Juan Felipe Gomez

Welcome! I am a fifth year PhD student in physics at Harvard University advised by Flavio Calmon and funded by the Department of Energy Computational Science Graduate Fellowship.

In the summers of 2023-2025, I worked as a member of the Princeton Plasma Physics Lab and Center for Statistics and Machine Learning at Princeton, where I was advised by William Tang. Previously, I was a Caltech undergraduate in Physics and grew up in Atlanta, Georgia.

My PhD research is focused on privacy-preserving machine learning, motivated by the following claims:

• Machine learning datasets often contain sensitive information, making it crucial to address privacy concerns and prevent adversaries from exploiting model outputs to identify individuals.
• Rigorous and interpretable privacy definitions are needed to ensure privacy guarantees, high model accuracy, and foster public trust.

Differential privacy has emerged as a framework that directly addresses these challenges. As such, my research focuses on advancing the theory and practice of differential privacy.

There are many surprising connections between differential privacy and physics. I (along with some amazing coauthors!) explored some of these connections in: The saddle-point method in differential privacy and Schrödinger mechanisms: Optimal differential privacy mechanisms for small sensitivity.

I have also worked on the trustworthiness of large language models, mainly in the role randomness plays in high stakes downstream tasks such as toxicity detection. While at Princeton, I worked on large-scale distributed pre-training of a foundation-scale model for clean-energy fusion applications. We presented some of our preliminary results at Super Computing 2024. See our presentation here!