Christine Allen-Blanchette

Christine Allen-Blanchette is an Assistant Professor in the Department of Mechanical and Aerospace Engineering and affiliated with the Center for Statistics and Machine Learning at Princeton University. She also collaborates with Robotics at Princeton and previously held a Princeton Presidential Postdoctoral Fellowship.

• Education: PhD in Computer Science (2020), MSE in Robotics (2013) from the University of Pennsylvania; dual BS degrees in Mechanical Engineering and Computer Engineering (2011) from San Jose State University.

Her research focuses on the intersection of deep learning, geometry, and dynamical systems. Key areas include control theory, robotics, and geometric deep learning, with applications to dexterous manipulation, 3D rotational dynamics, and equivariant neural architectures.

Recent publications emphasize geometric algebra-based models for robotics, equivariant autoencoders for fluid dynamics, and physics-informed generative modeling. Her work integrates domain-specific constraints into neural architectures to enhance interpretability and performance.

• Scientific Awards:
  • Princeton Presidential Postdoctoral Fellow

Christine investigates connections between opinion dynamics and graph neural networks while advancing surrogate modeling and reward guidance methods in reinforcement learning. She contributes to robotics and machine learning communities through interdisciplinary research.