PeopleofSuperintelligence

Researcher

Christine Allen-Blanchette

Christine Allen-Blanchette is a Princeton robotics researcher whose work brings geometry and physical dynamics into machine learning. Princeton named her to its inaugural Presidential Postdoctoral Research Fellows cohort in 2019. She earned a PhD in computer science at Penn in 2020, completed a master's degree in robotics there, and earned bachelor's degrees in mechanical and computer engineering at San Jose State. Her papers span equivariant image models, Lagrangian video prediction, rigid-body dynamics, Hamiltonian generative video, relational inference, dexterous grasping and parachute dynamics. As of October 2026, she is an assistant professor in mechanical and aerospace engineering and the Center for Statistics and Machine Learning at Princeton, affiliated with Robotics at Princeton.

Works
35
Citations
699
h-index
6

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2015–2018

    Exponential and equivariant models

    In 2015, she coauthors an ICRA paper on the exponential map for similarity transformations and motion interpolation. At ICLR 2018 she coauthors Polar Transformer Networks, which builds rotation and scale equivariance into CNNs, and at ECCV 2018 she coauthors Spherical CNNs for 3D rotation equivariance.

  2. 2019

    A Princeton postdoctoral fellowship

    Princeton names her to its inaugural Presidential Postdoctoral Research Fellows cohort in 2019, and Naomi Leonard mentors her. The appointment builds on her work constraining machine-learning models to maintain compatibility with the underlying structure of variation.

  3. By 2020

    A Penn PhD and engineering degrees

    She earns a PhD in computer science at the University of Pennsylvania in 2020. Her other degrees include a master's degree in robotics at Penn and bachelor's degrees in mechanical engineering and computer engineering at San Jose State.

  4. 2020

    A Lagrangian video prediction model

    In 2020, she coauthors LagNetViP, a Lagrangian neural network for video prediction, with Sushant Veer, Anirudha Majumdar and Naomi Leonard.

  5. 2022

    Rigid-body dynamics from images

    In 2022, she coauthors Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body with Justice Mason, Nicholas Zolman, Elizabeth Davison and Naomi Ehrich Leonard. The model maps images to a latent representation homeomorphic to SO(3), derives angular velocities from latent pairs and predicts future states with Hamiltonian equations.

  6. 2024

    Hamiltonian GAN for video

    In 2024, she introduces Hamiltonian GAN, a video-generation method that uses Hamiltonian formalism as an inductive bias for physically plausible video generation.

  7. 2025

    Learning agent relationships

    At AISTATS in 2025, she coauthors Behavior-Inspired Neural Networks for Relational Inference, which maps agent observations to preferences for latent categories. Its nonlinear opinion-dynamics model identifies mutually exclusive categories, predicts agent evolution over time and controls agent behavior.

  8. 2025

    Geometric algebra for dexterous grasping

    For ICRA in 2025, she coauthors GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping. The framework uses geometric algebra to enforce SE(3) equivariance and adds a differentiable physics-informed refinement layer to make generated grasps physically plausible and stable.

  9. 2026

    Generative parachute dynamics

    At the 28th AIAA Aerodynamic Decelerator Systems Technology Conference in 2026, she coauthors Generating Physically Plausible Parachute Dynamics with Deep Generative Modeling.

  10. As of October 2026

    Assistant professor at Princeton

    As of October 2026, she is an assistant professor in Princeton's Department of Mechanical and Aerospace Engineering and Center for Statistics and Machine Learning, with an affiliation at Robotics at Princeton.

Career

  1. NowPrinceton UniversityAssistant Professor

Education

  1. 2020University of PennsylvaniaPhD
  2. University of PennsylvaniaMaster's degree in Robotics
  3. San Jose State UniversityBachelor's degrees in Mechanical and Computer Engineering

From the biography.

Checked career sources: cablanc.github.io, engineering.princeton.edu, cs.princeton.edu.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. About | Christine Allen-Blanchette cablanc.github.io. Checked 27 October 2026.
  2. Four engineering scholars among Princeton's first Presidential Postdoctoral Research Fellows engineering.princeton.edu. Checked 27 October 2026.
  3. Christine Allen-Blanchette cs.princeton.edu. Checked 27 October 2026.
  4. Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body arxiv.org. Checked 27 October 2026.
  5. Hamiltonian GAN proceedings.mlr.press. Checked 27 October 2026.
  6. Behavior-Inspired Neural Networks for Relational Inference proceedings.mlr.press. Checked 27 October 2026.
  7. GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping gagrasp.github.io. Checked 27 October 2026.
  8. Christine Allen-Blanchette - Princeton University collaborate.princeton.edu. Checked 27 October 2026.
  9. Christine Allen-Blanchette - Princeton Engineering engineering.princeton.edu. Checked 27 October 2026.
  10. Polar Transformer Networks arxiv.org. Checked 27 October 2026.
  11. Learning SO(3) Equivariant Representations with Spherical CNNs arxiv.org. Checked 27 October 2026.
  12. Behavior-Inspired Neural Networks for Relational Inference arxiv.org. Checked 27 October 2026.
  13. GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping arxiv.org. Checked 27 October 2026.
  14. Polar transformer networks collaborate.princeton.edu. Checked 27 October 2026.
  15. Learning SO(3) Equivariant Representations with Spherical CNNs ecva.net. Checked 27 October 2026.