GAO Yihang

GAO Yihang is a Research Fellow in the Department of Mathematics at the National University of Singapore (NUS), working under the supervision of Prof. Vincent Y. F. Tan. He obtained his Ph.D. in Mathematics from The University of Hong Kong (HKU) in 2024 under Prof. Michael K. Ng and holds a B.S. in Mathematics from Zhejiang University (2020).

His educational background:

• Ph.D. in Mathematics, The University of Hong Kong, 2024
• B.S. in Mathematics, Zhejiang University, 2020

GAO Yihang's research spans Scientific Machine Learning, Optimization, Large Language Models, Data Science, and Tensor Computation. His work bridges mathematical theory and machine learning applications, focusing on neural network architectures, optimization algorithms, and generative modeling for scientific computing. Key contributions include theoretical analyses of Kolmogorov-Arnold Networks and efficient transformer frameworks.

His publication record (2022-2025) reveals a strong interdisciplinary trajectory across top mathematics journals and AI conferences. Research trends emphasize physics-informed neural networks, transformer optimization, and tensor methods, with notable work on length extrapolation techniques and Wasserstein-based uncertainty quantification. Collaborations frequently involve HKU and NUS researchers.

Scientific Awards:

• No awards listed in the provided information

No information is available regarding students advised or research grants. His current work appears integrated within Prof. Tan's research group at NUS, focusing on information-theoretic aspects of machine learning.

GAO Yihang maintains active engagement with computational mathematics communities through publications in venues like IEEE Transactions and NeurIPS, with recent work addressing transformer efficiency and neural network convergence properties.