Visiting Research Professor, Dongguk University–Seoul
Postdoctoral Researcher, InnoCORE PRISM-AI Center, KAIST
Pioneering data-driven methods for dynamic material constitutive characterization — from rigorous flow stress modeling of metals under extreme strain rates to AI-driven safety assessment of lithium-ion batteries under impact loading.
Unified by a shared methodology — combining experimental mechanics with data-driven computational approaches across different material and structural systems.
Highlights spanning constitutive modeling theory, impact mechanics prediction, and battery safety — all published in JCR Q1 journals.
Dr. Huang received his Ph.D. from the University of Manchester (2024) under Prof. Q.M. Li, where he developed foundational data-driven methods for dynamic material characterization. His trilogy of papers in the International Journal of Impact Engineering — rigorously verifying the Johnson-Cook equation and establishing ANN+SVD/CP frameworks for flow stress determination — forms the core of his academic identity in impact dynamics.
Currently at KAIST and Dongguk University, he applies this expertise to lithium-ion battery impact safety — characterizing failure modes, developing deep learning frameworks for real-time damage detection, and investigating multiphysics pack stability. Impact dynamics and constitutive modeling remain the core research identity; battery safety is the current application domain where mechanical expertise drives new contributions.