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Computer Science

D-Index
74
Citations
27209
World Ranking
1469
National Ranking
28

Overview

Uriel Feige is affiliated with the Weizmann Institute of Science in Israel. Their research spans multiple fields within computer science and economics, focusing particularly on areas intersecting computational theory, economics, and operations research.

The primary fields of study for Uriel Feige include:

  • Computer Science
  • Economics, Econometrics and Finance

Within these broad domains, Feige's subfields of expertise cover:

  • Economics and Econometrics
  • Computational Theory and Mathematics
  • Management Science and Operations Research
  • Artificial Intelligence
  • Computer Networks and Communications

Key research topics addressed in Feige's work include:

  • Game Theory and Voting Systems
  • Auction Theory and Applications
  • Complexity and Algorithms in Graphs
  • Advanced Graph Theory Research
  • Economic theories and models
  • Optimization and Search Problems
  • Law, Economics, and Judicial Systems

Uriel Feige has collaborated frequently with several researchers, including:

  • Moshe Babaioff
  • Tomer Ezra
  • Kousha Etessami
  • Gabriele Puppis
  • Vadim Grinberg

They have published extensively across multiple venues, with frequent appearances in:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Mathematics of Operations Research
  • Proceedings of the 23rd ACM Conference on Economics and Computation
  • ACM Transactions on Algorithms

Recent representative publications include:

  • "Fair and Truthful Mechanisms for Dichotomous Valuations," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Quantitative Group Testing and the rank of random matrices," 2020, arXiv (Cornell University)
  • "Fair-Share Allocations for Agents with Arbitrary Entitlements," 2023, Mathematics of Operations Research
  • "Fair Allocations for Smoothed Utilities," 2022, Proceedings of the 23rd ACM Conference on Economics and Computation
  • "Haplotype-Aware Long-Read Error Correction," 2025, arXiv (Cornell University)

The body of work demonstrates a consistent integration of algorithmic design, economic theory, and operational models, often addressing fairness, efficiency, and complexity in economic systems and computational frameworks.

Best Publications

  • A threshold of ln n for approximating set cover

    Uriel Feige

  • Zero-knowledge proofs of identity

    U. Feige;A. Fiat;A. Shamir

  • Maximizing Non-monotone Submodular Functions

    Uriel Feige;Vahab S. Mirrokni;Jan Vondrák

  • Witness indistinguishable and witness hiding protocols

    U. Feige;A. Shamir

  • Zero Knowledge and the Chromatic Number

    Uriel Feige;Joe Kilian

  • The Dense k -Subgraph Problem

    Uriel Feige;Guy Kortsarz;David Peleg

  • Adaptively secure multi-party computation

    Ran Canetti;Uri Feige;Oded Goldreich;Moni Naor

  • Approximating clique is almost NP-complete

    U. Feige;S. Goldwasser;L. Lovasz;S. Safra

  • On Maximizing Welfare When Utility Functions Are Subadditive

    Uriel Feige

  • Interactive proofs and the hardness of approximating cliques

    Uriel Feige;Shafi Goldwasser;Laszlo Lovász;Shmuel Safra

  • A threshold of ln n for approximating set cover (preliminary version)

    Uriel Feige

  • Approximating the value of two power proof systems, with applications to MAX 2SAT and MAX DICUT

    U. Feige;M. Goemans

  • Relations between average case complexity and approximation complexity

    Uriel Feige

  • Zero knowledge proofs of identity

    U. Fiege;A. Fiat;A. Shamir

  • Improved Approximation Algorithms for Minimum Weight Vertex Separators

    Uriel Feige;MohammadTaghi Hajiaghayi;James R. Lee

  • Detecting high log-densities: an O(n¼) approximation for densest k-subgraph

    Aditya Bhaskara;Moses Charikar;Eden Chlamtac;Uriel Feige

  • Multiple NonInteractive Zero Knowledge Proofs Under General Assumptions

    Uriel Feige;Dror Lapidot;Adi Shamir

  • Zero knowledge proofs of knowledge in two rounds

    Uriel Feige;Adi Shamir

  • Computing with Noisy Information

    Uriel Feige;Prabhakar Raghavan;David Peleg;Eli Upfal

  • A minimal model for secure computation (extended abstract)

    Uri Feige;Joe Killian;Moni Naor

Frequent Co-Authors

Moshe Tennenholtz
Moshe Tennenholtz Technion – Israel Institute of Technology
Robert Krauthgamer
Robert Krauthgamer Weizmann Institute of Science
Michal Feldman
Michal Feldman Tel Aviv University
Michael Langberg
Michael Langberg University at Buffalo, State University of New York
Adi Shamir
Adi Shamir Weizmann Institute of Science
Christian Borgs
Christian Borgs University of California, Berkeley
Jennifer Chayes
Jennifer Chayes University of California, Berkeley
Nicole Immorlica
Nicole Immorlica Microsoft (United States)
Prabhakar Raghavan
Prabhakar Raghavan Google (United States)
MohammadTaghi Hajiaghayi
MohammadTaghi Hajiaghayi University of Maryland, College Park

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