Shuang Liang

I am a PhD student in the Statistics Department at UCLA. I work on mathematical theory for deep learning.

I am fortunate to be advised by Guido Montúfar.

liangshuang at g.ucla.edu / Google scholar / Short CV

My current research focuses on optimization in neural networks. Specifically, I aim to better understand:

The optimization trajectory in parameter space;
The implicit bias of the optimization algorithm (which model the algorithm tends to select);
How these aspects are influenced by network architecture, optimizer, initialization, step size, etc.

Previously, I studied persistent homology, a data representation method in topological data analysis.

Implicit Bias of Mirror Flow for Shallow Neural Networks in Univariate Regression.
Shuang Liang, Guido Montúfar. ICLR 2025 (Spotlight). Preprint [arXiv:2410.03988]. Virtual poster [SlidesLive].
Pull-back Geometry of Persistent Homology Encodings.
Shuang Liang, Renata Turkeš, Jiayi Li, Nina Otter, Guido Montúfar. TMLR 2024. Preprint [arXiv:2310.07073]. Repo [GitHub]. Video [Video].

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