Yinan Huang
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Ph.D. student, Machine Learning, Georgia Institute of Technology |
About
I am a third-year Ph.D. student in Machine Learning at Georgia Tech, supervised by Prof. Pan Li. I am broadly interested in AI for science (developing data-driven methods for accelerating scientific discovery) and Science of AI (understanding the foundations and principles that govern modern AI systems). My current research is focused on
generative models: diffusion models and flow matching, and their applications in spatio-temporal modeling, and decision-making.
geometric deep learning: machine learning on graph/structured data, equivariant neural networks.
Prior to that, I earned my M.S. degree in ECE at Duke University and B.S. degree in Physics at Sun Yat-sen University. I also spent one year as a research intern at Peking University, supervised by Prof. Muhan Zhang.
News
2026/3 Our paper about graph SSM is accepted by TPAMI.
2026/2 Our paper Powers of Magnetic Graph Matrix: Fourier Spectrum, Walk Compression, and Applications is accepted by PNAS! A very interesting work studying the connection between random walk on directed networks and the Fourier transform of the Magnetic Laplacian.
Selected Publications & Preprints
Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective
Yinan Huang, Hans Hao-Hsun Hsu, Junran Wang, Bo Dai, Pan Li
arXiv:2602.05319Powers of Magnetic Graph Matrix: Fourier Spectrum, Walk Compression, and Applications
Yinan Huang, David F. Gleich, Pan Li
The Proceedings of the National Academy of Sciences (PNAS) 2026What Are Good Positional Encodings for Directed Graphs?
Yinan Huang, Haoyu Wang, Pan Li.
ICLR 2025On the Stability of Expressive Positional Encodings for Graphs
Yinan Huang*, William Lu*, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, Pan Li
ICLR 2024Boosting the Cycle Counting Power of Graph Neural Networks with I2-GNNs
Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang
ICLR 20233DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang
ICML 2022 (Oral).