I'm a Research Scientist at Meta Fundamental AI Research (FAIR) team in New York. I think about how to generate things. My research is on building simplified abstractions of the world through the lens of dynamical systems and flows.
Lately, I've been exploring insertion-based sequence generation [Edit Flows], investigating scaling laws and new capabilities for multimodal generation [OneFlow]. Among my research outputs, data-driven methods such as [Flow Matching] have been applied successfully by many for foundation models of video and audio [Movie Gen, SD3, etc]. Additionally, reward-driven methods such as [Adjoint Matching] have been applied to large-scale diffusion finetuning for internal GenAI models and AI & Chemistry [AS/ASBS].
CV | Github | Twitter | Google Scholar | rtqichen@gmail.com
Selected Research
- OneFlow: Concurrent Mixed-Modal And Interleaved Generation with Edit Flows Preprint. 2025 arxiv | site
- Edit Flows: Flow Matching with Edit Operations Advances in Neural Information Processing Systems (NeurIPS). 2025 arxiv
- Adjoint Schrödinger Bridge Sampler (ORAL) Advances in Neural Information Processing Systems (NeurIPS). 2025 arxiv
- Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching International Conference on Machine Learning (ICML). 2025 arxiv | code | models
- Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective (ORAL) International Conference on Learning Representations (ICLR). 2025 arxiv
- Generator Matching: Generative modeling with arbitrary Markov processes (ORAL) International Conference on Learning Representations (ICLR). 2025 arxiv
- Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control (SPOTLIGHT) International Conference on Learning Representations (ICLR). 2025 arxiv
- Discrete Flow Matching Advances in Neural Information Processing Systems (NeurIPS). 2024 arxiv
- Flow Matching Guide and Code Arxiv. 2024 arxiv | code
- FlowMM: Generating Materials with Riemannian Flow Matching International Conference on Machine Learning (ICML). 2024 arxiv | code
- Generalized Schrödinger Bridge Matching International Conference on Learning Representations (ICLR). 2024 arxiv | code
- Flow Matching on General Geometries (OUTSTANDING PAPER HONORABLE MENTION) International Conference on Learning Representations (ICLR). 2024 arxiv | code
- Flow Matching for Generative Modeling (SPOTLIGHT) International Conference on Learning Representations (ICLR). 2023 arxiv
- Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization International Conference on Learning Representations (ICLR). 2021 arxiv | bibtex | code
- Learning Neural Event Functions for Ordinary Differential Equations International Conference on Learning Representations (ICLR). 2021 arxiv | bibtex | slides | poster
- SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models (SPOTLIGHT) International Conference on Learning Representations (ICLR). 2020 arxiv | bibtex | poster | colab
- Residual Flows for Invertible Generative Modeling (SPOTLIGHT) Advances in Neural Information Processing Systems (NeurIPS). 2019 arxiv | bibtex | slides | talk | poster | code
- Invertible Residual Networks (LONG ORAL) International Conference on Machine Learning (ICML). 2019 arxiv | bibtex | code
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FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
(ORAL)
(BEST STUDENT PAPER @ AABI 2018) International Conference on Learning Representations (ICLR). 2019 arxiv | bibtex | poster | code - Neural Ordinary Differential Equations (BEST PAPER AWARD) Advances in Neural Information Processing Systems (NeurIPS). 2018 arxiv | bibtex | slides | poster | code