Yahong Yang
yyang66@binghamton.eduBiography
Yahong Yang received a PhD in mathematics from the Hong Kong University of Science and Technology in 2023. Yang was a postdoctoral scholar at Penn State University from 2023 to 2026 and a visiting assistant professor at the Georgia Institute of Technology from 2025 to 2026.
Yang’s research focuses on the mathematical foundations of deep learning for partial differential equations, including neural network approximation, statistical learning theory, and operator learning. Yang also develops mathematical models and computational methods for applications in materials science and biology.
Publications
- Wenrui Hao, Rui Peng Li, Yuanzhe Xi, Tianshi Xu, and Yahong Yang. “Multiscale Neural Networks for Approximating Green’s Functions.” SIAM Journal on Scientific Computing, 2026.
- Yahong Yang, Haizhao Yang, and Yang Xiang. “Nearly optimal VC-dimension and pseudo-dimension bounds for deep neural network derivatives.” Advances in Neural Information Processing Systems (NeurIPS), 2023.
- Chuqi Chen, Yahong Yang, Yang Xiang, and Wenrui Hao. “Automatic differentiation is essential in training neural networks for solving differential equations.” Journal of Scientific Computing, 2025.
- Wenrui Hao, Xinliang Liu, and Yahong Yang. “Newton informed neural operator for solving nonlinear partial differential equations.” Advances in Neural Information Processing Systems (NeurIPS), 2024.
- Chuqi Chen, Yahong Yang, Yang Xiang, and Wenrui Hao. “Learn singularly perturbed solutions via homotopy dynamics.” International Conference on Machine Learning (ICML), 2025.
Education
- PhD in mathematics, the Hong Kong University of Science and Technology, 2023
Research Interests
- Deep learning methods for solving partial differential equations
- Approximation and statistical learning theory of neural networks
- Neural operators and efficient training methods
- Mathematical modeling and simulation in materials science and biology