I'm a Ph.D. student at the University of Toronto, supervised by David Duvenaud. Previously, I had been a M.Sc. student under Mark Schmidt. I was first exposed to research as an undergraduate research assistant for Kevin Leyton-Brown.

I work on integrating structured transformations into probabilistic modeling, with the goal of improved interpretability, tractable optimization, or extending into novel areas of application. In terms of fundamental research, I combine numerical simulations, automatic differentiation, and stochastic estimation. I enjoy applying these tools to a variety of application domains, such as normalizing flows, stochastic optimization, and spatio-temporal event modeling.

Curriculum Vitae (CV) / Github / Google Scholar

Research

  • 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
  • Learning Neural Event Functions for Ordinary Differential Equations Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel International Conference on Learning Representations (ICLR). 2021 arxiv | slides | poster
  • Neural Spatio-Temporal Point Processes Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel International Conference on Learning Representations (ICLR). 2021 arxiv | poster
  • 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
  • Inverse design of dissipative quantum steady-states with implicit differentiation Rodrigo A. Vargas-Hernandez, Ricky T. Q. Chen, Kenneth A. Jung, Paul Brumer Workshop on Machine Learning and the Physical Sciences, NeurIPS. 2020 arxiv | poster
  • “Hey, that’s not an ODE”: Faster ODE Adjoints with 12 Lines of Code Patrick Kidger, Ricky T. Q. Chen, Terry Lyons Workshop on Machine Learning and the Physical Sciences, NeurIPS. 2020 arxiv | code
  • 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 | 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
  • Fast Patch-based Style Transfer of Arbitrary Style (ORAL) Tian Qi Chen, Mark Schmidt Workshop in Constructive Machine Learning, NIPS. 2016 arxiv | bibtex | slides | poster | code