I’m Chase Goddard, a research scientist at Meta working on generative recommendation systems.
Previously, I was a PhD student in the physics department at Princeton University advised by David Schwab and Bill Bialek. My research focused on generalization and algorithmic capabilities of modern machine learning methods, both in and out of distribution. I 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 data analysis that contributed to the inaugural Fortune Future 50 ranking.
For an up-to-date list of my work, see my Google Scholar, or see below.
Optimization and variability can coexistUnder review, PNAS, May 2025*Authors in alphabetical order.
