Christine Allen-Blanchette

Christine Allen-Blanchette is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Princeton University. She holds an affiliation with Robotics at Princeton and is associated with the Center for Statistics and Machine Learning. Her research interests include Control, Robotics and Dynamical Systems. Prior to her current position, she was part of the inaugural class of Princeton Presidential Postdoctoral Fellows and was mentored by Naomi Leonard. She completed her PhD in Computer Science and Master’s in Robotics at the University of Pennsylvania, where she was supervised by Kostas Daniilidis. Before her graduate studies, she earned Bachelor’s degrees in Mechanical Engineering and Computer Engineering at San Jose State University.

Christine Allen-Blanchette is an Assistant Professor at Princeton University specializing in computer vision. Her research focuses on developing equivariant representations and neural networks that leverage geometric principles to improve the robustness and interpretability of machine learning models. Her work explores spherical CNNs, polar transformer networks, and equivariant multi-view networks for tasks such as 3D object classification, video prediction, and dynamic alignment calibration. She also investigates Hamiltonian GANs, behavior-inspired neural networks, and methods for resolving oversmoothing in graph neural networks. Her research emphasizes the integration of geometric and physical principles into deep learning architectures to enhance performance in complex, real-world scenarios.