I'm a Research Scientist at FAIR, Meta, in New York.

My research is on building simplified abstractions of the world through the lens of dynamical systems and flows.

I generally work on integrating structured transformations into probabilistic modeling, with the goal of improved interpretability, tractable optimization, or extending into novel areas of application.

Recently I've been focusing my research on building simulation-free methods for learning dynamical systems that model data generation processes.

CV | Github | Twitter | Google Scholar | rtqichen@meta.com

Research

(See Google Scholar for exhaustive list.)
  • FlowMM: Generating Materials with Riemannian Flow Matching Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, Brandon M. Wood International Conference on Machine Learning (ICML). 2024 arxiv
  • Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models Neta Shaul, Uriel Singer, Matthew Le, Ricky T. Q. Chen, Ali Thabet, Albert Pumarola, Yaron Lipman International Conference on Machine Learning (ICML). 2024 arxiv
  • Bespoke Solvers for Generative Flow Models (SPOTLIGHT) Neta Shaul, Juan Perez, Ricky T. Q. Chen, Ali Thabet, Albert Pumarola, Yaron Lipman International Conference on Learning Representations (ICLR). 2024 arxiv
  • Generalized Schrödinger Bridge Matching Guan-Horng Liu, Yaron Lipman, Maximilian Nickel, Brian Karrer, Evangelos A Theodorou, Ricky T. Q. Chen International Conference on Learning Representations (ICLR). 2024 arxiv | code
  • Flow Matching on General Geometries (OUTSTANDING PAPER HONORABLE MENTION) Ricky T. Q. Chen, Yaron Lipman International Conference on Learning Representations (ICLR). 2024 arxiv | code
  • Distributional GFlowNets with Quantile Flows Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron Courville, Yoshua Bengio Transactions on Machine Learning Research (TMLR). 2024 arxiv | code
  • Stochastic Optimal Control Matching Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen Preprint. 2023 arxiv | code
  • Multisample Flow Matching: Straightening Flows with Minibatch Couplings Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, Ricky T. Q. Chen International Conference on Machine Learning (ICML). 2023 arxiv
  • On Kinetic Optimal Probability Paths for Generative Models Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matt Le, Yaron Lipman International Conference on Machine Learning (ICML). 2023 arxiv
  • Flow Matching for Generative Modeling (SPOTLIGHT) Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le International Conference on Learning Representations (ICLR). 2023 arxiv
  • Latent State Marginalization as a Low-cost Approach for Improving Exploration Dinghuai Zhang, Aaron Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen International Conference on Learning Representations (ICLR). 2023 arxiv | code
  • Neural Conservation Laws: A Divergence-Free Perspective Jack Richter-Powell, Yaron Lipman, Ricky T. Q. Chen Advances in Neural Information Processing Systems (NeurIPS). 2022 arxiv | bibtex | code
  • Semi-Discrete Normalizing Flows through Differentiable Tessellation Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel Advances in Neural Information Processing Systems (NeurIPS). 2022 arxiv | bibtex | poster | code
  • Matching Normalizing Flows and Probability Paths on Manifolds Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel, Ricky T.Q. Chen, Yaron Lipman International Conference on Machine Learning (ICML). 2022 arxiv | bibtex
  • Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud International Conference on Artificial Intelligence and Statistics (AISTATS). 2022 arxiv | bibtex | code
  • Fully differentiable optimization protocols for non-equilibrium steady states Rodrigo A Vargas-Hernández, Ricky T. Q. Chen, Kenneth A Jung, Paul Brumer New Journal of Physics. 2021 arxiv | bibtex | publisher link | code
  • “Hey, that's not an ODE”: Faster ODE Adjoints via Seminorms Patrick Kidger, Ricky T. Q. Chen, Terry Lyons International Conference on Machine Learning (ICML). 2021 arxiv | bibtex | code
  • Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron Courville International Conference on Learning Representations (ICLR). 2021 arxiv | bibtex | code
  • Learning Neural Event Functions for Ordinary Differential Equations Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel International Conference on Learning Representations (ICLR). 2021 arxiv | bibtex | slides | poster
  • Neural Spatio-Temporal Point Processes Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel International Conference on Learning Representations (ICLR). 2021 arxiv | bibtex | poster | code
  • Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering (ORAL) Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig Workshop on "I Can't Believe It's Not Better!", NeurIPS. 2020 arxiv | bibtex | slides | poster | talk
  • Scalable Gradients for Stochastic Differential Equations Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud International Conference on Artificial Intelligence and Statistics (AISTATS). 2020 arxiv | bibtex | code
  • SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models (SPOTLIGHT) Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen International Conference on Learning Representations (ICLR). 2020 arxiv | bibtex | poster | colab
  • Neural Networks with Cheap Differential Operators (SPOTLIGHT) Ricky T. Q. Chen, David Duvenaud Advances in Neural Information Processing Systems (NeurIPS). 2019 arxiv | bibtex | slides | talk (@9:45) | poster | code
  • Residual Flows for Invertible Generative Modeling (SPOTLIGHT) Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen Advances in Neural Information Processing Systems (NeurIPS). 2019 arxiv | bibtex | slides | talk | poster | code
  • Latent ODEs for Irregularly-Sampled Time Series Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud Advances in Neural Information Processing Systems (NeurIPS). 2019 arxiv | bibtex | code | talk
  • Invertible Residual Networks (LONG ORAL) Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen International Conference on Machine Learning (ICML). 2019 arxiv | bibtex | code
  • FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models (ORAL)
    (BEST STUDENT PAPER @ AABI 2018)
    Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud International Conference on Learning Representations (ICLR). 2019 arxiv | bibtex | poster | code
  • Neural Ordinary Differential Equations (BEST PAPER AWARD) Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud Advances in Neural Information Processing Systems (NeurIPS). 2018 arxiv | bibtex | slides | poster | code
  • Isolating Sources of Disentanglement in Variational Autoencoders (ORAL) Ricky T. Q. Chen, Xuechen Li, Roger Grosse, David Duvenaud Advances in Neural Information Processing Systems (NeurIPS). 2018 arxiv | bibtex | slides | talk | poster | code