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

Awards & Recognitions

2023 EECS Rising Star, Georgia Tech

Invited Talks

Feb 2026
Collaborative Prediction via Tractable Agreement
ML Scholars Workshop, Mohamed bin Zayed University of Artificial Intelligence
Dec 2025
Collaborative Prediction via Tractable Agreement
Next10 in AI Series, Max Planck Institute for Software Systems
Nov 2025
Collaborative Prediction via Tractable Agreement
TOC4Fairness
Nov 2025
Collaborative Prediction via Tractable Agreement
Computer Science Seminar, Colgate University
Oct 2025
Collaborative Prediction via Tractable Agreement
Symposium on Mathematical Foundations of Trustworthy Learning, ELLIS Research Program
June 2025
Model Ensembling for Downstream Optimization
Foundations of Responsible Computing (FORC)
Mar 2025
Preventing D-Hacking with Adaptive Data Analysis
Center for Technological Responsibility, Reimagination, and Redesign, Brown University
June 2024
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning
ACM Conference on Fairness, Accountability, and Transparency (FAccT)
May 2024
Collaborative Crowdsourced Machine Learning
Stanford Fairness Seminar
Oct 2023
Multicalibration as Boosting for Regression
INFORMS Annual Meeting
Jul 2023
Multicalibration as Boosting for Regression
International Conference on Learning Theory (ICML)
Jun 2023
Multicalibration as Boosting for Regression
Foundations of Responsible Computing (FORC)
Oct 2022
An Algorithmic Framework for Bias Bounties
INFORMS Annual Meeting
Jun 2022
An Algorithmic Framework for Bias Bounties
Oral presentation at ACM Conference on Fairness Accountability and Transparency (FAccT)
Apr 2022
An Algorithmic Framework for Bias Bounties
Reed College Computer Science Colloquium
Oct 2021
Lexicographically Fair Learning: Algorithms and Generalization
Theory of Computation for Fairness Seminar, Simons Institute

Advising

Undergraduate thesis advising for the Reed College computer science department

April Kopec
Scaling Explainable Artificial Intelligence: Filtering and Approximations for Influence Functions for Large Language Models
2023–2024
Becca Luff
Human Understandings of Fairness
2022–2023 · Co-advised with Adam Groce
Differentially Private Weighted Regression
2022–2023 · Co-advised with Leonard Wainstein

Teaching

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)