Steven Wu

Assistant Professor
Computer Science & Engineering
University of Minnesota

Email: zstevenwu [at]

I am an Assistant Professor of Computer Science & Engineering at the University of Minnesota. I received my PhD in Computer Science from the University of Pennsylvania, where I was extremely fortunate to have been co-advised by Michael Kearns and Aaron Roth. My doctoral dissertation received Penn's Morris and Dorothy Rubinoff Award for best thesis. Before joining the UMN, I spent a year as a post-doctoral researcher at Microsoft Research-New York City in the Machine Learning and Algorithmic Economics groups.

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 social and economic dynamics, such as strategic interactions, influence machine learning. I study these questions using methods and models from machine learning, statistics, optimization, differential privacy, game theory, and mechanism design.

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

Please see my CV and publications for more details.


August 2020

After two amazing years at UMN, I will be joining the School of Computer Science at Carnegie Mellon University this fall!

July 2020

Come learn about privacy at!

June 2020

Six papers accepted at ICML'20. Congratulations to Giuseppe Vietri on his three papers!

May 2020

Three papers accepted at EC'20, COLT'20, and DIS'20.

Jan 2020

One NSF proposal awarded (as PI) in the NSF Program on Fairness in Artificial Intelligence (FAI) in Collaboration with Amazon.

Nov 2019

I gave a talk on "Between Individual and Group Fairness" at the event Three Decades of DIMACS: The Journey Continues. Video

Oct 2019

I gave a tutorial on "Differential Privacy Techniques Beyond Differential Privacy" at the FOCS workshop "A TCS Quiver". Slides


PhD students

Hao-Fei Cheng (co-advised by Haiyi Zhu)
Giuseppe Vietri (co-advised by Maria Gini)
Daniel Ngo
Logan Stapleton


Spring 2020

Fall 2019

Fall 2018


Program Committee Member

NeurIPS 2020 (area chair)
ICML 2020 (area chair)
ICLR 2021, 2020 (area chair)
AISTATS 2021 (area chair)
ALT 2021
FORC 2020
FAT* 2019 (track co-chair)
TheWebConf 2020, 2018
EC 2020, 2019, 2018
TPDP 2019
HAI-GEN 2020


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.

Workshop Organizer

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


Nov 2019

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

Oct 2019

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