Hi! I’m a fourth year Ph.D. candidate in Statistics at The Wharton School, University of Pennsylvania. I’m lucky to be advised by Michael Kearns and Aaron Roth and supported by a NSF Graduate Fellowship . My primary research (Google Scholar) interests are in theoretical computer science and machine learning. Specifically in incorporating ethical notions of fairness and privacy into tools for data analysis and artificial intelligence. This works fits into the growing fields of algorithmic fairness and differential privacy. I graduated with a math degree from Harvard in 2015, and am a co-founder of Welligence, a fintech company focused on the energy sector.
Research – Differential Privacy | Fairness in Machine Learning
All author ordering is strictly alphabetical. My research has two 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 and adaptive data analysis
In progress: Approximately Optimal Private Truthful Allocations, Fairness by Committee, Smoothed Analysis of Private Learning, Efficient Private Synthetic Data Generation
Rawlsian Fairness for Machine Learning [FATML 2016]
Aztec Castles and the dP3 Quiver [Journal of Physics A 2015]
Mahalanobis Matching and Equal Percent Bias Reduction[Senior Thesis, Harvard ’15]
Plane Partitions and Domino Tilings [Intel Science Talent Search Semifinalist, 2011]
- Stanford Computer Science Dept. Seminar, 3.4.19.
- FAT* 2019. Atlanta, Georgia. 1.29.18.
- Temple CIS Dept. Seminar, Invited Speaker. 11.29.18.
- Boston University Computer Science Dept. Seminar. 11.19.18.
- Northeastern Computer Science Dept. Seminar. 11.8.18.
- MIT CSAIL Algorithms and Complexity Seminar, 11.7.18.
- Penn Research in Machine Learning, seminar series. 9.29.18. Slides.
- Program Committee ACM Conference on Fairness, Accountability, and Transparency 2019 (ACM FAT*)
- ICML 2018, Contributed Talk. 7.11.18, Stockholm, Sweden.
- Preventing Fairness Gerrymandering
- Mitigating Bias in Adaptive Data Gathering via Differential Privacy
- Mechanism Design for Social Good Workshop at EC 2018, Contributed Talk. 6.22.18, Ithaca, NY. Slides
- BIRS Mathematical Foundations of Data Privacy Workshop, Invited Talks. 4.29.18, Banff, Alberta.
- AAAI Workshop on Artificial Intelligence Ethics & Society. Poster Presentation. 2.3.18, New Orleans, LA.
- Neural Information Processing Systems, 12.8.17, Poster Presentation. Long Beach, CA.
- Facebook Privacy Ethics Workshop, 11.2.17, NY, NY.
- Theory and Practice of Differential Privacy, Contributed Talk. 10.30.17, Dallas, TX. Slides
- Fairness Accountability and Transparency in Machine Learning, Contributed Talk. New York University. 11.1.16. Video
Forbes 30 under 30 in Energy 2019 The Future of Entrepreneurship is Students (Forbes, May 2018) Facebook's Racially Targeted Ads Aren't as Racist As You Think (Wired, Nov. 2016) The Best Time to Play Powerball Could Have Been 600m Ago (CNBC, Jan. 2016)
Office(s): 416B‐1 B-Wing, 3401 Walnut Street, Philadelphia, PA (CompSci), 1635 Market St. 16th Floor, Philadelphia, PA (Welligence)
Email(s): firstname.lastname@example.org (company) email@example.com (academic)