about

My research investigates foundational questions about responsible machine learning. Much of this work aims to identify problematic behaviors that emerge in machine-learned models and to develop algorithmic tools that provably mitigate such behaviors. More broadly, I am interested in how the theory of computation can provide insight into emerging societal and scientific challenges.

Prior to Cornell, I was a Miller Postdoctoral Fellow at UC Berkeley, hosted by Shafi Goldwasser.
I completed my Ph.D. in the Stanford Theory Group under the guidance of Omer Reingold.

selected publications

Near-Optimal Algorithms for Omniprediction [arXiv]
Princewill Okoroafor, Robert Kleinberg, MPK
Preprint 2025

Certifying Private Probabilistic Mechanisms [IACR]
Zoe Ruha Bell, Shafi Goldwasser, MPK, Jean-Luc Watson
CRYPTO 2024

Planting Undetectable Backdoors in Machine Learning Models [arXiv] [Quanta]
Shafi Goldwasser, MPK, Vinod Vaikuntanathan, Or Zamir
FOCS 2022

Outcome Indistinguishability [arXiv] [ECCC]
Cynthia Dwork, MPK, Omer Reingold, Guy N. Rothblum, Gal Yona
STOC 2021

Multicalibration: Calibration for the (Computationally-Identifiable) Masses [arXiv]
Úrsula Hébert-Johnson, MPK, Omer Reingold, Guy N. Rothblum
ICML 2018

teaching

CS 6828: Foundations of Responsible Machine Learning [Fall 2024]

CS 4820: Introduction to Analysis of Algorithms [Spring 2024] [Spring 2025]