About

I am an Assistant Research Professor at Cornell's Center for Data Science for Enterprise and Society, co-hosted by Nikhil Garg and danah boyd.

My work is grounded in the algorithmic foundations of responsible computing. I broadly study mechanisms to resolve potential harms incurred by AI-driven decision-making, with a focus on techniques that are scalable for real-world use, holistically consider an algorithm in its broader context, and flexibly incorporate human input. I am particularly interested in methods that allow efficient post-deployment model adjustment.

I received my PhD from the University of Pennsylvania, advised by Aaron Roth and Michael Kearns. Prior to Penn, I worked at Boston University as a software engineer with SAIL and with the Harvard Privacy Tools Project. I received my bachelor's degree from Reed College in Portland, OR.

Publications

SODA 2026
Collina, Natalie; Globus-Harris, Ira; Goel, Surbhi; Gupta, Varun; Roth, Aaron; Shi, Mirah
FORC 2025
Globus-Harris, Ira; Gupta, Varun; Kearns, Michael; Roth, Aaron
FAccT 2024
Globus-Harris, Ira; Harrison, Declan; Kearns, Michael; Perona, Pietro; Roth, Aaron
ICML 2023
Globus-Harris, Ira; Harrison, Declan; Kearns, Michael; Roth, Aaron; Sorrell, Jessica
Oral Presentation
AIES 2023
Globus-Harris, Ira; Gupta, Varun; Jung, Christopher; Kearns, Michael; Morgenstern, Jamie; Roth, Aaron
FAccT 2022
Globus-Harris, Ira; Kearns, Michael; Roth, Aaron
Journal of Survey Statistics and Methodology 2022
Drechsler, Joerg; Globus-Harris, Ira; McMillan, Audra; Sarathy, Jayshree; Smith, Adam
FORC 2021
Diana, Emily; Gill, Wesley; Globus-Harris, Ira; Kearns, Michael; Roth, Aaron; Sharifi-Malvajerdi, Saeed
PETS 2019
Swanberg, Marika; Globus-Harris, Ira; Griffith, Iris; Ritz, Anna; Groce, Adam; Bray, Andrew
SOUPS 2019
Qin, Lucy; Lapets, Andre; Jansen, Frederick; Flockhart, Peter; Dak Albab, Kinan; Globus-Harris, Ira
IEEE SecDev 2019
Dak Albab, Kinan; Issa, Rawane; Lapets, Andrei; Flockhart, Peter; Qin, Lucy; Globus-Harris, Ira

Talks

2024
"Diversified Ensembling: An Experiment in Crowdsourced Machine Learning"
Oral presentation at FAccT, June 2024
2024
"Collaborative Crowdsourced Machine Learning"
Stanford Fairness Seminar, May 2024
2023
"Multicalibration as Boosting for Regression"
INFORMS Annual Meeting, October 2023
2023
"Multicalibration as Boosting for Regression"
Oral presentation at ICML, August 2023
2023
"Multicalibration as Boosting for Regression"
Foundations on Responsible Computing (FORC), June 2023
2022
"An Algorithmic Framework for Bias Bounties"
INFORMS Annual Meeting, October 2022
2022
"An Algorithmic Framework for Bias Bounties"
Oral presentation at FAccT, June 2022
2022
"An Algorithmic Framework for Bias Bounties"
Reed College Computer Science Colloquium, April 2022
2021
"Lexicographically Fair Learning: Algorithms and Generalization"
Theory of Computation for Fairness Seminar, Simons Institute, October 2021

Awards & Recognitions

2023 EECS Rising Star, Georgia Tech

Teaching

Fall 2022–Spring 2024 Undergraduate thesis advisor, Reed College
Spring 2022 Head TA & course design — CIS 423/523 Ethical Algorithm Design, University of Pennsylvania
Spring 2022 TA — NETS 412 Algorithmic Game Theory, University of Pennsylvania
Fall 2021 Head TA & course design — CIS 320 Introduction to Algorithms, University of Pennsylvania
2015–2018 TA & tutor — Math 121 Introduction to Programming, Reed College
2017–2018 Instructor & course design — Computer Science Outreach Program
2017 Tutor — Physics 202 Modern Physics, Reed College
2016 Tutor — Physics 201 Oscillations and Waves, Reed College
2015–2016 Tutor — Humanities 110, Reed College

Past Research Affiliations

Amazon AWS intern (Summer 2021–Spring 2022, Summer 2023, Summer 2024)