World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
11371
World Ranking
11889
National Ranking
4845

Eric Mjolsness 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 Eric Mjolsness 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: 153 publications — 28th percentile

28% of scientists in this discipline score the same or lower.

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

Eric Mjolsness 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 Eric Mjolsness 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: 34 D-Index — 16th percentile

16% 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

  • 2014 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Eric Mjolsness is affiliated with the University of California, Irvine in the United States. Their research spans several interconnected domains within biochemistry, genetics, and molecular biology, focusing extensively on molecular biology and biophysics, as well as specialized areas such as artificial intelligence, signal processing, and computational theory and mathematics.

Their scientific contributions are reflected in numerous publications, with a significant number appearing in venues such as arXiv (Cornell University), Biophysical Journal, bioRxiv (Cold Spring Harbor Laboratory), Nature Cell Biology, and Molecular Systems Biology.

Frequent coauthors who have collaborated with Eric Mjolsness include Cory Braker Scott, Terrence J. Sejnowski, Thomas M. Bartol, Matthew Hur, and Jacob Kim.

The main topics addressed in their work encompass:

  • Gene Regulatory Network Analysis
  • Bioinformatics and Genomic Networks
  • Cell Image Analysis Techniques
  • Cellular Automata and Applications
  • Advanced Electron Microscopy Techniques and Applications
  • Neural dynamics and brain function
  • Computational Physics and Python Applications

Among their recent papers are:

  • Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing, 2020, Nature Cell Biology
  • SBML Level 3: an extensible format for the exchange and reuse of biological models, 2020, Molecular Systems Biology
  • Graph diffusion distance: Properties and efficient computation, 2021, PLoS ONE
  • Detection and prediction of a beam-driven mode in field-reversed configuration plasma with recurrent neural networks, 2020, Nuclear Fusion
  • Explicit Calculation of Structural Commutation Relations for Stochastic and Dynamical Graph Grammar Rule Operators in Biological Morphodynamics, 2022, Frontiers in Systems Biology

Eric Mjolsness was awarded the Fellow of the American Association for the Advancement of Science (AAAS) in 2014.

Best Publications

  • The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

    M. Hucka;A. Finney;H. M. Sauro;H. Bolouri;H. Bolouri

  • Fast and globally convergent pose estimation from video images

    C.-P. Lu;G.D. Hager;E. Mjolsness

  • New algorithms for 2D and 3D point matching: pose estimation and correspondence

    Steven Gold;Anand Rangarajan;Chien-Ping Lu;Suguna Pappu

  • A connectionist model of development

    Eric Mjolsness;David H. Sharp;David H. Sharp;John Reinitz

  • Machine learning for science: state of the art and future prospects.

    Eric Mjolsness;Dennis DeCoste

  • Animation of plant development

    Przemyslaw Prusinkiewicz;Mark S. Hammel;Eric Mjolsness

  • SBML Level 3: an extensible format for the exchange and reuse of biological models

    Sarah M. Keating;Sarah M. Keating;Dagmar Waltemath;Matthias König;Fengkai Zhang

  • A robust point-matching algorithm for autoradiograph alignment

    Anand Rangarajan;Haili Chui;Eric Mjolsness;Suguna Pappu

  • Model for cooperative control of positional information in Drosophila by bicoid and maternal hunchback.

    John Reinitz;John Reinitz;Eric Mjolsness;David H. Sharp

  • Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations

    Bruce E. Shapiro;Andre Levchenko;Elliot M. Meyerowitz;Barbara J. Wold

  • Modeling the organization of the WUSCHEL expression domain in the shoot apical meristem

    Henrik Jönsson;Marcus Heisler;G. Venugopala Reddy;Vikas Agrawal

  • New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence

    Steven Gold;Chien-Ping Lu;Anand Rangarajan;Suguna Pappu

  • Analysis of ultrasound images in the presence of contrast agent

    Howard Dittrich;Harold Levene;Eric Mjolsness

  • A novel optimizing network architecture with applications

    Anand Rangarajan;Steven Gold;Eric Mjolsness

  • Scaling, machine learning, and genetic neural nets

    Eric Mjolsness;David H Sharp;Bradley K Alpert

  • Algebraic transformations of objective functions

    Eric Mjolsness;Charles Garrett

  • A Lagrangian relaxation network for graph matching

    A. Rangarajan;E.D. Mjolsness

  • Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures

    Steven Gold;Anand Rangarajan;Eric Mjolsness

  • Translation-invariant mixture models for curve clustering

    Darya Chudova;Scott Gaffney;Eric Mjolsness;Padhraic Smyth

  • Optimization in model matching and perceptual organization

    Eric Mjolsness;Gene Gindi;P. Anandan

  • Towards learned traversability for robot navigation: From underfoot to the far field

    Andrew Howard;Michael J. Turmon;Larry H. Matthies;Benyang Tang

Frequent Co-Authors

Elliot M. Meyerowitz
Elliot M. Meyerowitz California Institute of Technology
Anand Rangarajan
Anand Rangarajan University of Florida
Terrence J. Sejnowski
Terrence J. Sejnowski Salk Institute for Biological Studies
Henrik Jönsson
Henrik Jönsson University of Cambridge
Barbara J. Wold
Barbara J. Wold California Institute of Technology
Andre Levchenko
Andre Levchenko Yale University
Steve Chien
Steve Chien Jet Propulsion Lab
G. Wesley Hatfield
G. Wesley Hatfield University of California, Irvine
Michael Hucka
Michael Hucka California Institute of Technology
John C. Doyle
John C. Doyle California Institute of Technology

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