World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
30
Citations
4433
World Ranking
14017
National Ranking
5568

Prasad Tadepalli 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 Prasad Tadepalli 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: 199 publications — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Prasad Tadepalli 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 Prasad Tadepalli 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: 30 D-Index — 3rd percentile

3% of scientists in this discipline score the same or lower.

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

Overview

Prasad Tadepalli is affiliated with Oregon State University in the United States and specializes in the field of Computer Science, with a significant focus on Artificial Intelligence.

The scientist's work encompasses multiple subfields, including Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Computational Theory and Mathematics, and Biophysics.

The main research topics covered in Tadepalli's publications are:

  • Explainable Artificial Intelligence (XAI)
  • Reinforcement Learning in Robotics
  • Natural Language Processing Techniques
  • Bayesian Modeling and Causal Inference
  • Topic Modeling
  • Artificial Intelligence in Games
  • Cell Image Analysis Techniques

Recent papers authored or coauthored by Prasad Tadepalli address diverse areas within AI and machine learning. Selected recent works include:

  • "Planning in Factored Action Spaces with Symbolic Dynamic Programming," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Improving Multilingual Translation by Representation and Gradient Regularization," 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • "RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction," 2021, Proceedings of the International Conference on Automated Planning and Scheduling
  • "One Explanation is Not Enough: Structured Attention Graphs for Image Classification," 2020, arXiv (Cornell University)
  • "Parametrically Retargetable Decision-Makers Tend To Seek Power," 2022, arXiv (Cornell University)

Tadepalli frequently publishes in several venues, with a notable concentration in arXiv (Cornell University) and the Proceedings of the AAAI Conference on Artificial Intelligence. Other publication venues include Applied AI Letters, the Conference on Empirical Methods in Natural Language Processing, and the International Conference on Automated Planning and Scheduling.

Frequent collaborators include the following coauthors:

  • Alan Fern
  • Sriraam Natarajan
  • Stefan Lee
  • Harsha Kokel
  • Fuxin Li

Best Publications

  • Multi-task reinforcement learning: a hierarchical Bayesian approach

    Aaron Wilson;Alan Fern;Soumya Ray;Prasad Tadepalli

  • Active Learning with Committees for Text Categorization

    Ray Liere;Prasad Tadepalli

  • Dynamic preferences in multi-criteria reinforcement learning

    Sriraam Natarajan;Prasad Tadepalli

  • Relational Reinforcement Learning: An Overview

    Prasad Tadepalli;Robert Givan;Kurt Driessens

  • Structured machine learning: the next ten years

    Thomas G. Dietterich;Pedro Domingos;Lise Getoor;Stephen Muggleton

  • A Bayesian Approach for Policy Learning from Trajectory Preference Queries

    Aaron Wilson;Alan Fern;Prasad Tadepalli

  • Transfer in variable-reward hierarchical reinforcement learning

    Neville Mehta;Sriraam Natarajan;Prasad Tadepalli;Alan Fern

  • Model-based average reward reinforcement learning

    Prasad Tadepalli;DoKyeong Ok

  • Lower bounding Klondike Solitaire with Monte-Carlo planning

    Ronald Bjarnason;Alan Fern;Prasad Tadepalli

  • A decision-theoretic model of assistance

    Alan Fern;Sriraam Natarajan;Kshitij Judah;Prasad Tadepalli

  • Automatic discovery and transfer of MAXQ hierarchies

    Neville Mehta;Soumya Ray;Prasad Tadepalli;Thomas Dietterich

  • Multi-Agent Inverse Reinforcement Learning

    Sriraam Natarajan;Gautam Kunapuli;Kshitij Judah;Prasad Tadepalli

  • Maximizing the predictive value of production rules

    S. M. Weiss;R. S. Galen;P. V. Tadepalli

  • Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference

    Reza Ghaeini;Xiaoli Z. Fern;Prasad Tadepalli

  • Learning first-order probabilistic models with combining rules

    Sriraam Natarajan;Prasad Tadepalli;Thomas G. Dietterich;Alan Fern

  • Using trajectory data to improve bayesian optimization for reinforcement learning

    Aaron Wilson;Alan Fern;Prasad Tadepalli

  • Lazy explanation-based learning: a solution to the intractable theory problem

    Prasad Tadepalli

  • A decision-theoretic model of assistance

    Alan Fern;Sriraam Natarajan;Kshitij Judah;Prasad Tadepalli

  • Imitation learning in relational domains: a functional-gradient boosting approach

    Sriraam Natarajan;Saket Joshi;Prasad Tadepalli;Kristian Kersting

  • Learning Goal-Decomposition Rules using Exercises

    Chandra Reddy;Prasad Tadepalli

  • Learning goal-decomposition rules using exercises

    Chandra Reddy;Prasad Tadepalli

  • Learning first-order probabilistic models with combining rules

    Sriraam Natarajan;Prasad Tadepalli;Eric Altendorf;Thomas G. Dietterich

Frequent Co-Authors

Alan Fern
Alan Fern Oregon State University
Xiaoli Z. Fern
Xiaoli Z. Fern Oregon State University
Thomas G. Dietterich
Thomas G. Dietterich Oregon State University
Jude W. Shavlik
Jude W. Shavlik University of Wisconsin–Madison
Stefan Lee
Stefan Lee Oregon State University
Kristian Kersting
Kristian Kersting Technical University of Darmstadt
Weng-Keen Wong
Weng-Keen Wong Oregon State University
Stuart Russell
Stuart Russell University of California, Berkeley
Sridhar Mahadevan
Sridhar Mahadevan University of Massachusetts Amherst
Deborah L. McGuinness
Deborah L. McGuinness Rensselaer Polytechnic Institute

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