World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
37
Citations
4777
World Ranking
2538
National Ranking
1052

Eric Vigoda publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Eric Vigoda sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 107 publications — 14th percentile

14% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Eric Vigoda D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Eric Vigoda sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 37 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

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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