Steven Wu

Assistant Professor
School of Computer Science
Carnegie Mellon University

Email: zstevenwu [at]
Office: TCS Hall 424

I am an Assistant Professor in the School of Computer Science at Carnegie Mellon University, with my primary appointment in the Institute for Software Research (in the Societal Computing program), and affiliated appointments with the Machine Learning Department and the Human-Computer Interaction Institute. I am also affiliated with the CyLab and the Theory Group.

I work on algorithms and machine learning. My recent work focuses on (1) how to make machine learning better aligned with societal values, especially privacy and fairness, and (2) how to make machine learning more reliable and robust when algorithms interact with social and economic dynamics. I study these questions using methods and models from learning theory, optimization, statistics, differential privacy, game theory, mechanism design, and human-computer interaction. For more details, please see my publications.

My research has been generously supported by the National Science Foundation (NSF), the Okawa Foundation, an Amazon Research Award, a Google Faculty Research Award, a J.P. Morgan Faculty Award, Facebook Research awards, a Mozilla Research Grant, and Cisco Research.

Previously, I received a Ph.D. in Computer Science in 2017 from the University of Pennsylvania, where I was extremely fortunate to have been co-advised by Michael Kearns and Aaron Roth. My doctoral dissertation titled "Data Privacy Beyond Differential Privacy" received Penn's Morris and Dorothy Rubinoff Award for best thesis. Before joining CMU, I was an Assistant Professor of Computer Science & Engineering at the University of Minnesota for two years. Before that, I spent a year as a post-doctoral researcher at Microsoft Research-New York City in the Machine Learning and Algorithmic Economics groups. Please see my CV for more details.


April 2022

Two papers accepted at FAccT and one paper accepted at DIS.

March 2022

Feb 2022

I was invited to be a panelist at the PPAI 2022 workshop at AAAI.

Feb 2022

Feb 2022

Two papers accepted at CHI and one paper accepted at AAAI.

Dec 2021

I will be giving invited talks at CMStatistics 2021 and the StratML workshop at NeurIPS 2021.

Nov 2021

I will be giving invited talks at the 2021 FCSM Research and Policy Conference and Google’s Federated Learning Workshop.

Sep 2021

Two papers got accepted at NeurIPS'21.

Aug 2021

Our paper got accepted by Operations Research!


I am extremely fortunate to be able to work with several excellent students.

PhD students

Giuseppe Vietri (co-advised by Maria Gini)
Daniel Ngo (co-advised by Maria Gini)
Logan Stapleton (co-advised by Haiyi Zhu)
Justin Whitehouse (co-advised by Aaditya Ramdas)
Keegan Harris (co-advised by Hoda Heidari)
Gokul Swamy (co-advised by Drew Bagnell)
Luke Guerdan (co-advised by Ken Holstein)

BS/MS Students

Terrance Liu
Vicky Hu (visiting student from Peking Univ.)


Hao-Fei Cheng, now at Amazon


Fall 2021

Spring 2021

Fall 2020

Spring 2020

Fall 2019

Fall 2018


Conference Program Committee

NeurIPS 2022, 2021, 2020 (area chair)
ICML 2022, 2020 (area chair)
ICLR 2022, 2021, 2020 (area chair)
AISTATS 2021 (area chair)
ITCS 2022
SODA 2022
ALT 2021
FORC 2020
FAccT 2019, 2021 (area chair)
TheWebConf 2020, 2018
EC 2020, 2019, 2018

Workshop Program Committee

TPDP 2021, 2019
HAI-GEN 2020

Workshop Organizer

Intersectionality in Fair Machine Learning: Where Are We and Where Should We Go from Here? MOSAIC 2020: An Annual Conference on Intersectionality. Nov. 1, 2020.

Recent Developments in Research on Fairness. The Simons Institute for the Theory of Computing, Berkeley, CA. July 8-10, 2019.


I co-organize
During my undergraduate, I was involved in the Bard Prison Initiative as a math tutor at the Eastern New York Correctional Facility. Check out this amazing four-part documentary film series, College Behind Bars, about this initiative.


Feb 2022

Of Moments and Matching: Trade-offs and Treatments in Imitation Learning
Simons Institute Workshop on Adversarial Approaches in Machine Learning

Mar 2021

A Geometric View on Private Gradient-Based Optimization
Federated Learning One World Seminar (FLOW)
Google TechTalks
FLOW Video Google Video

Mar 2021

Feb 2021

Mar 2021
Feb 2021

Nov 2019

Between Individual and Group Fairness
Three Decades of DIMACS: The Journey Continues

Oct 2019

Tutorial: Differential Privacy Techniques Beyond Differential Privacy
FOCS 2019 workshop A TCS Quiver

Jan 2018

Competing Bandits: Learning under Competition
ITCS 2018

Aug 2017

Meritocratic Fairness for Cross-Population Selection
ICML 2017

June 2017

Fairness Incentives for Myopic Agents
EC 2017

June 2017

Multidimensional Dynamic Pricing for Welfare Maximization
EC 2017