World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
15718
World Ranking
7005
National Ranking
3069

John J. Grefenstette 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 J. Grefenstette 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 102 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

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

John J. Grefenstette 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 J. Grefenstette sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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.

Overview

John J. Grefenstette is affiliated with the University of Pittsburgh in the United States. Their research spans several fields, with a primary focus on mathematics and its applications in epidemiology and related areas.

Their scholarly contributions include publications in notable venues such as PLoS Computational Biology, Frontiers in Artificial Intelligence, and the Journal of Applied Research on Children Informing Policy for Children at Risk. The recent papers authored or co-authored by John J. Grefenstette include:

  • Detecting critical slowing down in high-dimensional epidemiological systems, 2020, PLoS Computational Biology
  • Planning as Inference in Epidemiological Dynamics Models, 2022, Frontiers in Artificial Intelligence
  • Interventions in measles outbreaks: the potential reduction in cases associated with school suspension and vaccination interventions, 2020, Journal of Applied Research on Children Informing Policy for Children at Risk

Their work covers multiple main topics, reflecting interdisciplinary interests in epidemiology, ecology, and public health. These topics include:

  • COVID-19 epidemiological studies
  • Ecosystem dynamics and resilience
  • Mental Health Research Topics
  • Climate change impacts on agriculture
  • Complex Systems and Decision Making
  • Vaccine Coverage and Hesitancy
  • Virology and Viral Diseases

Within the broader field of mathematics, John J. Grefenstette's subfields include Modeling and Simulation, Global and Planetary Change, Experimental and Cognitive Psychology, Ecology, Evolution, Behavior and Systematics, and Management Science and Operations Research.

Frequent collaborators in their research have included Mary G. Krauland, Tobias Brett, Marco Ajelli, Quan-Hui Liu, and Willem G. van Panhuis. These partnerships suggest a collaborative approach to investigating epidemiological dynamics and related systems.

Their publications often address complex challenges in modeling epidemiological processes and responses to infectious diseases, emphasizing computational and inferential techniques. Given the topics and venues where they publish, their work contributes to advancing understanding in epidemiological modeling, disease intervention strategies, and broader ecological and mental health impacts.

Best Publications

  • Optimization of Control Parameters for Genetic Algorithms

    John J. Grefenstette

  • Genetic Algorithms for the Traveling Salesman Problem

    John J. Grefenstette;Rajeev Gopal;Brian J. Rosmaita;Dirk Van Gucht

  • Genetic algorithms for changing environments

    John J. Grefenstette

  • A systematic review of barriers to data sharing in public health

    Willem G van Panhuis;Proma Paul;Claudia Emerson;John Grefenstette

  • Credit assignment in rule discovery systems based on genetic algorithms

    John J. Grefenstette

  • Genetic Algorithms for Tracking Changing Environments

    Helen G. Cobb;John J. Grefenstette

  • Genetic Algorithms in Noisy Environments

    J. Michael Fitzpatrick;John J. Grefenstette

  • Evolutionary algorithms for reinforcement learning

    David E. Moriarty;Alan C. Schultz;John J. Grefenstette

  • Learning Sequential Decision Rules Using Simulation Models and Competition

    John J. Grefenstette;Connie Loggia Ramsey;Alan C. Schultz

  • A parallel genetic algorithm

    Chrisila B. Pettey;Michael R. Leuze;John J. Grefenstette

  • Deception Considered Harmful.

    John J. Grefenstette

  • Case-Based Initialization of Genetic Algorithms

    Connie Loggia Ramsey;John J. Grefenstette

  • FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations

    John J Grefenstette;Shawn T Brown;Roni Rosenfeld;Jay DePasse

  • A Coevolutionary Approach to Learning Sequential Decision Rules

    Mitchell A. Potter;Kenneth A. De Jong;John J. Grefenstette

  • Genetic Search with Approximate Function Evaluation

    John J. Grefenstette;J. Michael Fitzpatrick

  • Evolvability in dynamic fitness landscapes: a genetic algorithm approach

    J.J. Grefenstette

  • Genetic Algorithms in Noisy Environments

    Unknown

  • A computer simulation of vaccine prioritization, allocation, and rationing during the 2009 H1N1 influenza pandemic.

    Bruce Y. Lee;Shawn T. Brown;Shawn T. Brown;George W. Korch;Philip C. Cooley

  • Lamarckian Learning in Multi-Agent Environments.

    John J. Grefenstette

  • Multi-objective learning via genetic algorithms

    J. David Schaffer;John J. Grefenstette

  • Genetic algorithms and machine learning

    Unknown

  • Genetic algorithms and their applications

    John J. Grefenstette

  • Proceedings of the First International Conference on Genetic Algorithms and their Applications

    John J. Grefenstette

  • parallel genetic algorithm

    C.B. Pettey;M.R. Leuze;J.J. Grefenstette

Frequent Co-Authors

Donald S. Burke
Donald S. Burke University of Pittsburgh
Alan C. Schultz
Alan C. Schultz United States Naval Research Laboratory
Richard K. Zimmerman
Richard K. Zimmerman University of Pittsburgh
Curtis P. Van Tassell
Curtis P. Van Tassell Agricultural Research Service
Roni Rosenfeld
Roni Rosenfeld Carnegie Mellon University
J. Michael Fitzpatrick
J. Michael Fitzpatrick Vanderbilt University
Kenneth de Jong
Kenneth de Jong George Mason University
Jessica G. Burke
Jessica G. Burke University of Pittsburgh
John L. Williams
John L. Williams University of Adelaide
William Barendse
William Barendse Commonwealth Scientific and Industrial Research Organisation

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Related Online Degrees & Career Pathways

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