Seth Viren Neel   

Screen Shot 2018-11-28 at 9.48.56 PMHi! 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.


All author ordering is strictly alphabetical. My research has two threads:

  1. Fairness in Machine Learning (design of new algorithms and fairness notions, in the online, bandit, batch settings)
  2. The study of fundamental problems in differential privacy, with an emphasis on private learning and adaptive data analysis

Differential Privacy & Learning

How to use Heuristics for Differential Privacy [In Submission]

with Aaron Roth & Steven Wu.

Mitigating Bias in Adaptive Data Gathering via Differential Privacy [ICML 2018]

with Aaron Roth.

Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM[NIPS  2017, TPDP spotlight talk]

with Katrina Liggett, Aaron Roth, Bo Waggoner, and Steven Wu.

Fairness in Machine Learning

An Empirical Study of Rich Subgroup Fairness for Machine Learning [FAT* 2019]

Code on my Github and developed further by the AlgoWatch Team 

with Michael Kearns, Aaron Roth, and Steven Wu.

Fair Algorithms for Learning in Allocation Problems [FAT* 2019]

with Hadi El Zayn, Chris Jung, Shahin Jabbari, Michael Kearns, Aaron Roth, and Zach Schutzmann.

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness [ICML 2018]

with Michael Kearns, Aaron Roth, and Steven Wu.

A Framework for Meritocratic Fairness of Online Linear Models [AAAI/AIES 2018]

with Matt Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth.

A Convex Framework for Fair Regression [FATML 2017]

with Richard Berk, Hoda Heidari, Matt Joseph,  Shahin Jabbari, Michael Kearns, and Aaron Roth.

Math stuff from College & High School

Aztec Castles and the dP3 Quiver [Journal of Physics A 2015]

with Gregg Musiker, Paxton Turner, and Megan Leoni.

Mahalanobis Matching and Equal Percent Bias Reduction[Senior Thesis, Harvard ’15]

Supervised by Natesh Pillai.

Plane Partitions and Domino Tilings [Intel Science Talent Search Semifinalist, 2011]

Supervised by Mirjana Vuletic.

Service & Talks


  • Stanford Computer Science Dept. Seminar, 3.15.18.
  • Temple CIS Dept. Seminar, Invited Speaker. 11.29.18.



Forbes 30 under 30 in Energy 2019

The Future of Entrepreneurship is Students 
(Forbes, May 2018)
Screen Shot 2018-06-08 at 1.21.35 AM.png

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): (company) (academic)


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