Assistant Professor
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
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
I gave a talk at the Data Privacy: Foundations and Applications Reunion at the Simons Institute.
Feb 2022
I gave a talk on imitatoin learning at the Simons Workshop on Adversarial Approaches in Machine Learning.
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.
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)
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
NIPSML4H18
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.
Other
I co-organize differentialprivacy.org.
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
YouTube
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
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
YouTube
Panopto
Mar 2021
Feb 2021
Leveraging Heuristics in Private Synthetic Data Generation
CMU Crypto/Applied crypto seminar
PPAI workshop 2021
Boston-area Data Privacy Seminar
PPAI Video
CMU Cryto Video
Oct 2019
Tutorial: Differential Privacy Techniques Beyond Differential
Privacy
FOCS
2019 workshop
A TCS Quiver
Slides