Mars (Liyao) Gao
Ph.D. student
Paul G. Allen School of Computer Science & Engineering
University of Washington
About me
I am a Ph.D. candidate in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Professor J. Nathan Kutz. My research lies in AI for scientific discovery, with a focus on developing interpretable and generalizable learning frameworks for complex spatiotemporal and dynamical systems. I work at the intersection of symbolic regression, sparse modeling, and deep learning, aiming to uncover governing equations and enable reliable long-term prediction to accelerate scientific understanding. My long-term goal is to build robust machine learning methods that can bridge data and physical laws across domains like physics, climate science, fluid dynamics, neuroscience, and materials science. I am broadly interested in deep learning, statistical learning theory, Bayesian methods, time-series modeling, scientific computing, and recently agentic flow for science.
News
Selected publications
 |
Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks. |
 |
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. |
 |
Convergence of uncertainty estimates in ensemble and Bayesian sparse model discovery. |
 |
Deformation Robust Roto-Scale-Translation Equivariant CNNs. |
 |
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization. |
Sudoku break
Contact
Email: marsgao [at] uw [dot] edu