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D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Computer Science 40 9367 9097 371 355 201 6079

Animesh Garg publications per year

The chart shows the history of publications by Animesh Garg between 2011 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Animesh Garg published across 15 years, from 2011 to 2025, averaging 15.5 papers a year. Output peaked at 57 publications in 2021. 30 of the 233 publications appeared in the last two years.

No. of publications
10 20 30 40 50
Bar chart. Horizontal axis: year, 2011 to 2025. Vertical axis: number of publications, 0 to 57. Peak 57 publications in 2021. 2011: 2 publications 2012: 1 publication 2013: 2 publications 2014: 2 publications 2015: 4 publications 2016: 8 publications 2017: 12 publications 2018: 9 publications 2019: 22 publications 2020: 36 publications 2021: 57 publications 2022: 29 publications 2023: 19 publications 2024: 20 publications 2025: 10 publications
2011 2025

233 publications in total across all disciplines

View publications per year as a table
Animesh Garg: publications per year, 2011 to 2025
Year Publications
2011 2
2012 1
2013 2
2014 2
2015 4
2016 8
2017 12
2018 9
2019 22
2020 36
2021 57
2022 29
2023 19
2024 20
2025 10
Total 233
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Animesh Garg publications per year - data summary

  • Animesh Garg, a Computer Science scholar from University of Toronto, has 233 publications recorded across 15 years, from 2011 to 2025.
  • The oldest publication on record dates to 2011 and the most recent to 2025.
  • The most productive year is 2021, with 57 publications.
  • The least productive year with any output is 2012, with 1 publication.
  • The rate of publication averages 15.5 papers per year over the whole span, or 15.5 per year counting only the 15 years with at least one publication.
  • The last 5 years on the chart (2021-2025) hold 135 publications, 58% of the career total.
  • Split into equal eras - 2011-2015: 11 publications (2.2 per year); 2016-2020: 87 publications (17.4 per year); 2021-2025: 135 publications (27.0 per year).
  • Comparing the opening and closing eras, the overall trend of publication is rising.

Animesh Garg 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 Animesh Garg sits on this spectrum.

No. of scientists
200 400 600
Bar chart with 97 bars. Horizontal axis: publications, 32–41 to 991+. Vertical axis: number of scientists, 0 to 609. Most scientists, 609, have 142–151 publications. The last bar groups every scientist with 991 publications or more. The highlighted bar, 192–201 publications, is where this scientist sits. 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
32–41 publications 991+

This scientist: 201 publications — 47th percentile

47% of scientists in this discipline score the same or lower.

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

View publications distribution as a table
Number of Computer Science scientists by publication count, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
Publications Scientists This scientist
32–41 7
42–51 22
52–61 82
62–71 134
72–81 249
82–91 324
92–101 421
102–111 420
112–121 497
122–131 544
132–141 555
142–151 609
152–161 559
162–171 534
172–181 556
182–191 583
192–201 519 201
202–211 508
212–221 490
222–231 437
232–241 423
242–251 408
252–261 377
262–271 301
272–281 335
282–291 320
292–301 293
302–311 250
312–321 238
322–331 206
332–341 209
342–351 208
352–361 162
362–371 176
372–381 127
382–391 158
392–401 128
402–411 104
412–421 94
422–431 99
432–441 83
442–451 108
452–461 73
462–471 77
472–481 69
482–491 84
492–501 62
502–511 54
512–521 57
522–531 51
532–541 51
542–551 32
552–561 38
562–571 28
572–581 43
582–591 33
592–601 41
602–611 32
612–621 28
622–631 25
632–641 27
642–651 17
652–661 20
662–671 17
672–681 15
682–691 14
692–701 21
702–711 13
712–721 12
722–731 19
732–741 14
742–751 12
752–761 10
762–771 10
772–781 11
782–791 10
792–801 11
802–811 8
812–821 8
822–831 7
832–841 11
842–851 10
852–861 5
862–871 9
872–881 4
882–891 6
892–901 3
902–911 6
912–921 3
922–931 2
932–941 2
942–951 2
952–961 3
962–971 3
972–981 3
982–990 5
991+ 100
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Animesh Garg publication distribution in Computer Science in 2026 - data summary

  • The chart plots the publication count of all 14,188 Computer Science scientists ranked by Research.com in 2026, grouped into 97 ranges running from 32–41 to 991+ publications.
  • Animesh Garg, a Computer Science scholar from University of Toronto, records 201 publications - the 47th percentile of the discipline.
  • 47% of ranked Computer Science scientists score the same or lower than Animesh Garg, and about 53% score higher.
  • The median of the discipline falls in the 202–211 publications range, and Animesh Garg ranks below the median.
  • The most crowded range is 142–151 publications, holding 609 scientists (4% of the field).
  • 70% of the field sits in the lowest quarter of the value range (up to 272–281 publications), so the distribution is heavily right-skewed and high scores are rare.
  • The final bar has no upper bound: it groups every scientist with 991 publications or more, 100 scientists in all (<1% of the field).

Animesh Garg 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 Animesh Garg sits on this spectrum.

No. of scientists
200 400 600 800
Bar chart with 52 bars. Horizontal axis: D-Index, 30–31 to 131+. Vertical axis: number of scientists, 0 to 990. Most scientists, 990, have 36–37 D-Index. The last bar groups every scientist with 131 D-Index or more. The highlighted bar, 40–41 D-Index, is where this scientist sits. 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
30–31 D-Index 131+

This scientist: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

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

View D-Index distribution as a table
Number of Computer Science scientists by D-index, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
D-Index Scientists This scientist
30–31 879
32–33 983
34–35 918
36–37 990
38–39 968
40–41 907 40
42–43 821
44–45 763
46–47 689
48–49 543
50–51 543
52–53 518
54–55 500
56–57 458
58–59 400
60–61 337
62–63 308
64–65 292
66–67 249
68–69 213
70–71 192
72–73 189
74–75 165
76–77 139
78–79 119
80–81 121
82–83 113
84–85 88
86–87 87
88–89 75
90–91 69
92–93 57
94–95 46
96–97 38
98–99 34
100–101 36
102–103 27
104–105 37
106–107 18
108–109 31
110–111 19
112–113 16
114–115 12
116–117 20
118–119 15
120–121 5
122–123 20
124–125 8
126–127 5
128–129 7
130 3
131+ 98
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Animesh Garg D-index placement in Computer Science in 2026 - data summary

  • The chart plots the discipline H-index (D-index) of all 14,188 Computer Science scientists ranked by Research.com in 2026, grouped into 52 ranges running from 30–31 to 131+ D-Index.
  • Animesh Garg, a Computer Science scholar from University of Toronto, records 40 D-Index - the 37th percentile of the discipline.
  • 37% of ranked Computer Science scientists score the same or lower than Animesh Garg, and about 63% score higher.
  • The median of the discipline falls in the 44–45 D-Index range, and Animesh Garg ranks below the median.
  • The most crowded range is 36–37 D-Index, holding 990 scientists (7% of the field).
  • 71% of the field sits in the lowest quarter of the value range (up to 54–55 D-Index), so the distribution is heavily right-skewed and high scores are rare.
  • The final bar has no upper bound: it groups every scientist with 131 D-Index or more, 98 scientists in all (<1% of the field).

Overview

Animesh Garg is affiliated with the University of Toronto in Canada. Their research spans multiple areas within computer science and engineering, with a particular focus on artificial intelligence and robotics.

Their main fields of study include:

  • Computer Science
  • Engineering

Within these fields, they specialize in several subfields, notably:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Biomedical Engineering
  • Mechanical Engineering

The major topics their work addresses encompass:

  • Reinforcement Learning in Robotics
  • Robot Manipulation and Learning
  • Human Pose and Action Recognition
  • Multimodal Machine Learning Applications
  • Adversarial Robustness in Machine Learning
  • Modular Robots and Swarm Intelligence
  • Robotic Locomotion and Control

They have contributed extensively to academic publications, with frequent appearances in the following venues:

  • arXiv (Cornell University)
  • Autonomous Robots
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE Robotics and Automation Letters
  • 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Recent significant papers authored or co-authored include:

  • "X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments," 2023, IEEE Robotics and Automation Letters
  • "Large language models for chemistry robotics," 2023, Autonomous Robots
  • "Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation," 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • "ORGANA: A robotic assistant for automated chemistry experimentation and characterization," 2024, Matter

The scientist has collaborated frequently with several individuals, including:

  • Florian Shkurti
  • Dieter Fox
  • Samarth Sinha
  • Alán Aspuru-Guzik
  • Homanga Bharadhwaj

Best Publications

  • ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

    Unknown

  • Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

    Michelle A. Lee;Yuke Zhu;Krishnan Srinivasan;Parth Shah

  • Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning:

    Sanjay Krishnan;Animesh Garg;Sachin Patil;Colin Lea

  • Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms

    Adithyavairavan Murali;Siddarth Sen;Ben Kehoe;Animesh Garg

  • Learning task-oriented grasping for tool manipulation from simulated self-supervision:

    Kuan Fang;Yuke Zhu;Animesh Garg;Animesh Garg;Andrey Kurenkov

  • Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization

    Siddarth Sen;Animesh Garg;David V. Gealy;Stephen McKinley

  • Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

    Michelle A. Lee;Yuke Zhu;Peter Zachares;Matthew Tan

  • Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

    Danfei Xu;Suraj Nair;Yuke Zhu;Julian Gao

  • X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval

    Unknown

  • Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning

    Brijen Thananjeyan;Animesh Garg;Sanjay Krishnan;Carolyn Chen

  • Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks

    Roberto Martin-Martin;Michelle A. Lee;Rachel Gardner;Silvio Savarese

  • Neural Task Graphs: Generalizing to Unseen Tasks From a Single Video Demonstration

    De-An Huang;Suraj Nair;Danfei Xu;Yuke Zhu

  • Adversarially Robust Policy Learning: Active construction of physically-plausible perturbations

    Ajay Mandlekar;Yuke Zhu;Animesh Garg;Li Fei-Fei

  • DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image

    Andrey Kurenkov;Jingwei Ji;Animesh Garg;Viraj Mehta

  • ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation

    Ajay Mandlekar;Yuke Zhu;Animesh Garg;Jonathan Booher

  • Weakly Supervised 3D Reconstruction with Adversarial Constraint

    JunYoung Gwak;Christopher B. Choy;Manmohan Chandraker;Animesh Garg

  • Transition State Clustering: Unsupervised Surgical Trajectory Segmentation for Robot Learning.

    Sanjay Krishnan;Animesh Garg;Sachin Patil;Colin Lea

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

    Unknown

  • Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos

    De-An Huang;Shyamal Buch;Lucio Dery;Animesh Garg

  • A single-use haptic palpation probe for locating subcutaneous blood vessels in robot-assisted minimally invasive surgery

    Stephen McKinley;Animesh Garg;Siddarth Sen;Rishi Kapadia

  • TSC-DL: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with Deep Learning

    Adithyavairavan Murali;Animesh Garg;Sanjay Krishnan;Florian T. Pokorny

  • Tumor localization using automated palpation with Gaussian Process Adaptive Sampling

    Animesh Garg;Siddarth Sen;Rishi Kapadia;Yiming Jen

  • Large language models for chemistry robotics

    Unknown

  • IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data

    Ajay Mandlekar;Fabio Ramos;Byron Boots;Silvio Savarese

  • Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter

    Michael Danielczuk;Andrey Kurenkov;Ashwin Balakrishna;Matthew Matl

  • DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting

    Eric Heiden;Miles Macklin;Yashraj S Narang;Dieter Fox

  • Transition state clustering

    Sanjay Krishnan;Animesh Garg;Sachin Patil;Colin Lea

  • Causal Discovery in Physical Systems from Videos

    Yunzhu Li;Antonio Torralba;Animashree Anandkumar;Dieter Fox

Frequent Co-Authors

Silvio Savarese
Silvio Savarese Stanford University
Ken Goldberg
Ken Goldberg University of California, Berkeley
Yuke Zhu
Yuke Zhu The University of Texas at Austin
Li Fei-Fei
Li Fei-Fei Stanford University
Anima Anandkumar
Anima Anandkumar Nvidia (United Kingdom)
Sachin Patil
Sachin Patil University of California, Berkeley
Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Jeannette Bohg
Jeannette Bohg Stanford University
Hugo Larochelle
Hugo Larochelle Google (United States)
Byron Boots
Byron Boots University of Washington

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