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About

Dr. Jinshuai Bai is a leading researcher at the intersection of trustworthy artificial intelligence and computational mechanics. Their work is defined by a dual commitment to ensuring AI systems are both reliable and ethically sound, while also advancing physics-informed machine learning for complex engineering problems. Dr. Bai’s most impactful contribution is a comprehensive systematic review on trustworthy and explainable AI in healthcare, which has garnered over 660 citations. This seminal work critically assesses quality, bias risk, and data fusion methods, establishing a foundational framework for developing risk-free AI in high-stakes medical settings. Beyond healthcare, Dr. Bai has pioneered the use of physics-informed neural networks to solve friction-involved nonsmooth dynamics problems, a breakthrough that bridges deep learning with classical mechanics. Their recent exploration of cracking and wrinkling morphomechanics in animal skins further demonstrates a unique ability to apply computational models to biological phenomena. With a growing body of work that spans from algorithmic fairness to solid mechanics, Dr. Bai is shaping the future of safe, explainable, and physically grounded artificial intelligence.