Research

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fairness in machine learning, online learning, bandit problems, computational learning theory, differential privacy, combinatorics. 

Papers

[7] A Convex Framework for Fair Regression. Submitted, May 2017.

[6] Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM. Submitted, May 2017. 

[5] A Framework for Meritocratic Fairness of Online Linear Models. Submitted, May 2017.

[4] Fairness in Linear Bandit Problems   Fairness, Accountability, and Transparency in Machine Learning (FATML) 2016. Presented at NYU Law School, November 2016. Full version: Rawlsian Fairness for Machine Learning.  with: Matt Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth

[3]  Aztec Castles and the dP3 Quiver

The Journal of Physics A: Mathematical and Theoretical, 2015.

[2] Mahalanobis Matching and Equal Percent Bias Reduction

Senior Honors Thesis Harvard University 2015, High Honors. Supervised by Natesh Pillai.

[1] Plane Partitions and Domino TilingsIntel STS Semifinalist 2011.

A new bijection between domino tilings of aztec diamonds and plane partitions is developed, leading to a simple proof of the generating function. A purely combinatorial proof of the Aztec Diamond Theorem due to [EKLP] is given. PDF available upon request.