I am a postdoc in the Department of Computer Science at The University of Toronto, hosted by Aleksander Nikolov and Nicolas Papernot. I am also a postdoctoral affiliate with the Vector Institute. Previously, I obtained my PhD the Ohio State University, where I was fortunate to be advised by Raef Bassily.

My research focuses on expanding the theoretical foundations of machine learning. I am specifically interested in the design and analysis of machine learning and optimization algorithms which operate under algorithmic constraints, such as privacy, stability, and fairness. My work both characterizes the limits of private learning under such constraints and develops techniuqes for avoiding bottlenecks in trustworthy machine learning by leveraging insights from theory.

Publications

Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry

Raef Bassily, Cristóbal Guzmán, Michael Menart
NeurIPS 2024

Public-data Assisted Private Stochastic Optimization: Power and Limitations

E Ullah, M Menart, R Bassily, C Guzmán, R Arora
NeurIPS 2024

Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates

M Menart, E Ullah, R Arora, R Bassily, C Guzmán
ALT 2024

Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap

R Bassily, C Guzmán, M Menart
COLT 2023

Faster Rates of Convergence to Stationary Points in Differentially Private Optimization

R Arora, R Bassily, T González, C Guzmán, M Menart, E Ullah
ICML 2023

Differentially Private Generalized Linear Models Revisited

Raman Arora, Raef Bassily, Cristóbal Guzmán, Michael Menart, Enayat Ullah
NeurIPS 2022

Differentially private stochastic optimization: New results in convex and non-convex settings

Raef Bassily, Cristóbal Guzmán, Michael Menart
NeurIPS 2021