Privacy | Fairness
All author ordering is strictly alphabetical. My research has a few threads:
- Fairness in Machine Learning (design of new algorithms and fairness notions, in the online, bandit, batch settings)
- The study of fundamental problems in differential privacy, with an emphasis on private learning
- Adaptive Data Analysis
18. Descent-to-Delete: Gradient-Based Methods for Machine Unlearning [Algorithmic Learning Theory ’21]
17. Optimal, Truthful, and Private Securities Lending [ACM AI in Finance ’20, NEURIPS Workshop on Robust AI in Financial Services ’19] selected for oral presentation!
16. Differentially Private Objective Perturbation: Beyond Smoothness and Convexity [ICML ’20, NEURIPS Workshop on Privacy in ML ’19]
15. A New Analysis of Differential Privacy’s Generalization Guarantees [ITCS ’20] regular talk slot!
14. The Role of Interactivity in Local Differential Privacy [FOCS ’19]
13. How to use Heuristics for Differential Privacy [FOCS ’19]
12. An Empirical Study of Rich Subgroup Fairness for Machine Learning [ACM FAT* ’19, ML track]
- Led development on package integrated into the IBM AI Fairness 360 package here. AIF360 development branch on my Github, with a stand-alone package developed by the AlgoWatch Team.
11. Fair Algorithms for Learning in Allocation Problems [ACM FAT* ’19, ML track]
10. Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness [ICML ’18, EC MD4SG ’18]
8. Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM [NIPS ’17, Journal of Privacy and Confidentiality ’19]
Math stuff from College & High School
2. Aztec Castles and the dP3 Quiver [Journal of Physics A ’15]
1. Mahalanobis Matching and Equal Percent Bias Reduction[Senior Thesis, Harvard ’15]
0. Plane Partitions and Domino Tilings [Intel Science Talent Search Semifinalist, ’11]