World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
8340
World Ranking
11033
National Ranking
4590

Rajarshi Das 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 Rajarshi Das 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: 98 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

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

Rajarshi Das 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 Rajarshi Das 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: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Best Publications

  • A study of control parameters affecting online performance of genetic algorithms for function optimization

    J. David Schaffer;Richard A. Caruana;Larry J. Eshelman;Rajarshi Das

  • Optimal power allocation in server farms

    Anshul Gandhi;Mor Harchol-Balter;Rajarshi Das;Charles Lefurgy

  • Gaussian LDA for Topic Models with Word Embeddings

    Rajarshi Das;Manzil Zaheer;Chris Dyer

  • Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning.

    Rajarshi Das;Shehzaad Dhuliawala;Manzil Zaheer;Luke Vilnis

  • Agent-human interactions in the continuous double auction

    Rajarshi Das;James E. Hanson;Jeffrey O. Kephart;Gerald Tesauro

  • A Multi-Agent Systems Approach to Autonomic Computing

    Gerald Tesauro;David M. Chess;William E. Walsh;Rajarshi Das

  • Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

    Rajarshi Das;Shehzaad Dhuliawala;Manzil Zaheer;Luke Vilnis

  • Genetic Reinforcement Learning for Neurocontrol Problems

    Darrell Whitley;Stephen Dominic;Rajarshi Das;Charles W. Anderson

  • Achieving Self-Management via Utility Functions

    J.O. Kephart;R. Das

  • Evolving Globally Synchronized Cellular Automata

    Rajarshi Das;James P. Crutchfield;Melanie Mitchell;James E. Hanson

  • A Genetic Algorithm Discovers Particle-Based Computation in Cellular Automata

    Rajarshi Das;Melanie Mitchell;James P. Crutchfield

  • Analyzing Complex Strategic Interactions in Multi-Agent Systems

    William E. Walsh;Rajarshi Das;Gerald Tesauro;Jeffrey O. Kephart

  • Coordinating Multiple Autonomic Managers to Achieve Specified Power-Performance Tradeoffs

    J.O. Kephart;Hoi Chan;R. Das;D.W. Levine

  • High-performance bidding agents for the continuous double auction

    Gerald Tesauro;Rajarshi Das

  • Autonomic multi-agent management of power and performance in data centers

    Rajarshi Das;Jeffrey O. Kephart;Charles Lefurgy;Gerald Tesauro

  • Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning

    Gerald Tesauro;Rajarshi Das;Hoi Chan;Jeffrey Kephart

  • On the use of hybrid reinforcement learning for autonomic resource allocation

    Gerald Tesauro;Nicholas K. Jong;Rajarshi Das;Mohamed N. Bennani

  • Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering

    Rajarshi Das;Shehzaad Dhuliawala;Manzil Zaheer;Andrew McCallum

  • Genetic Reinforcement Learning with Multilayer Neural Networks.

    L. Darrell Whitley;Stephen Dominic;Rajarshi Das

  • Case-based Reasoning for Natural Language Queries over Knowledge Bases

    Rajarshi Das;Manzil Zaheer;Dung Thai;Ameya Godbole

Frequent Co-Authors

Andrew McCallum
Andrew McCallum University of Massachusetts Amherst
Xiaoxiao Guo
Xiaoxiao Guo The University of Texas at Dallas
Mo Yu
Mo Yu IBM (United States)
Hamed Zamani
Hamed Zamani University of Massachusetts Amherst
Akshay Krishnamurthy
Akshay Krishnamurthy Microsoft (United States)
Adam Trischler
Adam Trischler Microsoft (United States)
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Siva Reddy
Siva Reddy McGill University
Antoine Bordes
Antoine Bordes Facebook (United States)
Chris Dyer
Chris Dyer Google (United States)

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