Yinan Huang

Ph.D. student, Machine Learning, Georgia Institute of Technology Google Scholar    CV    Linkedin E-mail: yhuang903@gatech.edu

About

I am a fourth-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 geometric deep learning and generative modeling of structured systems such as graphs and temporal data.

I was a research intern at NEC Lab America working with Martin Min, and a research intern at Peking University working with Prof. Muhan Zhang. I earned my M.S. degree in ECE at Duke University and B.S. degree in Physics at Sun Yat-sen University.

News

2026/10 Gave a talk at Center for Signal and Information Processing (CSIP) Seminar, Georgia Tech.

2026/6 Started to work as a research intern on protein design at NEC Lab America.

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

• Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective
  Yinan Huang, Hans Hao-Hsun Hsu, Junran Wang, Bo Dai, Pan Li
  ICLR 2026 ReALM–GEN Workshop (Spotlight)

• Powers 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) 2026

• What Are Good Positional Encodings for Directed Graphs?
  Yinan Huang, Haoyu Wang, Pan Li.
  ICLR 2025

• On the Stability of Expressive Positional Encodings for Graphs
  Yinan Huang*, William Lu*, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, Pan Li
  ICLR 2024

• Boosting the Cycle Counting Power of Graph Neural Networks with I2-GNNs
  Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang
  ICLR 2023

• 3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
  Yinan Huang, Xingang Peng, Jianzhu Ma, Muhan Zhang
  ICML 2022 (Oral).