World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
8474
World Ranking
8729
National Ranking
3736

Dorsa Sadigh 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 Dorsa Sadigh 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: 236 publications — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Dorsa Sadigh 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 Dorsa Sadigh 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: 41 D-Index — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Overview

Dorsa Sadigh is a researcher affiliated with Stanford University in the United States, specializing in fields spanning Computer Science and Engineering. Their work primarily focuses on subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Automotive Engineering, and Social Psychology.

The researcher's publications cover a range of topics including Reinforcement Learning in Robotics, Robot Manipulation and Learning, Multimodal Machine Learning Applications, Topic Modeling, Human Pose and Action Recognition, Traffic Control and Management, and Natural Language Processing Techniques.

Frequent publication venues for their work include:

  • arXiv (Cornell University)
  • IEEE Robotics and Automation Letters
  • 2022 International Conference on Robotics and Automation (ICRA)
  • The International Journal of Robotics Research
  • Autonomous Robots

Among recent papers associated with this researcher are:

  • "On the Opportunities and Risks of Foundation Models" (2021), arXiv (Cornell University)
  • "Toward verified artificial intelligence" (2022), Communications of the ACM
  • "Open X-Embodiment: Robotic Learning Datasets and RT-X Models" (2023), arXiv (Cornell University)
  • "Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback" (2023), arXiv (Cornell University)
  • "Social Coordination and Altruism in Autonomous Driving" (2022), IEEE Transactions on Intelligent Transportation Systems

Collaborations have been frequent with several co-authors, including:

  • Erdem Bıyık
  • Chelsea Finn
  • Jeannette Bohg
  • Suneel Belkhale
  • Siddharth Karamcheti

Best Publications

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • Planning for Autonomous Cars that Leverage Effects on Human Actions

    Dorsa Sadigh;Shankar Sastry;Sanjit A. Seshia;Anca D. Dragan

  • Reactive synthesis from signal temporal logic specifications

    Vasumathi Raman;Alexandre Donzé;Dorsa Sadigh;Richard M. Murray

  • Active preference-based learning of reward functions

    Dorsa Sadigh;Anca D. Dragan;Shankar Sastry;Sanjit A. Seshia

  • Information gathering actions over human internal state

    Dorsa Sadigh;S. Shankar Sastry;Sanjit A. Seshia;Anca Dragan

  • Hierarchical Game-Theoretic Planning for Autonomous Vehicles

    Jaime F. Fisac;Eli Bronstein;Elis Stefansson;Dorsa Sadigh

  • Planning for cars that coordinate with people: leveraging effects on human actions for planning and active information gathering over human internal state

    Dorsa Sadigh;Nick Landolfi;Shankar S. Sastry;Sanjit A. Seshia

  • A learning based approach to control synthesis of Markov decision processes for linear temporal logic specifications

    Dorsa Sadigh;Eric S. Kim;Samuel Coogan;S. Shankar Sastry

  • Toward verified artificial intelligence

    Unknown

  • Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

    Unknown

  • Safe Control under Uncertainty with Probabilistic Signal Temporal Logic.

    Dorsa Sadigh;Ashish Kapoor

  • OpenVLA: An Open-Source Vision-Language-Action Model

    Unknown

  • Towards Verified Artificial Intelligence

    Sanjit A. Seshia;Dorsa Sadigh

  • Multi-Agent Generative Adversarial Imitation Learning

    Jiaming Song;Hongyu Ren;Dorsa Sadigh;Stefano Ermon

  • Synthesis for Human-in-the-Loop Control Systems

    Wenchao Li;Dorsa Sadigh;S. Shankar Sastry;Sanjit A. Seshia

  • Open X-Embodiment: Robotic Learning Datasets and RT-X Models

    Unknown

  • Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

    Unknown

  • SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities

    Unknown

  • Automating exercise generation: a step towards meeting the MOOC challenge for embedded systems

    Dorsa Sadigh;Sanjit A. Seshia;Mona Gupta

  • Data-Driven Probabilistic Modeling and Verification of Human Driver Behavior

    Dorsa Sadigh;Katherine Rose Driggs-Campbell;Alberto Puggelli;Wenchao Li

  • Learning Reward Functions by Integrating Human Demonstrations and Preferences

    Malayandi Palan;Gleb Shevchuk;Nicholas Charles Landolfi;Dorsa Sadigh

  • Efficient and Trustworthy Social Navigation via Explicit and Implicit Robot–Human Communication

    Yuhang Che;Allison M. Okamura;Dorsa Sadigh

  • Multi-Agent Generative Adversarial Imitation Learning

    Jiaming Song;Hongyu Ren;Dorsa Sadigh;Stefano Ermon

  • Dynamic multi-robot task allocation under uncertainty and temporal constraints

    Shushman Choudhury;Jayesh K. Gupta;Mykel J. Kochenderfer;Dorsa Sadigh

  • Active Preference-Based Gaussian Process Regression for Reward Learning

    Erdem Biyik;Nicolas Huynh;Mykel J. Kochenderfer;Dorsa Sadigh

  • When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans

    Minae Kwon;Erdem Biyik;Aditi Talati;Karan Bhasin

  • Learning Reward Functions by Integrating Human Demonstrations and Preferences

    Malayandi Palan;Nicholas C. Landolfi;Gleb Shevchuk;Dorsa Sadigh

Frequent Co-Authors

Sanjit A. Seshia
Sanjit A. Seshia University of California, Berkeley
Shankar Sastry
Shankar Sastry University of California, Berkeley
Jeannette Bohg
Jeannette Bohg Stanford University
Mykel J. Kochenderfer
Mykel J. Kochenderfer Stanford University
Stefano Ermon
Stefano Ermon Stanford University
Anca D. Dragan
Anca D. Dragan University of California, Berkeley
Percy Liang
Percy Liang Stanford University
Noah D. Goodman
Noah D. Goodman Stanford University
Chelsea Finn
Chelsea Finn Stanford University
Alberto Sangiovanni-Vincentelli
Alberto Sangiovanni-Vincentelli University of California, Berkeley

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