World's Best Scientists 2026 revealed!
Matthias Seeger

Matthias Seeger

D-Index & Metrics

Computer Science

D-Index
42
Citations
15511
World Ranking
8154
National Ranking
399

Matthias Seeger 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 Matthias Seeger 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: 104 publications — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Matthias Seeger 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 Matthias Seeger 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Matthias Seeger is affiliated with Amazon in Germany and has contributed significantly to the field of computer science through numerous research publications. The primary focus of their work lies at the intersection of artificial intelligence, machine learning, and operations research, with particular emphasis on Bayesian inference and optimization techniques.

The main fields of study covered by Matthias Seeger include:

  • Computer Science

Within this broad area, their subfields of research extend to:

  • Artificial Intelligence
  • Management Science and Operations Research
  • Computational Theory and Mathematics
  • Signal Processing
  • Computer Vision and Pattern Recognition

Seeger's research encompasses a range of topics, notably:

  • Machine Learning and Data Classification
  • Gaussian Processes and Bayesian Inference
  • Machine Learning and Algorithms
  • Advanced Multi-Objective Optimization Algorithms
  • Advanced Bandit Algorithms Research
  • Time Series Analysis and Forecasting
  • Forecasting Techniques and Applications

Their publication record features several papers predominantly appearing in the venue arXiv (Cornell University), totaling 14 publications, as well as one paper in Foundations and Trends® in Machine Learning.

Recent papers authored or coauthored by Matthias Seeger include:

  • "LEEP: A New Measure to Evaluate Transferability of Learned Representations" (2020) in arXiv (Cornell University)
  • "Cost-aware Bayesian Optimization" (2020) in arXiv (Cornell University)
  • "Model-based Asynchronous Hyperparameter and Neural Architecture Search" (2020) in arXiv (Cornell University)
  • "Overfitting in Bayesian Optimization: an empirical study and early-stopping solution" (2021) in arXiv (Cornell University)
  • "Amazon SageMaker Autopilot: a white box AutoML solution at scale" (2020) in arXiv (Cornell University)

Frequent collaborators working with Matthias Seeger include:

  • Cédric Archambeau
  • Valerio Perrone
  • Aaron Klein
  • Michele Donini
  • Luca Franceschi

Best Publications

  • Using the Nyström Method to Speed Up Kernel Machines

    Christopher K. I. Williams;Matthias Seeger

  • Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

    Niranjan Srinivas;Andreas Krause;Matthias Seeger;Sham M. Kakade

  • Gaussian processes for machine learning.

    Matthias W. Seeger

  • Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting

    N. Srinivas;A. Krause;S. M. Kakade;M. Seeger

  • Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

    Niranjan Srinivas;Andreas Krause;Sham M. Kakade;Matthias Seeger

  • Learning with Labeled and Unlabeled Data

    Matthias Seeger

  • Fast Sparse Gaussian Process Methods: The Informative Vector Machine

    Ralf Herbrich;Neil D. Lawrence;Matthias Seeger

  • Fast Forward Selection to Speed Up Sparse Gaussian Process Regression

    Matthias W. Seeger;Christopher K. I. Williams;Neil D. Lawrence

  • Deep State Space Models for Time Series Forecasting

    Syama Sundar Rangapuram;Matthias W. Seeger;Jan Gasthaus;Lorenzo Stella

  • Model Learning with Local Gaussian Process Regression

    Duy Nguyen-Tuong;Matthias W. Seeger;Jan Peters

  • Pac-bayesian generalisation error bounds for gaussian process classification

    Matthias Seeger

  • Semiparametric Latent Factor Models

    Yee Whye Teh;Matthias W. Seeger;Michael I. Jordan

  • Bayesian Inference and Optimal Design for the Sparse Linear Model

    Matthias W. Seeger

  • Local Gaussian process regression for real time online model learning and control

    Duy Nguyen-Tuong;Jan Peters;Matthias Seeger

  • The Effect of the Input Density Distribution on Kernel-based Classifiers

    Christopher K. I. Williams;Matthias Seeger

  • Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations

    Matthias Seeger

  • Expectation Propagation for Exponential Families

    Matthias Seeger

  • Optimization of k-space trajectories for compressed sensing by Bayesian experimental design

    Matthias Seeger;Hannes Nickisch;Rolf Pohmann;Bernhard Schölkopf

  • Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers

    Matthias Seeger

  • Learning Inverse Dynamics: A Comparison

    Duy Nguyen-Tuong;Jan Peters;Matthias W. Seeger;Bernhard Schölkopf

  • Local Gaussian Process Regression for Real Time Online Model Learning

    Duy Nguyen-tuong;Jan R. Peters;Matthias Seeger

  • LEEP: A New Measure to Evaluate Transferability of Learned Representations

    Cuong V. Nguyen;Tal Hassner;Matthias Seeger;Cedric Archambeau

Frequent Co-Authors

Jan Peters
Jan Peters Technical University of Darmstadt
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Neil D. Lawrence
Neil D. Lawrence University of Cambridge
Matthias Bethge
Matthias Bethge University of Tübingen
Sham M. Kakade
Sham M. Kakade Harvard University
Andreas Krause
Andreas Krause ETH Zurich
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Ralf Herbrich
Ralf Herbrich Hasso Plattner Institute
Tal Hassner
Tal Hassner Facebook (United States)
Koji Tsuda
Koji Tsuda University of Tokyo

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