Yuantong Gu
Papers
3
Total Citations
784
H-Index
3
About
Yuantong Gu is a leading researcher at the forefront of trustworthy artificial intelligence and computational mechanics. His work centers on developing safe, reliable, and explainable AI systems, particularly for high-stakes applications like healthcare. Gu’s landmark 2023 systematic review on trustworthy and explainable AI in healthcare has garnered over 666 citations, establishing a critical framework for assessing quality, bias risk, and data fusion in medical AI. This contribution is complemented by his influential paper on risk-free trustworthy AI, which outlines essential requirements for deploying algorithmic decision-making across education, business, and justice sectors. Demonstrating remarkable versatility, Gu also advances computational methods through physics-informed neural networks, tackling complex friction-involved nonsmooth dynamics problems. His research bridges the gap between theoretical AI safety and practical engineering challenges, earning recognition for shaping the discourse on responsible AI development. With growing citation impact and interdisciplinary reach, Gu’s work continues to guide researchers and practitioners toward more robust, transparent, and ethically sound intelligent systems.
Research Focus
Key Achievements
Top Papers
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