Yeonjong Shin

Yeonjong Shin is an Assistant Professor of Mathematics at North Carolina State University (NC State), with prior tenure-track roles at KAIST (South Korea) and Brown University (USA). His research focuses on applied and computational mathematics, particularly in artificial intelligence, scientific machine learning, and numerical methods for partial differential equations. He holds a Ph.D. in Mathematics from The Ohio State University (2018) and dual B.S. in Mathematics and B.A. in Economics from Yonsei University (2013), with military service as a KATUSA (2009–2011).

Shin’s expertise includes approximation theory, numerical optimization, and uncertainty quantification. He leads research in AI-driven scientific computing, reduced order modeling, and physics-informed neural networks. His work bridges computational mathematics and machine learning, addressing challenges in data-driven dynamical systems and high-dimensional function approximation.

He has published extensively in top journals like SIAM Journals, Neural Networks, and Comput. Methods Appl. Mech. Eng. Notable contributions include thermodynamics-informed latent space dynamics (tLaSDI), S-OPT hyper-reduction algorithms, and error analysis for neural network-based PDE solvers.

Shin has taught courses at NC State, KAIST, and Brown, including statistical inference, numerical optimization, and computational linear algebra. He actively participates in conferences such as SIAM CSE, ICIAM, and KSIAM, and has delivered invited talks at institutions worldwide.