Research Summary

I'm a Ph.D. Candidate in the Computing and Mathematical Sciences department at Caltech, where I am advised by Katrina Ligett. My work seeks to bridge the gap between theory and practice in the formal study of privacy. This includes problems such as strategic aspects of data generation, incentivizing truthful reporting of data, impacts of privacy policy, human decision-making, and algorithm design. More broadly, I take a comprehensive approach to addressing real-world privacy challenges, using a diverse toolkit of both theoretical and practical perspectives. I'm supported in part by a Simons Award for Graduate Students in Theoretical Computer Science for 2015-2017.

For more information on my research interests, see my Curriculum Vitae and Research Statement.


Adaptive Learning with Robust Generalization Guarantees, with Katrina Ligett, Kobbi Nissim, Aaron Roth, and Zhiwei Steven Wu. COLT '16.

The Possibilities and Limitations of Private Prediction Markets, with David Pennock and Jennifer Wortman Vaughan. EC '16.

The Strange Case of Privacy in Equilibrium Models, with Katrina Ligett, Mallesh Pai, and Aaron Roth. EC '16.

Coordination Complexity: Small Information Coordinating Large Populations, with Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, and Zhiwei Steven Wu. ITCS '16.

Truthful Linear Regression, with Stratis Ioannidis and Katrina Ligett. COLT '15.

Accuracy for Sale: Aggregating Data with a Variance Constraint, with Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, and Juba Ziani. ITCS '15.

Privacy and Truthful Equilibrium Selection for Aggregative Games, with Michael Kearns, Aaron Roth, and Zhiwei Steven Wu. WINE '15.

Online Learning and Profit Maximization from Revealed Preferences, with Kareem Amin, Lili Dworkin, Michael Kearns, and Aaron Roth. AAAI '15.

Probability 1 Computation with Chemical Reaction Networks, with Dave Doty and David Soloveichik. DNA '14.
Journal verion: Natural Computing 2015.

The Empirical Implications of Privacy-Aware Choice, with Federico Echenique and Adam Wierman. EC '14.
Journal version: Operations Research, 2016.

Speed Faults in Computation by Chemical Reaction Networks, with Ho-Lin Chen, Dave Doty, and David Soloveichik. DISC '14. Best paper award
Journal version: Distributed Computing, 2015.

Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate, with Wei Chen, Alex Collins, Te Ke, Zhenming Liu, David Rincon, Xiaorui Sun, Yajun Wang, Wei Wei, and Yifei Yuan. SDM'11.