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About

Zehong Cao is a dynamic researcher whose work bridges artificial intelligence, brain-computer interfaces (BCIs), and human-centered computing. His interdisciplinary expertise spans neuroscience-driven AI, deep reinforcement learning, fuzzy logic systems, and multi-robot coordination, positioning him at the frontier of intelligent systems research. Cao's most influential contribution — a comprehensive review of AI for EEG-based BCIs (2020, 80 citations) — has become a key reference for researchers exploring how the human brain can interact with external environments through machine learning. Complementing this, his work on human preference-guided deep reinforcement learning (2021, 58 citations) advances reward-learning frameworks by introducing nuanced, dynamic human feedback, moving beyond rigid trajectory comparisons to more flexible and realistic supervision signals. His research on interpretable fuzzy logic controllers for multi-robot navigation (2021, 31 citations) demonstrates a commitment to making AI systems not only effective but also explainable and deployable in real-world scenarios. This theme extends to his editorial leadership on fuzzy systems and human-explainable AI, reflecting his growing influence in shaping research agendas. Across his body of work, Cao consistently champions human-centric intelligence — designing systems that are transparent, adaptive, and meaningfully responsive to human input.