World's Best Scientists 2026 revealed!

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Mathematics

D-Index
37
Citations
4777
World Ranking
2537
National Ranking
1051

Research.com Recognitions

  • 2019 - Fellow of the American Mathematical Society For contributions to theoretical computer science, in particular through its interactions with probability, combinatorics and statistical physics and for service to the profession.

Overview

Eric Vigoda is affiliated with the University of California, Santa Barbara in the United States. Their research primarily focuses on intersections of mathematics and computer science, with significant contributions in the fields of statistics and probability, mathematical physics, and artificial intelligence. The scientist's work encompasses theoretical and computational aspects of these disciplines.

Vigoda's main areas of study include Markov chains and Monte Carlo methods, stochastic processes and statistical mechanics, as well as theoretical and computational physics. Additional research topics involve Bayesian methods and mixture models, Bayesian modeling and causal inference, statistical methods and inference, and machine learning and algorithms.

The scientist has published extensively in various academic venues. Frequent publication platforms include:

  • arXiv (Cornell University)
  • ACM Transactions on Algorithms
  • Random Structures and Algorithms
  • SIAM Journal on Computing
  • The Annals of Applied Probability

Notable recent papers authored by or associated with Eric Vigoda are:

  • Fast algorithms at low temperatures via Markov chains, 2020, Random Structures and Algorithms
  • Sampling in Uniqueness from the Potts and Random-Cluster Models on Random Regular Graphs, 2020, SIAM Journal on Discrete Mathematics
  • Rapid Mixing of Glauber Dynamics up to Uniqueness via Contraction, 2020, arXiv (Cornell University)
  • On mixing of Markov chains: coupling, spectral independence, and entropy factorization, 2022, Electronic Journal of Probability
  • Rapid Mixing of Glauber Dynamics up to Uniqueness via Contraction, 2023, SIAM Journal on Computing

Frequent collaborators in the scientist's body of work include Zongchen Chen, Daniel Štefankovič, Antonio Blanca, Andreas Galanis, and Kuikui Liu.

Eric Vigoda was recognized as a Fellow of the American Mathematical Society in 2019. The fellowship was awarded for contributions to theoretical computer science, particularly through its interactions with probability, combinatorics, and statistical physics, as well as service to the profession.

Best Publications

  • A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries

    Mark Jerrum;Alistair Sinclair;Eric Vigoda

  • Improved bounds for sampling colorings

    Eric Vigoda

  • Phylogenetic MCMC Algorithms Are Misleading on Mixtures of Trees

    Elchanan Mossel;Elchanan Mossel;Eric Vigoda;Eric Vigoda

  • A polynomial-time approximation algorithm for the permanent of a matrix with non-negative entries

    Mark Jerrum;Alistair Sinclair;Eric Vigoda

  • Inapproximability of the Partition Function for the Antiferromagnetic Ising and Hard-Core Models

    Andreas Galanis;Daniel Štefankovič;Eric Vigoda

  • Torpid mixing of some Monte Carlo Markov chain algorithms in statistical physics

    C. Borgs;J.T. Chayes;A. Frieze;Jeong Han Kim

  • Fast convergence of the Glauber dynamics for sampling independent sets

    Michael Luby;Eric Vigoda;Eric Vigoda

  • Accelerating Simulated Annealing for the Permanent and Combinatorial Counting Problems

    Ivona Bezáková;Daniel Štefankovič;Vijay V. Vazirani;Eric Vigoda

  • Elementary bounds on Poincaré and log-Sobolev constants for decomposable Markov chains

    Mark Jerrum;Jung-Bae Son;Prasad Tetali;Eric Vigoda

  • Mixing in time and space for lattice spin systems: A combinatorial view

    Martin Dyer;Alistair Sinclair;Eric Vigoda;Dror Weitz

  • A non-Markovian coupling for randomly sampling colorings

    T.P. Hayes;E. Vigoda

  • Inapproximability for Antiferromagnetic Spin Systems in the Tree Nonuniqueness Region

    Andreas Galanis;Daniel Štefankovič;Eric Vigoda

  • Randomly coloring sparse random graphs with fewer colors than the maximum degree

    Martin Dyer;Abraham D. Flaxman;Alan M. Frieze;Eric Vigoda

  • Adaptive simulated annealing: A near-optimal connection between sampling and counting

    Daniel Štefankovič;Santosh Vempala;Eric Vigoda

  • A survey on the use of Markov chains to randomly sample colorings

    Alan Frieze;Eric Vigoda

  • A Note on the Glauber Dynamics for Sampling Independent Sets

    Eric Vigoda

  • Sampling binary contingency tables with a greedy start

    Ivona Bezáková;Nayantara Bhatnagar;Eric Vigoda

  • Approximately counting up to four (extended abstract)

    Michael Luby;Eric Vigoda

  • Optimal mixing of Glauber dynamics: entropy factorization via high-dimensional expansion

    Zongchen Chen;Kuikui Liu;Eric Vigoda

  • An FPTAS for #Knapsack and Related Counting Problems

    Parikshit Gopalan;Adam Klivans;Raghu Meka;Daniel tefankovic

  • A Non-Markovian Coupling for Randomly Sampling Colorings (Extended Abstract)

    Thomas P. Hayes;Eric Vigoda

Frequent Co-Authors

Alistair Sinclair
Alistair Sinclair University of California, Berkeley
Leslie Ann Goldberg
Leslie Ann Goldberg University of Oxford
Mark Jerrum
Mark Jerrum Queen Mary University of London
Martin Dyer
Martin Dyer University of Leeds
Prasad Tetali
Prasad Tetali Carnegie Mellon University
Santosh Vempala
Santosh Vempala Georgia Institute of Technology
Alan Frieze
Alan Frieze Carnegie Mellon University
Jinwoo Shin
Jinwoo Shin Korea Advanced Institute of Science and Technology
Vijay V. Vazirani
Vijay V. Vazirani University of California, Irvine

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