School of Computer Science
Carnegie Mellon University
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
Computing program), and affiliated appointments with
the Machine Learning
Interaction Institute. I am also affiliated with
the CyLab and
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, Apple, 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.
Seven papers accepted at NeurIPS 2022.
Our paper Policy impacts of statistical uncertainty and privacy is now published in Science.
I am giving several talks this summer: Google's FACT 2022 Conference, Google's FL seminar, the Privacy workshop at the Fields Institute in Toronto, the IMS Annual Meeting, and Baidu Research AI Colloquium.
I gave a mini course on private synthetic data at the BU Summer School on Differential Privacy.
Seven papers accepted at ICML 2022.
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)
Conference Program Committee
NeurIPS 2022, 2021, 2020 (area chair)
ICML 2022, 2020 (area chair)
ICLR 2022, 2021, 2020 (area chair)
AISTATS 2021 (area chair)
FAccT 2019, 2021 (area chair)
TheWebConf 2020, 2018
EC 2020, 2019, 2018
Workshop Program Committee
TPDP 2021, 2019
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.
Of Moments and Matching: Trade-offs and Treatments in Imitation Learning
Simons Institute Workshop on Adversarial Approaches in Machine Learning
Involving Stakeholders in Building Fair ML Systems
Foundations of Algorithmic Fairness Workshop
IDEAL Quarterly Theory Workshop: Algorithms and their Social Impact
Trustworthy ML Initiative (TrustML) Seminar