Working papersCommittee for the Study of Digital Platforms, Market Structure and Antitrust Subcommittee Report, joint with Fiona Scott Morton (chair), Theodore Nierenberg, Pascal Bouvier, Ariel Ezrachi, Bruno Jullien, Roberta Katz, Gene Kimmelman, and A. Douglas Melamed.
Predictive Inequity in Object Detection, joint with Benjamin Wilson and Judy Hoffman. Code can be found here. News coverage in Vox, Businesss Insider, The Guardian, NBC News.
Datasheets for Datasets , 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.
MentoringI'm very fortunate to be advising the following excellent students:
Bhuvesh Kumar (PhD, SCS, joint with Jake Abernethy)
Yuanyuan (Chloe) Yang
Jie (Claire) Zhang
Benjamin Wilson (MS)
Angel (Alex) Cabrera, (BS)
Varun Gupta, (BS)
Dhamma Kimpara, (BS)
ServiceIn 2020, I am an area chair for ICML, on the SPC for EC, ICLR, and COLT. I served as general cochair for FAT* 2019, which took place in Atlanta! In 2019, I served as an SPC member for EC and ICML. For 2018, I was 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.
FundingI 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.
|AISTATS 2020||Equalized odds postprocessing under imperfect group information.||Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern.|
|AIES (AI, Ethics and Society) 2020||Diversity and Inclusion in Subset Selection.||Alex Hanna, Dylan Baker, Emily Denton, Nyalleng Moorosi, Ben Hutchinson, Timnit Gebru, Meg Mitchell, Jamie Morgenstern.|
|NeurIPS 2019||Learning Auctions with Incentive Guarantees.||Jacob Abernethy, Rachel Cummings, Bhuvesh Kumar, Jamie Morgenstern, Samuel Taggart.|
|NeurIPS 2019||Multi-Criteria Dimensionality Reduction with Applications to Fairness||Jamie Morgenstern, Samira Samadi, Mohit Singh, Uthaipon Tantipongpipat, Santosh Vempala.|
|VIS 2019||FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning.||Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng Chau.|
|ICML 2019||Guarantees for Spectral Clustering with Fairness Constraints.||Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern.|
|ICML 2019||Fair k-center clustering for data summarization.||Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern.|
|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||Fairness in Reinforcement Learning.||Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth.|
|EC 2017||Fairness 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 2016||Simple Mechanisms for Agents with Complements||Michal Feldman, Ophir Friedler, Jamie Morgenstern, Guy Reiner||arxiv|
|COLT 2016||Learning Simple Auctions||Jamie Morgenstern, Tim Roughgarden||arxiv|
|STOC 2016||Do Prices Coordinate Markets? (short version)||Justin Hsu, Jamie Morgenstern, Ryan Rogers, Aaron Roth, Rakesh Vohra||arxiv|
|NeurIPS 2015||The Pseudo-Dimension of Nearly Optimal Auctions
Selected for a spotlight presentation, along with 3.6% of submissions.
|Jamie Morgenstern and Tim Roughgarden||arxiv|
|EC 2015||Private Pareto-Optimal Exchange||Sampath Kannan, Jamie Morgenstern, Ryan Rogers, and Aaron Roth||arxiv|
|EC 2015||Simple Auctions with Simple Strategies||Nikhil Devanur, Jamie Morgenstern, Vasilis Syrgkanis, S. Matthew Weinberg|
|EC 2015||Learning What's Going On: Reconstruction Preferences and Priorities from Opaque Transactions||Avrim Blum, Yishay Mansour, Jamie Morgenstern||arxiv|
|IJCAI 2015||Impartial Peer Review||David Kurokawa, Omer Lev, Jamie Morgenstern, Ariel Procaccia|
|SODA 2015||Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy)||Sampath Kannan, Jamie Morgenstern, Aaron Roth, Steven Wu||arxiv|
|AAAI 2015||Learning Valuation Distributions from Partial Observation||Avrim Blum, Yishay Mansour, Jamie Morgenstern||arxiv|
|ITCS 2015||Privacy-preserving Public Information in Sequential Games||Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam Smith||arxiv|
|AAAI 2013||How 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 2012||Additive Approximation for Near-Perfect Phylogeny Construction||Pranjal Awasthi, Avrim Blum, Jamie Morgenstern, Or Sheffet|
|AAAI 2012||On Maxsum Fair Cake Divisions||Steven J. Brams, Michal Feldman, John K. Lai, Jamie Morgenstern, Ariel D. Procaccia|
|STM 2011||A proof-carrying File system with Revocable and Use-once Certificates (Conference on Security and Trust Management)||Jamie Morgenstern, Deepak Garg, Frank Pfenning|
|ICFP 2010||Security-typed Programming Within Dependently Typed Programming||Jamie Morgenstern, Dan Licata|