Professor Christine Allen-Blanchette
Assistant Professor, Mechanical and Aerospace Engineering, Princeton University
Time: 1:00 pm – 2:00 pm
Date: Friday, October 24, 2025
Location: BC 220
Abstract: Scientists and engineers are increasingly applying deep neural networks (DNNs) to modelling and design of complex systems. While the flexibility of DNNs makes them an attractive tool, it also makes their solutions difficult to interpret and their predictive capability difficult to quantify. In contrast, scientific models directly expose the equations governing a process but their applicability is restricted in the presence of unknown effects or when the data are high-dimensional. The emerging paradigm of physics-guided artificial intelligence asks: How can we combine the flexibility of DNNs with the interpretability of scientific models to learn relationships from data consistent with known scientific theories? In this talk, I will discuss my work on incorporating prior knowledge of problem structure (e.g., physics-based constraints) into neural network design. I will demonstrate how prior knowledge of task symmetries can be leveraged for improved learning outcomes, and how appropriately structured learning algorithms can be useful in scientific contexts.
Bio: Christine Allen-Blanchette is an assistant professor in the Department of Mechanical and Aerospace Engineering, and Center for Statistics and Machine Learning at Princeton University. They hold an associated faculty appointment in the Computer Science department and an affiliation with Robotics at Princeton. Before joining the faculty, they were a Princeton Presidential Postdoctoral Fellow mentored by Naomi Leonard. They completed their PhD in Computer Science and MSE in Robotics at the University of Pennsylvania, and their BS degrees in Mechanical and Computer Engineering at San Jose State University. Among their awards are the Princeton Presidential Postdoctoral Fellowship, NSF Integrative Graduate Education and Research Training award, and GEM Fellowship sponsored by the Adobe Foundation.