World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
5071
World Ranking
11722
National Ranking
578

Jakob H. Macke 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 Jakob H. Macke 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: 143 publications — 24th percentile

24% of scientists in this discipline score the same or lower.

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

Jakob H. Macke 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 Jakob H. Macke 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Jakob H. Macke is affiliated with the Max Planck Institute for Intelligent Systems in Germany. Their research spans multiple disciplines, primarily focusing on neuroscience and computer science.

The main fields of study include:

  • Neuroscience
  • Computer Science

Within these fields, their work concentrates on the following subfields:

  • Cognitive Neuroscience
  • Artificial Intelligence
  • Cellular and Molecular Neuroscience
  • Astronomy and Astrophysics
  • Molecular Biology

The core topics that characterize this scientist's research are:

  • Neural dynamics and brain function
  • Functional Brain Connectivity Studies
  • Gaussian Processes and Bayesian Inference
  • Neural Networks and Applications
  • Pulsars and Gravitational Waves Research
  • EEG and Brain-Computer Interfaces
  • Advanced Memory and Neural Computing

Jakob H. Macke has contributed to scientific literature through publications in the following venues:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Nature Methods
  • eLife

Notable recent papers include:

  • Training deep neural density estimators to identify mechanistic models of neural dynamics, 2020, eLife
  • Deep learning enables fast and dense single-molecule localization with high accuracy, 2021, Nature Methods
  • Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference, 2023, Physical Review Letters
  • The impact of neuron morphology on cortical network architecture, 2022, Cell Reports
  • Simulation Intelligence: Towards a New Generation of Scientific Methods, 2021, arXiv (Cornell University)

Their collaborative work is marked by repeated partnerships with several frequent co-authors:

  • Michael Deistler
  • Pedro J. Gonçalves
  • Richard Gao
  • Julius Vetter
  • Cornelius Schröder

Best Publications

  • Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data

    Heiko H. Schütt;Heiko H. Schütt;Stefan Harmeling;Jakob H. Macke;Jakob H. Macke;Felix A. Wichmann

  • Training deep neural density estimators to identify mechanistic models of neural dynamics.

    Pedro J Gonçalves;Pedro J Gonçalves;Jan-Matthis Lueckmann;Jan-Matthis Lueckmann;Michael Deistler;Michael Deistler;Marcel Nonnenmacher;Marcel Nonnenmacher

  • Neural population coding: combining insights from microscopic and mass signals

    Stefano Panzeri;Stefano Panzeri;Jakob H. Macke;Jakob H. Macke;Joachim Gross;Christoph Kayser

  • Quantifying the effect of intertrial dependence on perceptual decisions.

    Ingo Fründ;Ingo Fründ;Felix A. Wichmann;Jakob H. Macke

  • Generating spike trains with specified correlation coefficients

    Jakob H. Macke;Philipp Berens;Alexander S. Ecker;Andreas S. Tolias

  • Deep learning enables fast and dense single-molecule localization with high accuracy.

    Artur Speiser;Lucas-Raphael Müller;Philipp Hoess;Ulf Matti

  • Empirical models of spiking in neural populations

    Jakob H Macke;Lars Buesing;John P Cunningham;Byron M Yu

  • Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

    Robert Küffner;Neta Zach;Raquel Norel;Johann Hawe

  • Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

    Philipp Berens;Jeremy Freeman;Thomas Deneux;Nikolay Chenkov

  • Inferring decoding strategies from choice probabilities in the presence of correlated variability

    Ralf M Haefner;Sebastian Gerwinn;Jakob H Macke;Matthias Bethge

  • sbi: A toolkit for simulation-based inference

    Álvaro Tejero-Cantero;Jan Boelts;Michael Deistler;Jan-Matthis Lueckmann

  • Intrinsic dimension of data representations in deep neural networks

    Alessio Ansuini;Alessandro Laio;Jakob H. Macke;Davide Zoccolan

  • Flexible statistical inference for mechanistic models of neural dynamics

    Jan-Matthis Lueckmann;Pedro J. Goncalves;Giacomo Bassetto;Kaan Öcal

  • Intrinsic dimension of data representations in deep neural networks

    Alessio Ansuini;Alessandro Laio;Jakob H. Macke;Davide Zoccolan

  • Automatic Posterior Transformation for Likelihood-Free Inference

    David S. Greenberg;Marcel Nonnenmacher;Jakob H. Macke

  • Analyzing biological and artificial neural networks: challenges with opportunities for synergy?

    David G. T. Barrett;Ari S. Morcos;Jakob H. Macke

  • Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity

    Jakob H. Macke;Manfred Opper;Matthias Bethge

  • Flexible statistical inference for mechanistic models of neural dynamics

    Jan-Matthis Lueckmann;Pedro J. Goncalves;Giacomo Bassetto;Kaan Öcal

  • Bayesian inference for generalized linear models for spiking neurons

    Sebastian Gerwinn;Sebastian Gerwinn;Jakob H. Macke;Jakob H. Macke;Jakob H. Macke;Matthias Bethge;Matthias Bethge

  • Contour−propagation algorithms for semi−automated reconstruction of neural processes

    Jakob H. Macke;Nina Maack;Rocky Gupta;Winfried Denk

  • Likelihood-free inference with emulator networks

    Jan-Matthis Lueckmann;Giacomo Bassetto;Theofanis Karaletsos;Jakob H. Macke

  • Spectral learning of linear dynamics from generalised-linear observations with application to neural population data

    Lars Buesing;Jakob H Macke;Maneesh Sahani

  • Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys

    Shih pi Ku;Arthur Gretton;Jakob Macke;Nikos K. Logothetis

Frequent Co-Authors

Matthias Bethge
Matthias Bethge University of Tübingen
Felix A. Wichmann
Felix A. Wichmann University of Tübingen
Andreas S. Tolias
Andreas S. Tolias Baylor College of Medicine
Maneesh Sahani
Maneesh Sahani University College London
Manfred Opper
Manfred Opper Technical University of Berlin
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Nikos K. Logothetis
Nikos K. Logothetis Chinese Academy of Sciences
Hans-Christian Hege
Hans-Christian Hege Zuse Institute Berlin
Iain Murray
Iain Murray University of Edinburgh
Jonathan W. Pillow
Jonathan W. Pillow Princeton University

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