World's Best Scientists 2026 revealed!
Sertac Karaman

Sertac Karaman

D-Index & Metrics

Computer Science

D-Index
60
Citations
20055
World Ranking
3188
National Ranking
1546

Sertac Karaman 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 Sertac Karaman 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.

Sertac Karaman 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 Sertac Karaman 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: 60 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Overview

Sertac Karaman is affiliated with MIT in the United States and has published extensively in the fields of Computer Science and Engineering. Their research spans major subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering, Automotive Engineering, and Control and Systems Engineering.

The scientist's work focuses on several core topics within robotics and autonomous systems. These include Autonomous Vehicle Technology and Safety, Reinforcement Learning in Robotics, Robotic Path Planning Algorithms, Robotics and Sensor-Based Localization, Advanced Vision and Imaging, Guidance and Control Systems, and Advanced Neural Network Applications.

Some of their recent notable papers include:

  • Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation, 2020, IEEE Robotics and Automation Letters
  • VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles, 2022, 2022 International Conference on Robotics and Automation (ICRA)
  • Improving Age of Information in Wireless Networks With Perfect Channel State Information, 2020, IEEE/ACM Transactions on Networking
  • Aerobatic Trajectory Generation for a VTOL Fixed-Wing Aircraft Using Differential Flatness, 2023, IEEE Transactions on Robotics
  • Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features, 2021, IEEE Robotics and Automation Letters

Frequent collaborators include Daniela Rus, Igor Gilitschenski, Ezra Tal, Vivienne Sze, and Wilko Schwarting.

Their publications are commonly found in venues such as arXiv (Cornell University), IEEE Robotics and Automation Letters, The International Journal of Robotics Research, 2022 International Conference on Robotics and Automation (ICRA), and IEEE Transactions on Robotics.

Best Publications

  • Sampling-based algorithms for optimal motion planning

    Sertac Karaman;Emilio Frazzoli

  • Real-Time Motion Planning With Applications to Autonomous Urban Driving

    Y. Kuwata;S. Karaman;J. Teo;E. Frazzoli

  • Anytime Motion Planning using the RRT

    Sertac Karaman;Matthew R. Walter;Alejandro Perez;Emilio Frazzoli

  • Incremental Sampling-based Algorithms for Optimal Motion Planning.

    Sertac Karaman;Emilio Frazzoli

  • Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

    Fangchang Mal;Sertac Karaman

  • A perception-driven autonomous urban vehicle

    John Leonard;Jonathan How;Seth Teller;Mitch Berger

  • Optimal kinodynamic motion planning using incremental sampling-based methods

    Sertac Karaman;Emilio Frazzoli

  • Self-Supervised Sparse-to-Dense: Self-Supervised Depth Completion from LiDAR and Monocular Camera

    Fangchang Ma;Guilherme Venturelli Cavalheiro;Sertac Karaman

  • Social behavior for autonomous vehicles

    Wilko Schwarting;Alyssa Pierson;Javier Alonso-Mora;Sertac Karaman

  • A Perception Driven Autonomous Urban Robot

    John Leonard;Jonathan How;Seth Teller;Mitch Berger

  • FastDepth: Fast Monocular Depth Estimation on Embedded Systems

    Diana Wofk;Fangchang Ma;Tien-Ju Yang;Sertac Karaman

  • Accurate Tracking of Aggressive Quadrotor Trajectories Using Incremental Nonlinear Dynamic Inversion and Differential Flatness

    Ezra Tal;Sertac Karaman

  • Optimizing Information Freshness in Wireless Networks Under General Interference Constraints

    Rajat Talak;Sertac Karaman;Eytan Modiano

  • Duckietown: An open, inexpensive and flexible platform for autonomy education and research

    Liam Paull;Jacopo Tani;Heejin Ahn;Javier Alonso-Mora

  • Minimizing age-of-information in multi-hop wireless networks

    Rajat Talak;Sertac Karaman;Eytan Modiano

  • Sampling-based motion planning with deterministic μ-calculus specifications

    Sertac Karaman;Emilio Frazzoli

  • Optimal control of Mixed Logical Dynamical systems with Linear Temporal Logic specifications

    S. Karaman;R.G. Sanfelice;E. Frazzoli

  • Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation

    Alexander Amini;Igor Gilitschenski;Jacob Phillips;Julia Moseyko

  • Motion Planning in Complex Environments using Closed-loop Prediction

    Unknown

  • FlightGoggles: Photorealistic Sensor Simulation for Perception-driven Robotics using Photogrammetry and Virtual Reality

    Winter Guerra;Ezra Tal;Varun Murali;Gilhyun Ryou

  • Optimal motion planning with the half-car dynamical model for autonomous high-speed driving

    Jeong Hwan Jeon;Raghvendra V. Cowlagi;Steven C. Peters;Sertac Karaman

  • Sampling-based optimal motion planning for non-holonomic dynamical systems

    Sertac Karaman;Emilio Frazzoli

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