Mars (Liyao) Gao

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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

[July 2025] General exam passed! New
[July 2025] PySHRED paper out led by amazing David Ye now out on arXiv!! Just pip install pyshred! [arXiv] New
[June 2025] Transformer-SHRED paper led by amazing Alexey Yermakov now out on arXiv!! [arXiv] New
[May. 2025] Mesh-free SINDy paper collab and advised by amazing Bernat Font now out on arXiv!! [PDF] New
[Apr. 2025] Invited talk @ UCSB Applied Math seminar, UW CS4Env, and MIT in Marin Soljačić’s group.
[Mar. 2025] Our newest work "Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks" with isotropic flow and convex loss landscape visualization is now available on arXiv! [Website] [Colab] [Github] New
[Oct. 2024] Invited talk @ Georgia Tech ACMS seminar.
[Mar. 2024] Our paper "Bayesian autoencoders for data-driven discovery of coordinates, governing equations, and fundamental constants," is now published in PRSA!

Selected publications

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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.

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Convergence of uncertainty estimates in ensemble and Bayesian sparse model discovery.
Mars L. Gao, Urban Fasel, Steven L. Brunton, J. Nathan Kutz.
Under review at Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

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

Contact

Email: marsgao [at] uw [dot] edu