAM-RL** framework, which integrates differentiable physics-based simulation and rendering to automatically build accurate world models, dramatically improving sample efficiency in complex environments. This work has garnered attention across multiple publications (2022–2024, with a top paper reaching 10 citations), positioning her at the forefront of embodied AI research. Beyond AI, Zhao has made impactful contributions to biomedical engineering. She co-authored an expert consensus on big data collection for skin diseases in Chinese populations (2024, 7 citations) and designed an innovative SMA-embedded clamp for endoscopic surgery (2010, 7 citations), demonstrating her versatility in translating engineering principles into practical medical solutions. Her interdisciplinary portfolio—spanning from differentiable physics to surgical robotics—reflects a rare ability to advance both foundational algorithms and real-world applications.