Mars (Liyao) Gao

Ph.D. student
Paul G. Allen School of Computer Science & Engineering
University of Washington

GitHub Google Scholar

News

About me

I am currently a Ph.D. student in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. I am very fortunate to be advised by Professor J. Nathan Kutz on sparse regression and deep learning methods for equation discovery. I am passionate about developing generalizable and interpretable learning frameworks that address practical challenges and enhance our understanding of the world. My interests include deep learning, statistical learning theory, and Bayesian methods. I aspire to apply these techniques to solve complex scientific problems in real-world scenarios.

Selected publications

Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks. Mars L. Gao, Jan P. Williams, J. Nathan Kutz. [GitHub] [Colab] [Website] [Youtube] arXiv, 2025.

Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. Mars L. Gao, J. Nathan Kutz. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

Convergence of uncertainty estimates in ensemble and Bayesian sparse model discovery. Mars L. Gao, Urban Fasel, Steven L. Brunton, J. Nathan Kutz. In submission.

Deformation Robust Roto-Scale-Translation Equivariant CNNs. Mars L. Gao, Wei Zhu, Guang Lin. Transaction of Machine Learning Research.

On Optimal Early Stopping: Over-informative versus Under-informative Parametrization. Ruoqi Shen, Mars L. Gao, Yi-An Ma. arxiv.

Contact

Email: marsgao [at] uw [dot] edu