I’m a PhD student in the Machine Learning Department at Carnegie Mellon University, co-advised by Aaditya Ramdas and Giulia Fanti, and a member of the StatML group.
I’m broadly interested in the algorithmic and statistical aspects of modern machine learning. I work on problems that I find both practically relevant and intellectually challenging, with research spanning areas such as optimization and generative modeling, primarily under differential privacy constraints.
Previously, I obtained my MSc from the Institute for Mathematical and Computational Engineering at the Catholic University of Chile, advised by Cristobal Guzman. I was also a Student Researcher at Google working with Courtney Paquette and Fabian Pedregosa.
Research
- Private Evolution Converges
Tomas Gonzalez, Giulia Fanti, Aaditya Ramdas
NeurIPS 2025 - Sequentially Auditing Differential Privacy
Tomas Gonzalez, Mateo Dulce-Rubio, Aaditya Ramdas, Monica Ribero
NeurIPS 2025 - Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
Tomas Gonzalez, Cristobal Guzman, Courtney Paquette
SIAM Journal on Optimization 2025 (To appear)
COLT 2024 (Extended abstract) - Faster Rates of Convergence to Stationary Points in Differentially Private Optimization
Raman Arora, Raef Bassily, Tomas Gonzalez, Cristobal Guzman, Michael Menart, Enayat Ullah
ICML 2023