400 Jon M. Huntsman Hall,
3730 Walnut Street,
Philadelphia, PA 19104
Machine learning and applications to fairness and transparency.
The Wharton School 2015-Present, PhD cand. in Statistics.
Harvard University 2015, BA in Mathematics.
Seth Neel (2017), Accuracy First: Selecting a Differential Privacy Level for ERM, Neural Information Processing Systems .
Description: NIPS 2017. Theory and Practice of Differential Privacy 2017.
Seth Neel, Michael Kearns, Aaron Roth, Matthew Joseph, Jamie morgenstern (2016), Fairness in Linear Bandit Problems,.
Description: Conference talk at FATML 2016 (fatml.org). Recording and pdf available at link.
Seth Neel, Michael Kearns, Aaron Roth, Matthew Joseph (Under Review), Rawlsian Fairness for Machine Learning.
Seth Neel, Megan Leoni, Gregg Musiker, Paxton Turner (2014), Aztec Castles and the DP3 Quiver, The Journal of Physics A: Mathematical and Theoretical, 47.
Fall 2016: Head TA for Regression Analysis for Business, STAT 613. Wharton Business School.
Spring 2015: TA for Introduction to Probability STAT 430. University of Pennsylvania.
Connecting recent goings-on at Facebook to the larger discussion on algorithmic fairness.
Crunching some lottery probabilities for CNBC.