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2025

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Rising Stars

D-Index
37
Citations
9008
World Ranking
746
National Ranking
121

Vikash Kumar 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 Vikash Kumar 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+

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

Vikash Kumar 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 Vikash Kumar 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+

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

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Vikash Kumar is affiliated with the University of Washington in the United States and has an active research profile spanning the fields of engineering and computer science. Their work primarily intersects subfields such as mechanical engineering, electrical and electronic engineering, materials chemistry, information systems, and biomedical engineering.

Their research topics cover a variety of specialized areas including electrodeposition and electroless coatings, muscle activation and electromyography studies, metallurgical processes and thermodynamics, electrocatalysts for energy conversion, semiconductor materials and interfaces, corrosion behavior and inhibition, and additive manufacturing materials and processes.

Recent scholarly contributions by Vikash Kumar include the following publications:

  • Tailoring electroless Ni-W-P polyalloy coatings: Unveiling the synergistic impact of cerium addition and annealing on surface functional properties, 2024, Surface and Coatings Technology
  • Optimization, Prediction, and Characterization of Electroless Ni-W-P-nanoTiO2 Composite Coatings on Pipeline Steel, 2023, Arabian Journal for Science and Engineering
  • MyoSim: Fast and physiologically realistic MuJoCo models for musculoskeletal and exoskeletal studies, 2022, 2022 International Conference on Robotics and Automation (ICRA)
  • Abrupt Casting Failures Due to Sub Entry Nozzle Clogging in Calcium Treated Aluminum Killed Steel, 2022, Journal of Failure Analysis and Prevention
  • Influence of bulk post processing techniques on anisotropy of microstructural and tribological properties of L-DED produced Ti64 alloy, 2025, Wear

Vikash Kumar frequently collaborates with several researchers, including Buddhadeb Oraon, Biplab Baran Mandal, Somnath Kumar, Sumanta Mukherjee, and S. Suresh. These collaborations have contributed to advancing research in their areas of expertise.

Their work has appeared in publication venues such as Procedia Computer Science, arXiv (Cornell University), the 2022 International Conference on Robotics and Automation (ICRA), Journal of Failure Analysis and Prevention, and Arabian Journal for Science and Engineering.

Best Publications

  • Soft Actor-Critic Algorithms and Applications

    Tuomas Haarnoja;Aurick Zhou;Kristian Hartikainen;George Tucker

  • Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

    Aravind Rajeswaran;Vikash Kumar;Abhishek Gupta;Giulia Vezzani

  • Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

    Matthias Plappert;Marcin Andrychowicz;Alex Ray;Bob McGrew

  • Optimal control with learned local models: Application to dexterous manipulation

    Vikash Kumar;Emanuel Todorov;Sergey Levine

  • Deep Dynamics Models for Learning Dexterous Manipulation.

    Anusha Nagabandi;Kurt Konolige;Sergey Levine;Vikash Kumar

  • An integrated system for real-time model predictive control of humanoid robots

    Tom Erez;Kendall Lowrey;Yuval Tassa;Vikash Kumar

  • Learning Latent Plans from Play

    Corey Harrison Lynch;Mohi Khansari;Ted Xiao;Vikash Kumar

  • Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost

    Henry Zhu;Abhishek Gupta;Aravind Rajeswaran;Sergey Levine

  • Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

    Aravind Rajeswaran;Vikash Kumar;Abhishek Gupta;John Schulman

  • Domain Randomization and Generative Models for Robotic Grasping

    Josh Tobin;Lukas Biewald;Rocky Duan;Marcin Andrychowicz

  • Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning

    Abhishek Gupta;Vikash Kumar;Corey Lynch;Sergey Levine

  • Dynamics-Aware Unsupervised Discovery of Skills

    Archit Sharma;Shixiang Gu;Sergey Levine;Vikash Kumar

  • Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

    Cathy Wu;Aravind Rajeswaran;Yan Duan;Vikash Kumar

  • MuJoCo HAPTIX: A virtual reality system for hand manipulation

    Vikash Kumar;Emanuel Todorov

  • The Ingredients of Real World Robotic Reinforcement Learning

    Henry Zhu;Justin Yu;Abhishek Gupta;Dhruv Shah

  • Divide-and-Conquer Reinforcement Learning

    Dibya Ghosh;Avi Singh;Aravind Rajeswaran;Vikash Kumar

  • Learning Dexterous Manipulation Policies from Experience and Imitation.

    Vikash Kumar;Abhishek Gupta;Emanuel Todorov;Sergey Levine

  • Deep Dynamics Models for Learning Dexterous Manipulation

    Anusha Nagabandi;Kurt Konoglie;Sergey Levine;Vikash Kumar

  • ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

    Michael Ahn;Henry Zhu;Kristian Hartikainen;Hugo Ponte

  • Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention

    Abhishek Gupta;Justin Yu;Tony Z. Zhao;Vikash Kumar

Frequent Co-Authors

Sergey Levine
Sergey Levine University of California, Berkeley
Emanuel Todorov
Emanuel Todorov University of Washington
Shixiang Gu
Shixiang Gu Google (United States)
Igor Mordatch
Igor Mordatch Google (United States)
Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Yuval Tassa
Yuval Tassa Google (United States)
Alexandre M. Bayen
Alexandre M. Bayen University of California, Berkeley
Danica Kragic
Danica Kragic Royal Institute of Technology
Chelsea Finn
Chelsea Finn Stanford University

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