Chase Goddard
PhD student at Princeton University
I’m Chase Goddard, a PhD student in the physics department at Princeton University advised by David Schwab and Bill Bialek. My research has focused on understanding how modern machine learning methods work, particularly questions related to optimization and generalization. Much of my work relies on controlled experiments that are simple enough to be theoretically well-understood, but complex enough to still provide practical understanding of the complex phenomena observed in deep learning. I’ve also taught several courses at the graduate and undergraduate level.
Before Princeton, I majored in physics and computer science at Cornell University, where I worked with Carl Franck on X-ray spectroscopy and with Julia Thom-Levy on the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider. I also held an internship at Boston Consulting Group’s Henderson Institute, where I worked on a data analysis project that contributed to the Fortune Future 50 ranking.
I am currently working on understanding & improving reasoning in LLMs via reinforcement learning, as well as understanding global geometric properties of the loss landscape of overparameterized models. Stay tuned!
For an up-to-date list of my work, see my Google Scholar, or see below.
Publications
2025
July
- Preprint
Optimization and variability can coexistMay 2025*Authors in alphabetical order. To be submitted.
