World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
66
Citations
24694
World Ranking
2273
National Ranking
1138

J. Andrew Bagnell 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 J. Andrew Bagnell 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: 190 publications — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

J. Andrew Bagnell 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 J. Andrew Bagnell 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: 66 D-Index — 84th percentile

84% of scientists in this discipline score the same or lower.

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

Overview

J. Andrew Bagnell is affiliated with Carnegie Mellon University in the United States and specializes in the field of Computer Science. Their research primarily focuses on Artificial Intelligence, with significant contributions across several subfields including Computer Vision and Pattern Recognition, Control and Systems Engineering, Management Science and Operations Research, and Computer Networks and Communications.

The main topics of their scholarly work include:

  • Reinforcement Learning in Robotics
  • Machine Learning and Algorithms
  • Robotic Path Planning Algorithms
  • Robot Manipulation and Learning
  • Domain Adaptation and Few-Shot Learning
  • Advanced Bandit Algorithms Research
  • Evolutionary Algorithms and Applications

Bagnell has published extensively in venues such as arXiv (Cornell University), with 23 papers, and the Proceedings of the AAAI Conference on Artificial Intelligence, contributing 2 publications. Selected recent papers include:

  • Feedback in Imitation Learning: The Three Regimes of Covariate Shift (2021), arXiv (Cornell University)
  • Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient (2022), arXiv (Cornell University)
  • CMAX++: Leveraging Experience in Planning and Execution using Inaccurate Models (2021), Proceedings of the AAAI Conference on Artificial Intelligence
  • Causal Imitation Learning under Temporally Correlated Noise (2022), arXiv (Cornell University)
  • Sequence Model Imitation Learning with Unobserved Contexts (2022), arXiv (Cornell University)

Frequent collaborators in Bagnell's research include Sanjiban Choudhury, Gokul Swamy, Zhiwei Steven Wu, Anirudh Vemula, and Yuda Song. These partnerships have resulted in multiple joint works, reflecting engagement with a range of topics primarily within reinforcement learning and robotics.

Best Publications

  • Reinforcement learning in robotics: A survey

    Jens Kober;J. Andrew Bagnell;Jan Peters

  • Maximum entropy inverse reinforcement learning

    Brian D. Ziebart;Andrew Maas;J. Andrew Bagnell;Anind K. Dey

  • A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

    Stéphane Ross;Geoffrey J. Gordon;J. Andrew Bagnell

  • CHOMP: Gradient optimization techniques for efficient motion planning

    Nathan Ratliff;Matt Zucker;J. Andrew Bagnell;Siddhartha Srinivasa

  • Encyclopedia of Machine Learning and Data Mining

    Unknown

  • Maximum margin planning

    Nathan D. Ratliff;J. Andrew Bagnell;Martin A. Zinkevich

  • CHOMP: Covariant Hamiltonian optimization for motion planning

    Matt Zucker;Nathan Ratliff;Anca D. Dragan;Mihail Pivtoraiko

  • An Algorithmic Perspective on Imitation Learning

    Takayuki Osa;Joni Pajarinen;Gerhard Neumann;J. Andrew Bagnell

  • Planning-based prediction for pedestrians

    Brian D. Ziebart;Nathan Ratliff;Garratt Gallagher;Christoph Mertz

  • Learning monocular reactive UAV control in cluttered natural environments

    Stephane Ross;Narek Melik-Barkhudarov;Kumar Shaurya Shankar;Andreas Wendel

  • Modeling purposeful adaptive behavior with the principle of maximum causal entropy

    J. Andrew Bagnell;Brian D. Ziebart

  • Autonomous helicopter control using reinforcement learning policy search methods

    J.A. Bagnell;J.G. Schneider

  • Contextual classification with functional Max-Margin Markov Networks

    Daniel Munoz;J Andrew Bagnell;Nicolas Vandapel;Martial Hebert

  • Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior

    Brian D. Ziebart;Andrew L. Maas;Anind K. Dey;J. Andrew Bagnell

  • Pose Machines: Articulated Pose Estimation via Inference Machines

    Varun Ramakrishna;Daniel Munoz;Martial Hebert;James Andrew Bagnell

  • Learning to search: Functional gradient techniques for imitation learning

    Nathan D. Ratliff;David Silver;J. Andrew Bagnell

  • Stacked hierarchical labeling

    Daniel Munoz;J. Andrew Bagnell;Martial Hebert

  • Modeling Interaction via the Principle of Maximum Causal Entropy

    Brian D. Ziebart;J. A. Bagnell;Anind K. Dey

  • Improving multi-step prediction of learned time series models

    Arun Venkatraman;Martial Hebert;J. Andrew Bagnell

  • Online) Subgradient Methods for Structured Prediction

    Nathan D. Ratliff;J. Andrew Bagnell;Martin A. Zinkevich

  • Reinforcement and Imitation Learning via Interactive No-Regret Learning

    Stéphane Ross;J. Andrew Bagnell

Frequent Co-Authors

Martial Hebert
Martial Hebert Carnegie Mellon University
Wen Sun
Wen Sun Cornell University
Anthony Stentz
Anthony Stentz Carnegie Mellon University
Siddhartha S. Srinivasa
Siddhartha S. Srinivasa University of Washington
Byron Boots
Byron Boots University of Washington
Anind K. Dey
Anind K. Dey University of Washington
Geoffrey J. Gordon
Geoffrey J. Gordon Carnegie Mellon University
Nancy S. Pollard
Nancy S. Pollard Carnegie Mellon University
David Silver
David Silver DeepMind (United Kingdom)
Andrew B. Schwartz
Andrew B. Schwartz University of Pittsburgh

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