World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
28382
World Ranking
9923
National Ranking
4169

Matthew D. Hoffman publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Matthew D. Hoffman sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 94 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Matthew D. Hoffman D-index placement in Computer Science in 2026

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

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Matthew D. Hoffman is a researcher affiliated with Google in the United States. Their work spans the field of computer science, with a primary focus on areas such as artificial intelligence and statistics and probability. They have contributed extensively to research in subfields including computer vision and pattern recognition, as well as organic chemistry and electrical and electronic engineering.

The scientist has published 25 works within computer science. Their research topics frequently cover areas like Markov chains and Monte Carlo methods, Gaussian processes and Bayesian inference, statistical methods and inference, Bayesian methods and mixture models, asymmetric hydrogenation and catalysis, organometallic complex synthesis and catalysis, and topic modeling.

Publications by Matthew D. Hoffman have appeared in various venues. They have published 13 papers in arXiv (Cornell University), one in Nature Communications, one in Bayesian Analysis, one in bioRxiv (Cold Spring Harbor Laboratory), and one in Organometallics.

  • Underspecification Presents Challenges for Credibility in Modern Machine Learning, 2020, arXiv (Cornell University)
  • What Are Bayesian Neural Network Posteriors Really Like?, 2021, arXiv (Cornell University)
  • Lossy Compression with Gaussian Diffusion, 2022, arXiv (Cornell University)
  • tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware, 2020, arXiv (Cornell University)
  • Scalable spatiotemporal prediction with Bayesian neural fields, 2024, Nature Communications

Their frequent coauthors include Rif A. Saurous, Pavel Sountsov, Feras A. Saad, Colin Carroll, and Brian Patton. The number of collaborations is notable, with the top collaborators having contributed to multiple papers alongside them.

Best Publications

  • Stan: A Probabilistic Programming Language

    Bob Carpenter;Andrew Gelman;Matthew D. Hoffman;Daniel Lee

  • Stochastic variational inference

    Matthew D. Hoffman;David M. Blei;Chong Wang;John Paisley

  • Online Learning for Latent Dirichlet Allocation

    Matthew Hoffman;Francis R. Bach;David M. Blei

  • The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

    Matthew D. Hoffman;Andrew Gelman

  • Variational Autoencoders for Collaborative Filtering

    Dawen Liang;Rahul G. Krishnan;Matthew D. Hoffman;Tony Jebara

  • Stochastic Gradient Descent as Approximate Bayesian Inference

    Stephan Mandt;Matthew D. Hoffman;David M. Blei

  • Underspecification Presents Challenges for Credibility in Modern Machine Learning

    Alexander D'Amour;Katherine A. Heller;Dan Moldovan;Ben Adlam

  • Learning Activation Functions to Improve Deep Neural Networks

    Forest Agostinelli;Matthew D. Hoffman;Peter J. Sadowski;Pierre Baldi

  • Static and Dynamic Source Separation Using Nonnegative Factorizations: A unified view

    Paris Smaragdis;Cedric Fevotte;Gautham J. Mysore;Nasser Mohammadiha

  • Bayesian Nonparametric Matrix Factorization for Recorded Music

    David M. Blei;Perry R. Cook;Matthew Hoffman

  • Deep Probabilistic Programming

    Dustin Tran;Matthew D. Hoffman;Rif A. Saurous;Eugene Brevdo

  • TensorFlow Distributions

    Joshua V. Dillon;Ian Langmore;Dustin Tran;Eugene Brevdo

  • Sparse Stochastic Inference for Latent Dirichlet allocation

    David Mimno;Matt Hoffman;David Blei

  • Nonparametric variational inference

    Samuel Gershman;Matt Hoffman;David M. Blei

  • Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths

    Zhicheng Liu;Yang Wang;Mira Dontcheva;Matthew Hoffman

  • A variational analysis of stochastic gradient algorithms

    Stephan Mandt;Matthew D. Hoffman;David M. Blei

  • EASY AS CBA: A SIMPLE PROBABILISTIC MODEL FOR TAGGING MUSIC

    Matthew D. Hoffman;David M. Blei;Perry R. Cook

  • On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning

    Matthew D. Hoffman;Bobak Shahriari;Nando de Freitas

  • The Stan Math Library: Reverse-Mode Automatic Differentiation in C++

    Bob Carpenter;Matthew D. Hoffman;Marcus Brubaker;Daniel D. Lee

  • What Are Bayesian Neural Network Posteriors Really Like

    Pavel Izmailov;Sharad Vikram;Matthew Hoffman;Andrew Wilson

  • Generalizing Hamiltonian Monte Carlo with Neural Networks

    Daniel Levy;Matthew D. Hoffman;Jascha Sohl-Dickstein

  • Music Transformer

    Cheng-Zhi Anna Huang;Ashish Vaswani;Jakob Uszkoreit;Noam Shazeer

Frequent Co-Authors

David M. Blei
David M. Blei Columbia University
Perry R. Cook
Perry R. Cook Princeton University
Dustin Tran
Dustin Tran Google (United States)
Ryan P. Adams
Ryan P. Adams Princeton University
Hailin Jin
Hailin Jin Adobe Systems (United States)
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Daniel J. Lee
Daniel J. Lee Samsung (South Korea)
Andrew Gelman
Andrew Gelman Columbia University
Aaron Hertzmann
Aaron Hertzmann Adobe Systems (United States)
Caglar Gulcehre
Caglar Gulcehre DeepMind (United Kingdom)

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