Hadi Elzayn

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I’m a Postdoctoral Fellow at Stanford University’s RegLab, where I am advised by Dan Ho and Jacob Goldin. Currently, I work on algorithmic and equity concerns in applying machine learning to closing the tax gap. More broadly, I’m interested in fundamental questions at the intersection of computer science and economics, as well as how algorithms in general, and machine learning in particular, can exacerbate or improve societal concerns around fairness, privacy, and markets.

Prior to the RegLab, I did my PhD at the University of Pennsylvania, where I was advised by Michael Kearns. During my PhD, I interned at Facebook, Microsoft Research, and the Federal Reserve Bank of Philadelphia, and before graduate school I worked for several years at TGG Group, a Chicago-based consulting firm applying econometrics and behavioral economics to solve problems for business, non-profits, and government. I received my B.A. in Mathematics and Economics from Columbia University.

News

  • (March 2021) I am finally updating/rebuilding my website.
  • (Jan 2021) I started my Postdoc at Stanford University in the RegLab.
  • (Dec 2020) I graduated from Penn with My PhD.


Stanford University
Regulation, Technology, and Governance Lab
559 Nathan Abbot Way
Stanford, CA 94305

hselzayn@law.stanford.edu




8) Equilibria in Auctions with Ad Types
TheWebConf 2022. H. Elzayn, R. Colini-Baldeschi, B. Lan, O. Schrijvers. (Arxiv Version.)

7) Algorithms and Learning for Fair Portfolio Design
EC 2021. E. Diana, T. Dick, H. Elzayn , M. Kearns, A. Roth, Z. Schutzman, S. Sharifi-Malvajerdi, and J. Ziani. (Publication Version). (Arxiv Version).

6) Differentially Private Call Auctions and Market Impact.
EC 2020. E. Diana, H. Elzayn, M. Kearns, A. Roth, S. Sharifi-Malvajerdi, and J. Ziani. (Publication Version) (Arxiv Version)

5) The Effects of Competition and Regulation on Error Inequality in Data-Driven Markets.
ACM FAT* 2020. H. Elzayn, B. Fish. (Publication Version)
Also presented as an oral/poster presentation at the NeuRIPS 2019 AI for Social Good Workshop. AISG Best poster winner.

4) Equilibrium Characterization for Data-Acquisition Games
IJCAI 2019. J. Dong, H. Elzayn, S. Jabbari, M. Kearns, Z. Schutzman. (Publication Version) (Arxiv Version)

3) Price of Privacy in the Keynesian Beauty Contest.
EC 2019. H. Elzayn, Z. Schutzman. (Publication Version) (Arxiv Version)

2) Hidden Information, Teamwork, and Prediction in Trick-Taking Card Games.
RLDM 2019 (Extended Abstract). H. Elzayn, M. Fereydounian, M. Hayhoe, H. Kumar. (Publication Version) (Arxiv Version)

1) Fair Algorithms for Learning in Allocation Problems.
ACM FAT* 2019. H. Elzayn, S. Jabbari, C. Jung, M. Kearns, S. Neel, A. Roth, Z. Schutzman (Publication Version) (Arxiv Version)






NETS 412: Algorithmic Game Theory
Teaching Assistant. Instructor: Bo Waggoner. Spring 2018. NETS 412

AMCS/MATH 602: Algebraic Techniques.
Grader. Instructor: Zhenfu Wang. Fall 2017. AMCS 602