Jamie Morgenstern

Assistant Professor in the School of Computer Science
at Georgia Tech
Office: 2136 Klaus

Email: 'jamiemmt' 'dot' 'cs' 'at' 'gatech' 'dot' 'edu'

I am an assistant professor in the School of Computer Science Georgia Tech. Prior to this appointment, I was fortunate to be hosted by Michael Kearns, Aaron Roth, and Rakesh Vohra as a Warren Center fellow at the University of Pennsylvania. I completed my PhD working with Avrim Blum at Carnegie Mellon University. I study the social impact of machine learning and the impact of social behavior on ML's guarantees. How should machine learning be made robust to behavior of the people generating training or test data for it? How should ensure that the models we design do not exacerbate inequalities already present in society?


I'm very excited to point you to my new paper, Predictive Inequity in Object Detection, joint with Benjamin Wilson and Judy Hoffman. Code can be found here.


I'm very fortunate to be advising the following excellent students:
Bhuvesh Kumar (PhD, SCS, joint with Jake Abernethy)
Yuanyuan (Chloe) Yang<, ISYE ACO)
Benjamin Wilson (MS)
Angel (Alex) Cabrera, (BS)
Varun Gupta, (BS)
Dhamma Kimpara, (BS)


I'm excited to serve as general cochair for FAT* 2019, which will take place in Atlanta! For 2018, I'm on the PC for EC, ICML, FAT*, WWW, and ALT. In 2017, I was on the PC for EC, ICML, NetEcon, and FAT/ML. I also served on the EC PC in 2016.


I have been fortunate to be supported by the Simons Award for Graduate Students in Theoretical Computer Science (2014-2016), an NSF GFRP fellowship, as well as the Microsoft Research Graduate Women's Scholarship.

Working papers

Datasheets for Datasets

I'm very excited about a project I've been working on with Timnit Gebru, Briana Vecchione, Jennifer Wortman Vaughn, Hanna Wallach, Hal Daume III, and Kate Crawford. We're proposing transparency and standardization of the documentation accompanying datasets. The working paper is here!


Ph.D, Market Algorithms: Incentives, Learning, and Privacy (Defended May 2015)
Original CMU Tech Report (Not updated for typos)


NeurIPS 2018 The Price of Fair PCA: One Extra Dimension. Samira Samadi, Uthaipon Tantipongpipat, Mohit Singh Jamie Morgenstern, and Santosh Vempala.
NeurIPS 2018 A Smoothed Analysis of the Greedy Algorithm. Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, and Steven Wu.
ICML 2017 Fair Learning in Markovian Environments. Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth.
EC 2017Fairness Incentives for Myopic Agents. Sampath Kannan, Michael Kearns, Jamie Morgenstern, Mallesh Pai, Aaron Roth, Rakesh Vohra, and Zhiwei Steven Wu.arxiv
WINE 2016 Strategic Network Formation with Attack and Immunization Sanjeev Goyal, Shahin Jabbari, Michael Kearns, Sanjeev Khanna, Jamie Morgenstern.arxiv
NeurIPS 2016 Fairness in Learning: Classic and Contextual Bandits. Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth.arxiv
EC 2016Simple Mechanisms for Agents with Complements Michal Feldman, Ophir Friedler, Jamie Morgenstern, Guy Reinerarxiv
COLT 2016Learning Simple Auctions Jamie Morgenstern, Tim Roughgardenarxiv
STOC 2016Do Prices Coordinate Markets? (short version)Justin Hsu, Jamie Morgenstern, Ryan Rogers, Aaron Roth, Rakesh Vohraarxiv
NeurIPS 2015 The Pseudo-Dimension of Nearly Optimal Auctions

Selected for a spotlight presentation, along with 3.6% of submissions.

Jamie Morgenstern and Tim Roughgardenarxiv
EC 2015Private Pareto-Optimal ExchangeSampath Kannan, Jamie Morgenstern, Ryan Rogers, and Aaron Rotharxiv
EC 2015Simple Auctions with Simple StrategiesNikhil Devanur, Jamie Morgenstern, Vasilis Syrgkanis, S. Matthew Weinberg
EC 2015Learning What's Going On: Reconstruction Preferences and Priorities from Opaque TransactionsAvrim Blum, Yishay Mansour, Jamie Morgensternarxiv
IJCAI 2015Impartial Peer ReviewDavid Kurokawa, Omer Lev, Jamie Morgenstern, Ariel Procaccia
SODA 2015Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy)Sampath Kannan, Jamie Morgenstern, Aaron Roth, Steven Wuarxiv
AAAI 2015Learning Valuation Distributions from Partial ObservationAvrim Blum, Yishay Mansour, Jamie Morgensternarxiv
ITCS 2015Privacy-preserving Public Information in Sequential GamesAvrim Blum, Jamie Morgenstern, Ankit Sharma, Adam Smitharxiv
AAAI 2013How Bad is Selfish Voting?Simina Brânzei, Ioannis Caragiannis, Jamie Morgenstern, Ariel D. Procaccia
COSN 2013 Hierarchical community decomposition via oblivious routing techniques William Sean Kennedy, Jamie Morgenstern, Gordon Wilfong, Lisa Zhang
APPROX 2012Additive Approximation for Near-Perfect Phylogeny ConstructionPranjal Awasthi, Avrim Blum, Jamie Morgenstern, Or Sheffet
AAAI 2012On Maxsum Fair Cake DivisionsSteven J. Brams, Michal Feldman, John K. Lai, Jamie Morgenstern, Ariel D. Procaccia
STM 2011A proof-carrying File system with Revocable and Use-once Certificates (Conference on Security and Trust Management)Jamie Morgenstern, Deepak Garg, Frank Pfenning
ICFP 2010Security-typed Programming Within Dependently Typed ProgrammingJamie Morgenstern, Dan Licata