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About

Yong Deng is a leading researcher whose work bridges fuzzy systems, evidence theory, and intelligent robotics, with a focus on managing uncertainty in complex environments. His key contributions include the development of a fuzzy sensor fusion methodology based on evidence theory, which enhances the reliability of multisensor systems by combining complementary observations under uncertainty. This foundational work, published in 2013, has garnered 42 citations and remains influential in signal processing and decision-making. Deng also introduced a novel divergence measure for basic probability assignments, advancing information fusion in extremely uncertain contexts—a critical tool for pattern classification and decision analysis. More recently, he has ventured into bio-inspired robotics, co-developing the SonoRotor, an acoustic rotational platform for manipulating zebrafish embryos and larvae, which opens new avenues for on-chip biological microscopy. As a guest editor for a special issue on fuzzy systems toward human-explainable AI, Deng is shaping the next generation of human-centric, interpretable artificial intelligence. His work, cited across diverse fields, demonstrates a sustained impact on both theoretical foundations and practical applications in uncertain information processing and intelligent systems.