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 Software and Societal Systems Department (with 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.

My broad research interests are in algorithms and machine learning. These days I am excited about:

  • Foundations of responsible AI, with emphasis on privacy and fairness considerations.
  • Interactive learning, including contextual bandits and reinforcement learning, and its interactions with causal inference and econometrics.
  • Economic aspects of machine learning, with a focus on learning in the presence of strategic agents.
My research has been generously supported by the National Science Foundation (NSF), the Okawa Foundation, CMU’s Block Center for Technology and Society, an Amazon Research Award, a Google Faculty Research Award, J.P. Morgan Faculty Awards, Meta Research awards, a Mozilla Research Grant, Apple, and Cisco Research.

For more details, please see my Papers and CV


Jan 2024

I am teaching a new course this spring semester: 17757: Modern Techniques in Uncertainty Quantification.

Dec 2023

Sep 2023

June 2023

March 2023

Our CMU team ("puffle") led by Ken Liu won the 1st place at the U.S. Privacy-Enhancing Technologies (PETs) Prize Challenge, Pandemic Forecasting Track (USD $100,000). See coverage by the White House, UK Government, Summit for Democracy, DrivenData, NSF, and CMU news.

March 2023


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

PhD students

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 Nina Balcan)
Gokul Swamy (co-advised by Drew Bagnell)
Luke Guerdan (co-advised by Ken Holstein)
Terrance Liu
Jingwu Tang (co-advised by Fei Fang)
Anusha Sinha (co-advised by Hoda Heidari)


Pratiksha Thaker, CBI Fellow, (co-advised by Virginia Smith)

Undergrad/Masters Students

Jiahao Zhang (visiting undergrad from Peking U.)
Yuqi Pan (visiting undergrad from Peking U.)
Jiayun Wu (visiting masters student from Tsinghua)


Giuseppe Vietri (PhD student), now at Amazon
Hao-Fei Cheng (PhD student), now at Amazon
Shuran Zheng (Post-doc), now on the IIIS faculty at Tsinghua University

Xin Gu (MS), now a PhD student at Penn State
Ken Ziyu Liu (MS), now a PhD student at Stanford
Konwoo Kim (undergrad, winner of the Allen Newell Award), soon joining Jump Trading
Xinyan Hu (visiting undergrad from Peking U.), now a PhD student at UC Berkeley
Leijie Wang (visiting undergrad from Tsinghua), now a PhD student at Univ. of Washington

Jocelyn Chou (REU student from Rutgers)
Diana Qing (REU student from Berkeley)
Harry Tian (REU student from Carleton), now a PhD student at Purdue
Grace Tian (REU student from Harvard), now an ML engineer at Hive
Allen Marquez (REU student from Cal State LA), now a software engineer at Northrop Grumman


Spring 2024

Fall 2023

Fall 2022

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 2024, 2022, 2021, 2020 (area chair)
AISTATS 2021 (area chair)
COLT 2024, 2023 (SPC)
ITCS 2022
SODA 2022
ALT 2021
FORC 2020, 2024
FAccT 2019, 2021, 2023 (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