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

[all] [scholar]

Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration [arXiv]
Parikshit Gopalan, MPK, Omer Reingold
NeurIPS 2023

Is your model predicting the past? [arXiv] [github]
Moritz Hardt and MPK
EAAMO 2023

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

Universal Adaptability: Target-Independent Inference that Competes with Propensity Scoring
MPK, Christoph Kern, Shafi Goldwasser, Frauke Kreuter, Omer Reingold
PNAS 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


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