Current: Senior Software Engineer at LinkedIn (we are hiring differential privacy research engineers).
- University of Pennsylvania (Philadelphia, PA)
- Cambridge University (UK)
- Stetson University
- BS Mathematics
(Summa Cum Laude)
- BA Religious Studies (Summa Cum Laude)
- Differential Privacy
- Adaptive Data Analysis
- Algorithmic Game Theory
- Mechanism Design
- Optimal Differential Privacy Composition for Exponential Mechanisms and the Cost of Adaptivity. Joint work with Jinshuo Dong and David Durfee. In submission.
- Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. Joint work with Aaron Roth, Adam Smith, Nathan Srebro, Om Thakkar, Blake Woodworth. In submission.
- Practical Differentially Private Top-k Selection with Pay-what-you-get Composition. Joint work with David Durfee. To appear in the proceedings of NeurIPS 2019 as a spotlight presentation (top 3% of submissions).
- Lower Bounds for Locally Private Estimation via Communication Complexity. Joint work with John Duchi. In the proceedings of COLT 2019.
- Protection Against Reconstruction and Its Applications in Private Federated Learning. Joint work with Abhishek Bhowmick, John Duchi, Julien Freudiger, and Gaurav Kapoor. In submission.
- Locally Private Mean Estimation: Z-test and Tight
Confidence Intervals. Joint work with Marco Gaboardi and Or
Sheffet. In the proceedings of AISTATS 2019.
- Local Private Hypothesis Testing: Chi-Square Tests. Joint work with Marco
Gaboardi. In the proceedings of ICML 2018.
- Learning with Privacy at Scale. Joint work with the Differential Privacy Team at Apple. Posted on the Apple Machine Learning Journal.
- Leveraging Privacy in Data Analysis. PhD Dissertation 2017.
- A Decomposition of Forecast Error in Prediction Markets. Joint work with Miro Dudik, Sebastien Lahaie, and Jenn Wortman Vaughan. In the proceedings of NIPS 2017.
- A New Class of Private Chi-Square Tests. Joint work with Daniel Kifer. In the proceedings of AISTATS 2017.
- Privacy Odometers and Filters:
Pay-as-you-Go Composition. Joint work with Aaron Roth,
Jonathan Ullman, and Salil Vadhan. In the proceedings of NIPS 2016.
- Max-Information, Differential
Privacy, and Post-Selection Hypothesis Testing. Joint work
with Aaron Roth, Adam Smith, and Om Thakkar. In the proceedings of FOCS
- Differentially Private
Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing.
Joint work with Marco
Gaboardi, Hyun woo Lim, and Salil Vadhan. In the proceedings
of ICML 2016.
- Robust Mediators in Large Games.
Joint work with Michael Kearns, Mallesh
M. Pai, Aaron Roth, and Jonathan Ullman. In submission. (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).
- Do Prices Coordinate Markets?
Joint work with Jamie Morgenstern, Justin
Hsu, Aaron Roth, and Rakesh Vohra.
In the proceedings of STOC 2016.
- Presentation- Google NY
(with some borrowed slides from Aaron Roth)
- Poster - Presented at
- Invited to SIGecom exchanges.
- Learning from Rational
Behavior: Predicting Solutions to Unknown Linear Programs. Joint
work with Shahin Jabbari,
Aaron Roth, and Steven Wu. In the proceedings of NIPS 2016.
- Inducing Approximately Optimal
Flow using Truthful Mediators. Joint work with Aaron Roth,
and Steven Wu.
In the proceedings of EC 2015.
- Private Pareto Optimal Exchange.
Joint work with Sampath
Morgenstern, and Aaron Roth.
In the proceedings of EC 2015.
- Invited to special issue of Transactions on Economics and Computation special issue of EC'15.
- Asymptotically Truthful
Equilibrium Selection in Large Congestion Games. Joint
work with Aaron Roth. In the proceedings of EC 2014.
- Algorithmic Game Theory. Mathematics Part III Essay 2011.
- Identification of localized
structure in a nonlinear damped harmonic oscillator using Hamilton's
Principle. Joint work with Thomas
(2010); vol. 3, issue 4.
- President and co-founder of SIAM
student chapter at Penn, 2014-2016.
- Rowing Coach for Wharton
- Rowing Experience: