World's Best Scientists 2026 revealed!
John P. Cunningham

John P. Cunningham

D-Index & Metrics

Computer Science

D-Index
45
Citations
14150
World Ranking
7025
National Ranking
3080

John P. Cunningham 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 John P. Cunningham 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: 135 publications — 21st percentile

21% of scientists in this discipline score the same or lower.

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

John P. Cunningham 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 John P. Cunningham 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2015 - Fellow of Alfred P. Sloan Foundation

Overview

John P. Cunningham is affiliated with Columbia University in the United States. Their research spans multiple fields, primarily in Computer Science and Neuroscience, with a considerable focus on subfields such as Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Statistics and Probability, and Molecular Biology.

The scientist's work covers a variety of topics, including:

  • Neural dynamics and brain function
  • Gaussian Processes and Bayesian Inference
  • Functional Brain Connectivity Studies
  • EEG and Brain-Computer Interfaces
  • Machine Learning and Data Classification
  • Artificial Intelligence in Healthcare and Education
  • Motor Control and Adaptation

John P. Cunningham has contributed to several well-known publication venues, often publishing most frequently in:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • eLife
  • Neuron
  • Nature Communications

Notable recent papers include:

  • "Almanac - Retrieval-Augmented Language Models for Clinical Medicine" (2024, NEJM AI)
  • "Neural Trajectories in the Supplementary Motor Area and Motor Cortex Exhibit Distinct Geometries, Compatible with Different Classes of Computation" (2020, Neuron)
  • "Flexible neural control of motor units" (2022, Nature Neuroscience)
  • "Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity" (2022, eLife)
  • "Predicting post-operative right ventricular failure using video-based deep learning" (2021, Nature Communications)

The scientist frequently collaborates with peers such as Geoff Pleiss, Liam Paninski, Mark M. Churchland, David M. Blei, and E. Kelly Buchanan.

In terms of recognition, John P. Cunningham was awarded the Alfred P. Sloan Foundation Fellowship in 2015.

Best Publications

  • Neural population dynamics during reaching

    Mark M. Churchland;John P. Cunningham;John P. Cunningham;Matthew T. Kaufman;Justin D. Foster

  • Dimensionality reduction for large-scale neural recordings.

    John P Cunningham;Byron M Yu

  • Stimulus onset quenches neural variability: a widespread cortical phenomenon

    Mark M. Churchland;Byron M. Yu;Byron M. Yu;John P. Cunningham;Leo P. Sugrue;Leo P. Sugrue

  • Influence of heart rate on the BOLD signal: the cardiac response function

    Catie Chang;John P. Cunningham;Gary H. Glover

  • Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity

    Byron M Yu;John P Cunningham;Gopal Santhanam;Stephen I. Ryu

  • A high-performance neural prosthesis enabled by control algorithm design

    Vikash Gilja;Paul Nuyujukian;Cindy A Chestek;John P Cunningham;John P Cunningham

  • Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine?

    Mark M. Churchland;John P. Cunningham;John P. Cunningham;Matthew T. Kaufman;Stephen I. Ryu;Stephen I. Ryu

  • Linear dimensionality reduction: survey, insights, and generalizations

    John P. Cunningham;Zoubin Ghahramani

  • Reorganization between preparatory and movement population responses in motor cortex

    Gamaleldin F. Elsayed;Antonio H. Lara;Matthew T. Kaufman;Mark M. Churchland

  • Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex

    Cynthia A Chestek;Vikash Gilja;Paul Nuyujukian;Justin D Foster

  • Bayesian Optimization with Inequality Constraints

    Jacob Gardner;Matt Kusner;Zhixiang;Kilian Weinberger

  • Towards the neural population doctrine.

    Shreya Saxena;John P Cunningham

  • Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response

    Abigail A. Russo;Abigail A. Russo;Sean R. Bittner;Sean R. Bittner;Sean M. Perkins;Jeffrey S. Seely;Jeffrey S. Seely

  • Empirical models of spiking in neural populations

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

  • Single-trial dynamics of motor cortex and their applications to brain-machine interfaces

    Jonathan C. Kao;Paul Nuyujukian;Stephen I. Ryu;Mark M. Churchland

  • A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces

    John Patrick Cunningham;John Patrick Cunningham;Paul Nuyujukian;Vikash Gilja;Cindy A Chestek

  • BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODELS

    Evan Archer;Il Memming Park;Lars Buesing;John Cunningham

  • Structure in neural population recordings: an expected byproduct of simpler phenomena?

    Gamaleldin F Elsayed;John P Cunningham

  • Neural Trajectories in the Supplementary Motor Area and Motor Cortex Exhibit Distinct Geometries, Compatible with Different Classes of Computation.

    Abigail A. Russo;Ramin Khajeh;Sean R. Bittner;Sean M. Perkins

  • Fast Kernel Learning for Multidimensional Pattern Extrapolation

    Andrew Wilson;Elad Gilboa;John P Cunningham;Arye Nehorai

  • Linear dynamical neural population models through nonlinear embeddings

    Yuanjun Gao;Evan W. Archer;Liam Paninski;John P. Cunningham

Frequent Co-Authors

Liam Paninski
Liam Paninski Columbia University
Krishna V. Shenoy
Krishna V. Shenoy Stanford University
Stephen I. Ryu
Stephen I. Ryu Stanford University
Mark M. Churchland
Mark M. Churchland Columbia University
Byron M. Yu
Byron M. Yu Carnegie Mellon University
Maneesh Sahani
Maneesh Sahani University College London
Anne K. Churchland
Anne K. Churchland University of California, Los Angeles
William T. Newsome
William T. Newsome Stanford University
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Arye Nehorai
Arye Nehorai Washington University in St. Louis

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