World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
42
Citations
10209
World Ranking
6397
National Ranking
1757

Ronald Parr publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Ronald Parr sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 77 publications — 3rd percentile

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

The last bar groups every scientist with 804 publications or more.

Ronald Parr D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Ronald Parr sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 42 D-Index — 35th percentile

35% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Research.com Recognitions

  • 1973 - Member of the National Academy of Sciences

Overview

Ronald Parr is affiliated with Duke University in the United States. Their research primarily focuses on computer science, with specific contributions to fields such as artificial intelligence, management science and operations research, civil and structural engineering, economics and econometrics, and statistical and nonlinear physics.

Their scholarly output includes eleven publications in artificial intelligence and related areas. Key topics covered in their work include:

  • Reinforcement Learning in Robotics
  • Imbalanced Data Classification Techniques
  • Machine Learning and Data Classification
  • Machine Learning and Algorithms
  • Game Theory and Applications
  • Infrastructure Resilience and Vulnerability Analysis
  • Game Theory and Voting Systems

Ronald Parr has collaborated frequently with several researchers, including Lesia Semenova, Cynthia Rudin, George Konidaris, Michael L. Littman, and Kavosh Asadi.

Their recent papers include:

  • On the Existence of Simpler Machine Learning Models, 2022, 2022 ACM Conference on Fairness, Accountability, and Transparency
  • Computing Optimal Strategies to Commit to in Stochastic Games, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Deep Radial-Basis Value Functions for Continuous Control, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Fitted Q-Learning for Relational Domains, 2020, arXiv (Cornell University)
  • Deep Radial-Basis Value Functions for Continuous Control, 2020, arXiv (Cornell University)

The main venues where their work has appeared are arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence, and the 2022 ACM Conference on Fairness, Accountability, and Transparency. They have also contributed to PubMed.

Ronald Parr has been recognized as a member of the National Academy of Sciences since 1973.

Best Publications

  • Least-squares policy iteration

    Michail G. Lagoudakis;Ronald Parr

  • Reinforcement Learning with Hierarchies of Machines

    Ronald Parr;Stuart J. Russell

  • Efficient solution algorithms for factored MDPs

    Carlos Guestrin;Daphne Koller;Ronald Parr;Shobha Venkataraman

  • Multiagent Planning with Factored MDPs

    Carlos Guestrin;Daphne Koller;Ronald Parr

  • DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks

    Austin Eliazar;Ronald Parr

  • Coordinated Reinforcement Learning

    Carlos Guestrin;Michail G. Lagoudakis;Ronald Parr

  • Making Rational Decisions Using Adaptive Utility Elicitation

    Urszula Chajewska;Daphne Koller;Ronald Parr

  • Hierarchical control and learning for markov decision processes

    Ronald Edward Parr;Stuart Russell

  • Bayesian Fault Detection and Diagnosis in Dynamic Systems

    Uri Lerner;Ronald Parr;Daphne Koller;Gautam Biswas

  • DP-SLAM 2.0

    A.I. Eliazar;R. Parr

  • Approximating optimal policies for partially observable stochastic domains

    Ronald Parr;Stuart Russell

  • Policy Iteration for Factored MDPs

    Daphne Koller;Ronald Parr

  • An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning

    Ronald Parr;Lihong Li;Gavin Taylor;Christopher Painter-Wakefield

  • Complexity of computing optimal stackelberg strategies in security resource allocation games

    Dmytro Korzhyk;Vincent Conitzer;Ronald Parr

  • Reinforcement learning as classification: leveraging modern classifiers

    Michail G. Lagoudakis;Ronald Parr

  • Computing Factored Value Functions for Policies in Structured MDPs

    Daphne Koller;Ronald Parr

  • Analyzing feature generation for value-function approximation

    Ronald Parr;Christopher Painter-Wakefield;Lihong Li;Michael Littman

  • Max-norm projections for factored MDPs

    Carlos Guestrin;Daphne Koller;Ronald Parr

  • Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms

    Uri Lerner;Ronald Parr

  • Kernelized value function approximation for reinforcement learning

    Gavin Taylor;Ronald Parr

Frequent Co-Authors

Daphne Koller
Daphne Koller insitro Inc.
Vincent Conitzer
Vincent Conitzer Carnegie Mellon University
Lawrence Carin
Lawrence Carin Duke University
Michael L. Littman
Michael L. Littman Brown University
Carlos Guestrin
Carlos Guestrin Stanford University
Lihong Li
Lihong Li Amazon (United States)
Cynthia Rudin
Cynthia Rudin Duke University
George Konidaris
George Konidaris Brown University
Shlomo Zilberstein
Shlomo Zilberstein University of Massachusetts Amherst
Min Wang
Min Wang Google (United States)

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