Aaron Roth

Photo of me

About Me

I am the class of 1940 Bicentennial Term Associate Professor of Computer and Information Science at the University of Pennsylvania computer science department, associated with the theory group, PRiML (Penn Research in Machine Learning) the Warren Center for Network and Data Sciences, and am co-director of our program in Networked and Social Systems Engineering. I am also affiliated with the AMCS program (Applied Mathematics and Computational Science). I spent a year as a postdoc at Microsoft Research New England. Before that, I received my PhD from Carnegie Mellon University, where I was fortunate to have been advised by Avrim Blum. My main interests are in algorithms and machine learning, and specifically in the areas of private data analysis, fairness in machine learning, game theory and mechanism design, and learning theory. I am the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE), an Alfred P. Sloan Research Fellowship, an NSF CAREER award, a Google Faculty Research Award, an Amazon Research Award, and a Yahoo Academic Career Enhancement award.

For more information, see my CV.

My lovely wife Cathy just got her PhD in math at MIT. At her insistence, I link to her website

Contact Information

Office: 3401 Walnut Street, room 406b
Phone: 215-746-6171
Email: aaroth@cis.upenn.edu


[PHOTO]

Coming Fall 2019: Michael Kearns and I have written a general-audience book about the science of designing algorithms that embed social values like privacy and fairness; here is the publisher's description:

Over the course of a generation, algorithms have gone from mathematical abstractions to powerful mediators of daily life. Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps.

Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.


Teaching
I am on sabbatical for Fall 2019.
Previous courses:
I'm fortunate to be able to work with several excellent graduate students and postdocs.
Current: Alumni:
Books and Surveys
  1. The Ethical Algorithm. Joint with Michael Kearns. Oxford University Press, Forthcoming Fall 2019.
  2. The Algorithmic Foundations of Differential Privacy. Joint with Cynthia Dwork. Foundations and Trends in Theoretical Computer Science, NOW Publishers. 2014.
  3. Privacy and Mechanism Design. Joint with Mallesh Pai. SIGecom Exchanges, 2013.

Recent and Selected Publications
 (See here for all publications, or my Google Scholar profile)
Click for abstract/informal discussion of results
  1. Exponential Separations in Local Differential Privacy Through Communication Complexity. Joint work with Matthew Joseph and Jieming Mao. Manuscript.
  2. Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. Joint work with Ryan Rogers, Adam Smith, Nati Srebro, Om Thakkar, and Blake Woodworth. Manuscript.
  3. Eliciting and Enforcing Subjective Individual Fairness. Joint work with Christopher Jung, Michael Kearns, Seth Neel, Logan Stapleton, and Steven Wu. Manuscript.
  4. Average Individual Fairness: Algorithms, Generalization and Experiments. Joint work with Michael Kearns and Saeed Sharifi-Malvajerdi. Manuscript.
  5. Gaussian Differential Privacy. Joint work with Jinshuo Dong and Weijie Su. Manuscript.
  6. The Role of Interactivity in Local Differential Privacy. Joint work with Matthew Joseph, Jieming Mao, and Seth Neel. To appear in FOCS 2019.
  7. How to Use Heuristics for Differential Privacy. Joint with Seth Neel and Steven Wu. To appear in FOCS 2019.
  8. The Downstream Effects of Affirmative Action. Joint with Sampath Kannan and Juba Ziani. In the Proceedings of FAT* 2019.
  9. Online Learning with an Unknown Fairness Metric. Joint with Stephen Gillen, Christopher Jung, and Michael Kearns. In the proceedings of NIPS 2018.
  10. A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. Joint with Sampath Kannan, Jamie Morgenstern, Bo Waggoner, and Steven Wu. In the proceedings of NIPS 2018.
  11. Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. Joint with Michael Kearns, Seth Neel, and Steven Wu. In the proceedings of ICML 2018.
  12. Fairness in Learning: Classic and Contextual Bandits. Joint with Matthew Joseph, Jamie Morgenstern, and Michael Kearns. In the proceedings of NIPS 2016.
  13. Robust Mediators in Large Games. Joint with Michael Kearns, Mallesh Pai, Ryan Rogers, and Jon Ullman. Manuscript. (This paper subsumes both "Mechanism Design in Large Games: Incentives and Privacy" which appeared in ITCS 2014, and "Asymptotically Truthful Equilibrium Selection" which appeared in EC 2014).
  14. Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing. Joint with Ryan Rogers, Adam Smith, and Om Thakkar. In the proceedings of FOCS 2016.
  15. Do Prices Coordinate Markets?. Joint work with Justin Hsu, Jamie Morgenstern, Ryan Rogers, and Rakesh Vohra. In the proceedings of STOC 2016.
  16. Watch and Learn: Optimizing from Revealed Preferences Feedback. Joint with Jon Ullman and Steven Wu. In the proceedings of STOC 2016.
  17. Private Algorithms for the Protected in Social Network Search. Joint with Michael Kearns, Steven Wu, and Grigory Yaroslavtsev. In Proceedings of the National Academy of Sciences (PNAS), January 2016.
  18. Jointly Private Convex Programming. Joint work with Justin Hsu, Zhiyi Huang, and Steven Wu. In the proceedings of SODA 2016.
  19. The Reusable Holdout: Preserving Validity in Adaptive Data Analysis. Joint work with Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, and Omer Reingold. In Science, August 7 2015.
  20. Preserving Statistical Validity in Adaptive Data Analysis. Joint work with Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, and Omer Reingold. In the proceedings of STOC 2015.
  21. Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). Joint with Sampath Kannan, Jamie Morgenstern, and Steven Wu. In the proceedings of SODA 2015.
  22. Private Matchings and Allocations. Joint work with Justin Hsu, Zhiyi Huang, Tim Roughgarden, and Steven Wu. In the proceedings of STOC 2014.
  23. Differential Privacy for the Analyst via Private Equilibrium Computation. Joint with Justin Hsu and Jon Ullman. In the proceedings of STOC 2013.
  24. Privately Releasing Conjunctions and the Statistical Query Barrier. Joint with Anupam Gupta, Moritz Hardt, and Jonathan Ullman. In the proceedings of STOC 2011.
    Full version appears in SIAM Journal on Computing (SICOMP) 2013.
  25. Selling Privacy at Auction. Joint work with Arpita Ghosh. In the proceedings of EC 2011.
    Invited to a special issue of Games and Economic Behavior (GEB) 2013.
  26. Interactive Privacy via the Median Mechanism. Joint with Tim Roughgarden. In the proceedings of STOC 2010.
  27. A Learning Theory Approach to Non-Interactive Database Privacy. Joint with Avrim Blum and Katrina Ligett. In the proceedings of STOC 2008: The 40th ACM Symposium on the Theory of Computing.
    Full version appears in Journal of the ACM (JACM) 2013.
  28. Regret Minimization and the Price of Total Anarchy. Joint with Avrim Blum, MohammadTaghi Hajiaghayi, and Katrina Ligett. In the proceedings of STOC 2008: The 40th ACM Symposium on the Theory of Computing.
Professional Activities

Workshops, Tutorials, Interviews, and Panels: Program Committee Member For:
Presentations
(Slides Available Upon Request)