Guan-Horng Liu

Research Scientist @ FAIR (Meta AI)

Hi, I am Guan-Horng Liu (I go by "Guan"), a Research Scientist in FAIR, NYC.

I study fundamental algorithms for learning diffusion models with optimality structures. I’m actively contributing to nonlinear diffusion models—mainly Schrödinger Bridge and Mirror Diffusion— and large-scale methods for applications in generative modeling, image restoration, unpaired image translation, watermarked generation, opinion depolarization, and single-cell RNA sequencing. Prior to this, I worked on robust architecture-aware neural optimizers.

I’m generally interested in integrating optimality/domain structures into diffusion and flow models, with the goals of enhancing theoretical understanding and developing large-scale algorithms for novel applications. In terms of fundamental research, I combine dynamic optimal transport, stochastic optimal control, and statistical physics. I enjoy applying these tools to a variety of scientific and machine learning problems.

I’m extremely fortunate to intern in FAIR Lab, Meta and Nvidia Research during 2023 and 2022 Summer, working with many talented researchers, including (FAIR) Ricky T. Q. Chen, Yaron Lipman, Maximilian Nickel, Brian Karrer, (Nvidia) Weili Nie, Arash Vahdat, Anima Anandkumar, De-An Huang, (Google DeepMind) Valentin De Bortoli, and (Georgia Tech) Molei Tao. In Georgia Tech, I am advised by Evangelos Theodorou.

See my full CV here (updated in July 2025).

Contact: ghliu [at] meta [dot] com
Follow: Google Scholar | LinkedIn | ghliu | @guanhorng_liu

Updates
[07/2025] I will give a talk at MSR Generative Modeling & Sampling Seminar.
[05/2025] Adjoint Sampling accepted to ICML.
[09/2024] I have joined FAIR, Meta AI as a Research Scientist at NYC.
[04/2024] I co-organize ICML workshop on Structured Probabilistic Inference & Generative Modeling (SPIGM).
[02/2024] Generalized Schrödinger Bridge Matching (with FAIR, Meta AI) accepted to ICLR.
[09/2023] Two papers, Mirror Diffusion and Momentum Schrödinger Bridge, accepted to NeurIPS.
[05/2023] I have joined FAIR Lab, Meta AI as a research scientist intern this summer.
[04/2023] I co-organize ICML workshop on New Frontiers in Learning, Control, and Dynamical Systems.
[03/2023] Image-to-Image Schrödinger Bridge (with Nvidia Research) accepted to ICML.
[09/2022] Deep Generalized Schrödinger Bridge accepted to NeurIPS (Oral 1.9%).
[02/2022] Likelihood training of Schrödinger Bridge accepted to ICLR.
[09/2021] Three papers on architecture-aware neural optimizers accepted to NeurIPS (Spotlight 3.0%), ICML (Oral 3.0%), and ICLR (Spotlight 3.8%).
Selected Publications
(∗ Equal contribution, * Core contributors, † Equal advising, ‡ Alphabetical order. See Google Scholar for the full list.)
  1. Adjoint Schrödinger Bridge Sampler
    Guan-Horng Liu*, Jaemoo Choi*, Yongxin Chen, Benjamin K. Miller, Ricky T. Q. Chen*
    preprint, 2025
    arxiv
  2. Non-equilibrium Annealed Adjoint Sampler
    Jaemoo Choi*, Yongxin Chen, Molei Tao, Guan-Horng Liu*
    preprint, 2025
    arxiv
  3. Momentum Multi-Marginal Schrödinger Bridge Matching
    Panagiotis Theodoropoulos, Augustinos D. Saravanos, Evangelos A. Theodorou†, Guan-Horng Liu†
    preprint, 2025
    arxiv
  4. Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
    Aaron Havens*, Benjamin Kurt Miller*, Bing Yan*, Carles DomingoEnrich, Anuroop Sriram, Brandon Wood, Daniel Levine, Bin Hu, Brandon Amos, Brian Karrer, Xiang Fu*, Guan-Horng Liu*, Ricky T. Q. Chen*
    International Conference on Machine Learning (ICML), 2025
    arxiv
  5. React-OT: Optimal Transport for Generating Transition State in Chemical Reactions
    Chenru Duan∗, Guan-Horng Liu∗, Yuanqi Du∗, Tianrong Chen, Qiyuan Zhao, Haojun Jia, Carla P. Gomes, Evangelos Theodorou, Heather J. Kulik
    Nature Machine Intelligence, 2025
    arxiv
  6. Improving Generative Model-based Unfolding with Schrödinger Bridges
    Sascha Diefenbacher‡, Guan-Horng Liu‡, Vinicius Mikuni‡, Benjamin Nachman‡, Weili Nie‡
    Physical Review D, 2024
    arxiv | code
  7. Generalized Schrödinger Bridge Matching
    Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos Theodorou, Ricky T. Q. Chen
    International Conference on Learning Representations (ICLR), 2024
    arxiv | code
  8. Mirror Diffusion Models for Constrained and Watermarked Generation
    Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou†, Molei Tao†
    Advances in Neural Information Processing Systems (NeurIPS), 2023
    arxiv | code
  9. I²SB: Image-to-Image Schrödinger Bridge
    Guan-Horng Liu, Arash Vahdat, De-An Huang, Evangelos Theodorou, Weili Nie, Anima Anandkumar
    International Conference on Machine Learning (ICML), 2023
    arxiv | code | project
  10. Deep Generalized Schrödinger Bridge
    Guan-Horng Liu, Tianrong Chen, Oswin So, Evangelos Theodorou
    Advances in Neural Information Processing Systems (NeurIPS), 2022   [Oral 1.9%]
    arxiv | code
  11. Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory
    Tianrong Chen∗, Guan-Horng Liu∗, Evangelos Theodorou
    International Conference on Learning Representations (ICLR), 2022
    arxiv | code
  12. Second-order Neural ODE Optimizer
    Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou
    Advances in Neural Information Processing Systems (NeurIPS), 2021   [Spotlight 3.0%]
    arxiv | code
  13. Dynamic Game Theoretic Neural Optimizer
    Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou
    International Conference on Machine Learning (ICML), 2021   [Long talk 3.0%]
    arxiv
  14. DDPNOpt: Differential Dynamic Programming Neural Optimizer
    Guan-Horng Liu, Tianrong Chen, Evangelos Theodorou
    International Conference on Learning Representations (ICLR), 2021   [Spotlight 3.8%]
    arxiv